GIS for Mapping Vegetation

2.01

GIS for Mapping Vegetation

Georg Bareth and Guido Waldhoff, University of Cologne, Cologne, Germany © 2018 Elsevier Inc. All rights reserved.

2.01.1 2.01.2 2.01.3 2.01.4 2.01.4.1 2.01.4.2 2.01.4.3 2.01.4.4 2.01.4.5 2.01.4.6 2.01.4.7 2.01.5 2.01.6 References Further Reading

2.01.1

Introduction Plant Communities and Vegetation Inventories Vegetation Data in Official Information Systems Multi-Data Approach for Land Use and Land Cover Mapping Introduction to Land Use and Land Cover Mapping Methods for Remote Sensing-Based Land Use/Land Cover Mapping Integration of Remote Sensing and GIS for Land Use/Land Cover Mapping Data and GIS Methods for Enhanced Land Use/Land Cover Mapping Multi-Data Approach for Enhanced Land Use/Land Cover Mapping Examples for Land Use/Land Cover Map Enhancement With the MDA Summary and Conclusion Analysis of High-Resolution DSMs in Forestry and Agriculture Conclusion and Outlook

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Introduction

GIS-based mapping of vegetation is a very common and broadly established application which is interconnected with remote sensing of vegetation (Jones and Vaughan, 2010), with digital surveying and mapping of vegetation (Küchler and Zonneveld, 1988), and with traditional approaches for mapping of vegetation (Mueller-Dombois, 1984). The latter three are not in the focus of this chapter but will be included where strong dependencies occur. Traditionally, mapping of vegetation is a major objective of vegetation science, geobotany, biogeography, and landscape ecology using cartographic techniques (Küchler and Zonneveld, 1988; Pedrotti, 2013). Pedrotti (2013) even states that “Geobotanical cartography is a field of thematic cartography.” which includesdas always in GIS applicationsdthe expertise of cartography. Consequently, GIS-based mapping of vegetation is a strongly interdisciplinary topic. As in almost all cases of GIS applications, and especially true in the context of vegetation, the overarching use of vegetation maps serves spatial decision making (Küchler, 1988a). The planning of protected areas, future land use, change of land use, etc. depends on related and for the spatial decision-making process adequate spatial data. Furthermore, analysis of the change in vegetation inventories which are mapped for certain time windows gives a clear signal of the magnitude of changes in or on the environment. Finally, in the context of agriculture, forestry, and resource management, the monitoring of the temporal development of vegetation, the phenology, is of key interest for management decisions like fertilization, weeding, pest control, and many more (Brown, 2005; Mulla, 2013; Naesset, 1997). In general, the mapping scale also determines the mapping technologies. In the literature, several approaches to classify scale categories or regional extent for surveying techniques are available (e.g., Alexander and Millington, 2000). The authors categorize spaceborne remote sensing (> 5 m resolution) for potential global areal extent while aerial photography with a much higher spatial resolution (1–10 m) is limited to several square kilometers. Such categories are often found in textbooks but this approach will not be followed in this chapter in the context of GIS applications. On the contrary, we strongly believe that nowadays proximal, airborne, and satellite-based remote sensing in combination with ground surveying techniques like field sampling, GPS, and laser scanning are already supporting vegetation mapping in all scales. Increasing data availability, computing resources, and software capabilities nowadays enable, e.g., global land cover mapping in 30 m resolution (Hansen et al., 2013) which was not covered as a complete data product in the categorization of scales in earlier publications. This trend of increasing spatial resolution of global data products on vegetation will continue, due to the fact that satellite imagery from ESA’s Sentinel-2 is already producing global coverage with a spatial resolution of 10 m and is accessible openly (https://sentinel.esa.int). So, nowadays it is more a combination of scale overlapping methods which results in vegetation maps. For example, mapping and monitoring the spatial variability of plant growth can be supported in subcentimeter resolution with unmanned aerial systems (UAS) or with low-altitude flying manned vehicles like gyrocopters, or in submeter or meter resolution from optical or microwave satellite-based remote sensing (Bendig et al., 2015; Hütt et al., 2016; Koppe et al., 2013) for study areas ranging from several square meters to several thousand hectares. Additionally, these airborne sensors can be operated with low cost, are available globally, and only are limited by national aviation regulations. The usage of such technologies for vegetation mapping will even grow with the increasing availability of sensor data (e.g., ESA’s Copernicus program; http://www.copernicus.eu) and with the availability of easy-to-use imaging UASs (Bareth et al., 2015). In Fig. 1, a UAS data example is shown from Turner et al. (2014), who carried out multisensor UAS campaigns for Antarctic Moss Beds mapping. It becomes obvious that such sensor-carrying systems can support the mapping of vegetation in almost any environment.

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Fig. 1 Robinson Ridge study site: (A) visible mosaic of entire area, (B) RGB image subset, (C) multispectral image subset, (D) thermal infrared image subset, and (E) typical multispectral reflectance function of a healthy Antarctic moss turf. Turner D, Lucieer A, Malenovsky Z, King DH, and Robinson SA (2014): Spatial co-registration of ultra-high resolution visible, multispectral and thermal images acquired with a Micro-UAV over antarctic moss beds. Remote Sensing 6 (5), 4003–4024.

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Having the developments of traditional vegetation mapping and of proximal and remote sensing of the last decade in mind, this chapter of GIS applications of mapping of vegetation will focus on four major topics: l

vegetation inventories; vegetation data in available spatial information systems; l GIS-supported land use and land cover (LULC) mapping; l GIS-based analysis of super high-resolution digital surface models (DSMs). l

For the mapping of plant species and communities, GISs serve as a spatial database to manage, administrate, and visualize the surveyed data on vegetation. GIS-based analyses are only used for point to polygon regionalization or for (geostatistical) interpolation between sampling points. The set-up of spatial topographic information systems like the Authorative TopographicCartographic Information System (ATKIS: Amtliches Topographisch-Kartographisches Informationssystem) in Germany are also an important source for spatial vegetation data (www.atkis.de). Such spatial information systems are available in many countries at a scale of at least 1:25.000. More precise and spatial vegetation data is mapped, e.g., in Germany in so-called biotope maps which deserve protection in a scale < 1:25.000 (http://www4.lubw.baden-wuerttemberg.de/servlet/is/19264/). Special vegetation data products are produced for land cover and land use data sets. Examples are the European CORINE Land Cover data (Coordination of Information on the Environment Land CoverdCLC; www.eea.europa.eu/publications/COR0-landcover) and the German DeCover activities (www.decover.info). Finally, the previously mentioned new high-resolution sensor technologies result in new data products with an astonishing spatial resolution in centimeter or subcentimeter resolution. Such data can be analyzed with GIS techniques in a new context deriving plant species or plant dynamics.

2.01.2

Plant Communities and Vegetation Inventories

The mapping of plant species and plant communities (phytocenose) has a long tradition and dates back to the 15th century (Küchler, 1988b). While these early activities of mapping vegetation were more or less unstandardized, the developments in topographical surveying and the generation of detailed topographic maps from the 18th century on resulted in a mapping of consistent vegetation classes (Küchler, 1988b). The first pure vegetation maps were produced in the middle of the 19th century and in the early 20th century, the field of geobotanical cartography emerged (Küchler, 1988b; Pedrotti, 2013). In addition to the vegetation surveys as a basis for vegetation maps, spatial data technologies emerged in the middle of the 20th century (GISdgeographical information systems; RSdremote sensing), which have supported mapping of vegetation since then (Alexander and Millington, 2000; Pedrotti, 2013). Scales are an important issue for mapping of vegetation and determine in general the level of detail that can be mapped (Küchler, 1988c). The consideration of scales and the corresponding mapping of detail is closely related to the subject of “levels of synthesis in geobotanical mapping” described by Pedrotti (2013). Pedrotti (2013) classified in total eight such mapping levels, which correspond with distinct map types: 1. The level of individual plants (species) enables the mapping of plant populations at a very high detail, even considering individual plants. The corresponding map type is the population map at a scale of larger than 1:2000 (Küchler, 1988c; Pedrotti, 2013). 2. Also in the map type of a population map is the level of populations (species). The focus in this level is on the distribution of species in a given small study or mapping area. The preferred map scale is larger than 1:5000 (Küchler, 1988c; Pedrotti, 2013). 3. The level of synusiae is for the mapping of synusia, which represents a “group of functionally similar plant species in a vegetation stand.” The map type is defined as a synusial map at a scale of larger than 1:5000 (Küchler, 1988c; Pedrotti, 2013). 4. The mapping of plant communities (phytocenose) is defined as the level of phytocoenosis. Plant associations are of major importance here and the map type is a phytosociological map at a preferred scale of larger than 1:25,000 (Küchler, 1988c; Pedrotti, 2013). 5. The level of ecotopes (teselas) represents the mapping of sigmetum series, which represents a combination of phytocenose in a landscape unit. The corresponding map type is a synphytosociological map at a scale of larger than 1:25,000 (Küchler, 1988c; Pedrotti, 2013). 6. The landscape scale is categorized in the level of catenas. The idea of this geo-synphytosociological map type is the mapping of “catenas of vegetation series” and the scale is larger than 1:100,000 (Küchler, 1988c; Pedrotti, 2013). 7. The regional or national scale is defined as the “level of lower phytogeographical units.” The recommended map scale for this “regional-phytogeographical” map type is larger than 1:250,000 (Küchler, 1988c; Pedrotti, 2013). 8. Finally, the level of higher phytogeographical units and biomes serves for continental or global scales. This also includes maps of vegetation zones. The map scale is larger than 1:5,000,000 (Küchler, 1988c; Pedrotti, 2013). Additional to these mentioned map types, Zonneveld (1988) differentiates between “real vegetation maps” and “potential natural vegetation maps.” Maps of plant biodiversity are also in the focus of some mapping activities (Pedrotti, 2013; Scott et al., 1993). Especially, GIS-based multiscale inventories for biodiversity were recognized as an important approach to identify “landscape patterns, vegetation, habitat structure, and species distribution” (Noss, 1990). In Germany, for example, selected rare biotopes are mapped in high detail at a scale of 1:25,000. The responsibility for this Biotopkartierung (biotope mapping) lies within each of the federal states. In North Rhine-Westphalia (NRW) for example, such biotopes, covering 18% of the state’s area (approximately 6135 km2), are mapped and the data is openly accessible (http://bk.naturschutzinformationen.nrw.de). In Fig. 2, a part of the

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Data Sources: - Biotop Map of NRW (http://bk.naturschutzinformationen.nrw.de/bk/de/downloads) - WMS: Digital topographic map of NRW (https://www.wms.nrw.de/geobasis/wms_nw_dtk)

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Fig. 2 Example of the Biotope Map of North Rhine-Westfalia, Germany: colored polygons represent surveyed and mapped biotopes. Source: Schutzwürdige Biotope in Nordrhein-Westfalen - http://bk.naturschutzinformationen.nrw.de.

biotope map of NRW between Cologne and Aachen is shown. The colored polygons represent the biotopes mapped and described in detail. The attribute descriptions include plant communities and plant species. The data is openly downloadable as a shape file or is accessible via a WebGIS (http://bk.naturschutzinformationen.nrw.de/bk/de/karten/bk). Finally, the mapping of invasive species is increasing in importance and comprehensive inventories and databases like the Delivering Alien Invasive Species Inventories for Europe project (DAISE; www.europe-aliens.org) (Lambdon et al., 2008) or USDA’s National Invasive Species Information Center (NISIC; https://www.invasivespeciesinfo.gov) have been set up. Other mapping and classification approaches are described by Mueller-Dombois (1984) and scale-dependently by Alexander and Millington (2000). Besides the approaches described of how to map vegetation as a function of scale, numerous guidelines and mapping procedures are available. The United States Geological Survey (USGS) together with the National Park Service (NPS), e.g., have provided a final draft for “Field Methods for Vegetation Mapping” (1994; https://www1.usgs.gov/vip/standards/fieldmethodsrpt.pdf). The guidelines consider GIS technologies and the usage of orthoimagery. The report is designed for a map scale of 1:24,000. The NPS provides up-to-date information on its vegetation mapping online and a 12-Step Guidance for NPS Vegetation Inventories (https://science. nature.nps.gov/im/inventory/veg/docs/Veg_Inv_12step_Guidance_v1.1.pdf) updated in 2013. Similar resources on vegetation mapping are, e.g., available from Australia. The Australian National Vegetation Information System (NVIS) is a comprehensive spatial data information system which provides numerous additional information in books, reports, and fact sheets (https:// www.environment.gov.au/land/native-vegetation/national-vegetation-information-system). Via the NVIS webpage numerous documents on classification, vegetation mapping, legend, etc. are available. Even the documents of Australia’s Native Vegetation Framework (2012) are downloadable. Similar activities are also found, e.g., in China. The vegetation map of China (1:1,000,000) is accompanied by much additional information on vegetation and plant communities (Guo, 2010). The usage of GIS in mapping plant communities and vegetation inventories is more or less reduced to spatial data storage, visualization, and map production (van der Zee and Huizing, 1988). Boundaries, plots, or stands can be captured by field surveys with technologies like tachymeter, compass, measuring tape, altimeter, laser distance meter, and GPS (Pedrotti, 2013). The data are stored as raster data or vector data (points, lines, or polygons with corresponding attributes) depending on the field mapping approach (Mueller-Dombois, 1984; Pedrotti, 2013; Zonneveld, 1988). Analysis is of minor importance and is limited to geostatistical interpolation and extrapolation methods, as well as to point-to-polygon regionalization concepts. Nowadays, GIS technologies support vegetation mapping in the field more actively. Portable computers (smartphones, PADs, notebooks) can be interfaced with a GPS receiver or have a GPS module included. Together with internet access via mobile data connection, available digital geodata via, e.g., Web Map Service (WMS), and adequate GIS software, it is possible to electronically use digital orthophotos (DOPs),

GIS for Mapping Vegetation

5

satellite imagery, soil or geological data, and topographic base maps as background and orientation information in the field and for direct digital vegetation surveys or mapping. The potential and beneficiary usage of remote sensing for vegetation mapping is well described by Wyatt (2000). While from the 1950s to the 1980s, the usage of aerial photographs dominated in mapping of vegetation, from the late 1980s until today, the importance of satellite-based remote sensing has grown exponentially (Houborg et al., 2015; Millington and Alexander, 2000). Three recent developments in remote sensing will have a fundamental impact on vegetation mapping: (i) Increase of spatial resolution: – satellite-based RS: up to submeter resolution – airborne-based RS: up to subdecimeter resolution – UAS-based RS: up to subcentimeter resolution (ii) Increase of temporal resolution: – satellite-based RS: up to daily repetition – airborne-based RS: up to multiple repetitions per day – UAS-based RS: up to hourly repetitions (iii) Increase of spectral resolution: – satellite-based RS: multi- and hyperspectral, multithermal, X-, C-, L-band sensors – airborne-based RS: multi- and hyperspectral, multithermal, mm-microwave, X-, C-, L-band sensors – UAS-based RS: low-weight multi- and hyperspectral, multithermal, mm-microwave, X- and C-band sensors In summary, for all RS platforms, all available sensor technologies are mountable, which not only enables the determination of species or plant communities but also of vitality, nutrient status, and stresses, e.g., in crops or trees (Jones and Vaughan, 2010; Thenkabail et al., 2011; Wulder and Franklin, 2003). The high temporal resolution can even capture phenology in vegetation from leaf development to flowering, ripening, and senescence (More et al., 2016; Parplies et al., 2016; Bendig et al., 2015). Finally, using fluorescence remote sensing technology, photosynthesis can be observed in a diurnal or seasonal context (Schickling et al., 2016; Wieneke et al., 2016; Rascher et al., 2015; Zarco-Tejada et al., 2003; https://earth.esa.int/web/guest/missions/esa-futuremissions/flex). The context of this chapter is clearly not on remote sensing methods for mapping of vegetation or on surveying techniques for mapping vegetation. The focus is on how GIS technologies can support vegetation mapping. According to Pedrotti (2013) “a vegetation map consists of a topographic map that shows vegetation units..” This more traditional view of vegetation mapping fits very well into the understanding of spatial information systems described by Bill (2016). All content-related spatial information systems for soil, geology, vegetation, etc. should be based on a topographic information system (Bill, 2016). This concept is also described by Bareth (2009) in the context of establishing a spatial environmental information system (SEIS). Nowadays, topographic information systems have replaced the traditional topographic mapping procedures. Consequently, modern mapping of vegetation should be based on those systems that are usually established, maintained, and provided by official surveying and mapping agencies.

2.01.3

Vegetation Data in Official Information Systems

The availability of vegetation data in spatial information systems is manifold and ranges over all scales. Global land cover data are, e.g., available from several research initiatives like the Global Land Cover Facility (GLCF) (http://glcf.umd.edu), the Global Land Analysis & Discovery Group (http://glad.umd.edu), or the GlobeLand30 initiative, which provides a 30-m land cover data set (http://www. globallandcover.com; Arsanjani et al., 2016; Chen et al., 2015; Congalton et al., 2014). The latter is of significance because it contains global land cover classes for cultivated lands, forests, grasslands, shrublands, tundra, and wetlands (compare Fig. 3). Besides the LULC data, on a regional or national scale numerous LULC data products are available that contain more detailed information on vegetation. For example, the European Union’s CORINE Land Cover data is available for most of the member states for 1990, 2000, 2006 and 2012 for minimal mapping units of 25 ha containing 44 mapping units (http://land.copernicus.eu/paneuropean/corine-land-cover). The data is available via download from various EU or national official websites (e.g., Germany: http://www.corine.dfd.dlr.de/intro_en.html). Some EU states improved the spatial CLC data quality by increasing the minimal mapping unit to 10 ha or even 1 ha. For example, the CLC data for the year 2012 for Germany is based on such a minimal mapping unit of 1 ha containing 37 land cover classes (Fig. 4) (Keil et al., 2015). For this enhancement of the spatial quality, the Authorative Topographic-Cartographic Information System (ATKISdwww.atkis.de) was implemented in the land cover analysis (Keil et al., 2015). The importance of official LULC data, usually provided by the official surveying and mapping agencies, for LULC products is also stated by Inglada et al. (2017). The authors present a processing scheme based on Sentinel-2 image data to derive LULC for France with a 10 m resolution. In the processing scheme, additional LULC data is used as reference data. Finally, LULC data are available in varying detail from national authorities for many countries, e.g., for the United States from the USGS Land Cover Institute (LCI) (https://landcover.usgs.gov) or from the USDA’s Cropscape Project (https://nassgeodata.gmu.edu/CropScape). Roy et al. (2015) present a seamless and very detailed vegetation type map for India at a scale of 1:50,000. (Fig. 6). The methodological approach of the mapping of vegetation is shown in Fig. 5. The authors combined several spatial data sources in a GIS environment for vegetation classification, mapping, and accuracy assessment.

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Fig. 3 The GlobeLand30 global land cover map. Figure from Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G., Peng, S., Lu, M., Zhang, W., Tong, X., Mills, J. (2015). Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing 103, 7–27, doi:10.1016/j.isprsjprs.2014.09.002, with permission.

GIS for Mapping Vegetation

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FOREST AND SEMINATURAL AREA FORESTS

ARTIFICIAL SURFACES URBAN FABRIC

311 Broad-leaved forest 312 Coniferous forest 313 Mixed forest

111 Continuous urban fabric 112 Discontinuous urban fabric

INDUSTRIAL, COMMERCIAL AND TRANSPORT UNITS

SCRUBS AND/OR HERBACEOUS VEGETATION

121 Industrial, commercial and public units 122 Road and rail networks and associated land 123 Port areas 124 Airport

321 Natural grassland 322 Moors and heathland 324 Transitional woodland-scrub

OPEN SPACES WITH LITTLE OR NO VEGETATION

MINES, DUMPS AND CONSTRUCTION SITES

331 Beaches, dunes, sand 332 Bare rock 333 Sparsely vegetated areas 334 Burnt areas 335 Glaciers and perpetual snow

131 Mineral extraction sites 132 Dump sites 133 Construction sites

ARTIFICIAL NON-AGRICULTURAL VEGETATED AREAS 141 Green urban areas 142 Sport and leisure facilities

WETLANDS INLAND WETLANDS 411 Inland marshes 412 Peat bogs

AGRICULTURAL AREAS ARABLE LAND

COASTAL WETLANDS

211 Non-irrigated arable land

421 Salt marshes 423 Intertidal flats

PERMANENT CROPS 221 Vineyards 222 Fruit trees and berries plantations

WATER BODIES INLAND WATERS

PASTURES

511 Water courses 512 Water bodies

231 Pastures

MARINE WATERS

HETEROGENEOUS AGRICULTURAL AREAS

521 Coastal lagoons 522 Estuaries 523 Sea and ocean

242 Complex cultivation patterns 243 Land principally occupied by agriculture, with significant areas of natural vegetation

Fig. 4 LULC classes of CLC 2012 for Germany. From Keil, M., Esch, T., Divanis, A., Marconcini, M., Metz, A., Ottinger, M., Voinov, S., Wiesner, M., Wurm, M., Zeidler, J. (2015). Updating the Land Use and Land Cover Database CLC for the Year 2012 - “Backdating”of DLM-DE from the Reference Year 2009 to the Year 2006. Umweltbundesamt: Dessau-Roßlau, 80 p. (http://www.umweltbundesamt.de/publikationen/updating-the-land-use-landcover-database-clc-for), with permission.

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Fig. 5 Methodological approach of vegetation type mapping for India. Figure from Roy, P.S., Behera, M.D., Murthy, M.S.R., Roy, A., Singh, Sarnam, Kushwaha, S.P.S., Jha, C.S., Sudhakar, S., Joshi, P.K., Reddy, C.S, Gupta, S., Pujar, G., Dutt, C.B.S., Srivastava, V.K., Porwal, M.C., Tripathi, P., Singh, J.S., et al. (2015). New vegetation type map of India prepared using satellite remote sensing: comparison with global vegetation maps and utilities. International Journal of Applied Earth Observation and Geoinformation 39, 142–159, with permission.

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1. Mixed forest formation

2. Gregarious forest formation Shorea sp. Tropical evergreen Tectona sp. Andaman tropical evergreen Dipterocarpus sp. Southern hilltop tropical evergreen Bamboo sp. Secondary tropical evergreen Pinus sp. Sub-tropical broadleaved evergreen Abies sp. Sub-tropical dry evergreen Quercus sp. Montane wet temperate Cedrus sp. Himalayan moist temperate Hardwickia sp. Sub-alpine Red sanders Cleistanthus sp. Tropical semi-evergreen Boswellia sp. Tropical moist deciduous Acacia catechu Tropical sal mixed moist deciduous Anogeissus pendula Tropicalteak mixed moist deciduous Acacia senegal Tropical dry deciduous Rhododendron sp. Tropical sal mixed dry deciduous Juniperus sp. Tropical teak mixed dry deciduous Tropical thorn Dry tropical bamboo mixed Temperate coniferous Sub-tropical pine mixed

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Arecanut Coconut Mango Saffron Cryptomeria Padauk

3. Locale-specific formation Mangrove forest Avicennia sp. Lumnitzera sp. Mangrove scrub Phoenix sp. Rhizophora sp. Xylocarpus sp. Littoral forest Fresh water swamp Lowland swamp Syzigium sp. swamp Sholas Riverine Ravine Sacred groves Tropical seasonal swamp Kans

4. Plantation Forest plantation Eucalyptus sp. Acacia sp. Casuriana sp. Alnus sp. Mixed plantation Gliricidia sp.

5. Degraded formation Degraded forest Shifting cultivation Abandoned jhum Current jhum

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10.Others Agriculture Cold deserts Settlement Barren land River bed Water body Wet lands

8. Grassland Grassland Wet Riverine Moist alpine pasture Dry alpine pasture Dry Swampy Lasiurus-Panicum sp. CenchrusDactyloctenium sp. Sehima-Dichanthium sp. Costal swampy

Fig. 6 Detailed vegetation type map of India which is produced at a scale of 1:50,000. Roy, P.S., Behera, M.D., Murthy, M.S.R., Roy, A., Singh, Sarnam, Kushwaha, S.P.S., Jha, C.S., Sudhakar, S., Joshi, P.K., Reddy, C.S, Gupta, S., Pujar, G., Dutt, C.B.S., Srivastava, V.K., Porwal, M.C., Tripathi, P., Singh, J.S., et al. (2015): New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities. International Journal of Applied Earth Observation and Geoinformation 39, 142–159

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As Pedrotti (2013) stated and as is very obvious from the case studies just mentioned, vegetation maps are based on topographic maps and the combined use for mapping of vegetation is described in detail in the following section “Multidata Approach for Land Use and Land Cover Mapping.” Topographic maps are produced in many countries ranging in scale from 1:25,000 to 1:1,000,000. The change from static paper map production to topographic information systems from the 1990s is based on GIS technologies. The official surveying and mapping agencies are responsible for the implementation and maintenance of such topographic information systems. These are, e.g., in P.R. China the National Geomatics Center of China (NGCC) (http://ngcc.sbsm.gov.cn/article/en), in the Netherlands the Kadaster (https://www.kadaster.nl/-/top10nl), or in the United States the USGS’s The National Map (https:// nationalmap.gov). There are differences in detail of these topographical products; e.g., the German ATKIS is a multiscale topgraphic information system which provides digital landscape models (DLMs) (vector data), digital elevation models (DEMs) (raster data), digital orthophotos (DOPs) (raster data), and digital topographic maps (raster data) (www.atkis.de). The most precise and content rich is the Basis-DLM which is generated to represent topographic information at a scale of 1:10,000 to 1:25,000. The LULC information in the Basis-DLM is very rich and is summarized in Table 1. As is clearly obvious from Table 1, the ATKIS already provides rich information on vegetation in a high spatial resolution and the data is updated according to differing priority classes from every 6 months up to every 5 years. Additionally, the different DLMs for various scale are described by detailed mapping procedures and quality demands. Therefore, the metadata of the ATKIS DLMs provide a sound information base for the scales and purposes for which each DLM can be used. The combined analysis of RS-based LULC with official geodata like ATKIS is presented in detail in section “Multi-Data Approach for Land Use and Land Cover Mapping” following. Finally, user-driven data portals like Open Street Map (OSM) (www.openstreetmap.org) must be mentioned. These are unofficial, community-driven spatial data mapping activities but are also very rich in topographic information and also include LULC data, in varying detail and in a less-organized LULC class scheme than the official topographic map products. Nevertheless, the data is extremely valuable and also can be incorporated in LULC analysis using remote sensing (Johnson and Iizuka, 2016).

Table 1

Vegetation-specific LULC classes contained in the ATKIS Basis-DLM

ID

Feature class

Feature sub-class

Sub-class ID

43001

Agriculture

43002

Forest

Arable Land Arable Field Orchard Hops Grassland Meadow Orchard Horticulture Tree Nursery Vineyard Orchard Deciduous Forest Coniferous Forest Mixed Forest

1010 1011 1012 1020 1021 1030 1031 1040 1050 1100 1200 1300

43003 43004 43005 43006 54001

Shrubland Heathland Peatland Wetland Characteristic vegetation

Deciduous Tree Coniferous Tree Deciduous Trees Coniferous Trees Mixed Trees Hedge Deciduous Tree Row Coniferous Tree Row Mixed Trees Row Brushwood Shrubs Forest Aisle Reed Bed Grass Fruit Tree

1011 1012 1021 1022 1023 1100 1210 1220 1230 1250 1260 1300 1400 1500 1600

10

GIS for Mapping Vegetation

2.01.4

Multi-Data Approach for Land Use and Land Cover Mapping

2.01.4.1

Introduction to Land Use and Land Cover Mapping

The availability of spatial land use (LU) and land cover (LC) information for larger areas is essential for numerous topics. The spectrum ranges from local to global applications with different demands on LULC data from the various stakeholders (Giri, 2012; Komp, 2015; Mora et al., 2014). Key areas are, for instance, land use planning (Manakos and Lavender, 2014), food security (Foley et al., 2005, 2011; Thenkabail, 2010, 2012) or environmental modeling and climate change studies (Bareth, 2009; Bojinski et al., 2014; Simmer et al., 2014). In contrast to vegetation inventories, which focus on selected vegetation types, LULC datasets provide comprehensive information on the composition of the entire land surface. LULC datasets therefore also contain information on built-up areas, water bodies or barren land (Anderson et al., 1976). Nevertheless, vegetation mapping, especially on croplands, plays a preeminent role and is often a major driver for conducting LULC mapping (Teluguntla et al., 2015). Nowadays, LULC data is usually provided in the form of digital maps either in raster or vector data format. In such maps, areas of different LULC are allocated into different categories (e.g., urban, forest, water). Although LU and LC are strongly interconnected, they have different meanings. Land cover denotes the observed biotic and abiotic composition of the earth surface with, e.g., forests, water bodies, urban areas, etc. (Giri, 2012; Meyer and Turner, 1992). Land use, however, refers more to the usage of land by humans (Campbell and Wynne, 2011a; Loveland and DeFries, 2004). For instance, a vegetated area may have the land cover of forest or trees, but the land use may be recreation area or a tree nursery. Additionally, a specific land use can be composed of several land cover types. However, to satisfy as many potential users as possible, maps often contain a mixture of LU and LC (Anderson et al., 1976). The categorical information provided by LULC maps is usually based on a classification scheme that may be newly created or adapted from an established nomenclature. One of the first classification schemes is the one by Anderson et al. (1976), which differentiates basic land cover types like Urban, Agricultural land or Water with up to two sublevels (e.g., Level I: Urban or built-up (1), Level II: Residential (11), Level III: Single-family Units (111) or Level I: Agricultural Land (2), Level II: Cropland or Pasture (21)). Many of the succeeding classification systems built upon this structure, although adaptations concerning a specific thematic focus or the observation scale are common, for example, NOAA (2016); Xian et al. (2009). Further examples for popular contemporary LULC cover products in this regard are the National Land Cover Database 2011 (NLCD 2011) for the United States of America (Homer et al., 2015) and its predecessors, or the CORINE Land Cover (CLC) program of the European Union (Büttner et al., 2014; EEA, 2007). LULC data is usually needed for rather large areas, irrespective of the investigation scale. Nevertheless, depending on the main application and the size of the study area, mapping endeavors can be roughly categorized as local, regional to country-wide or as continental to global scale studies (Table 2). Due to the large amount of work and costs associated with LULC mapping through field surveys, mapping of large areas only became possible after the initiation of the Landsat Program and the launch of Landsat-1 in 1972 (Loveland, 2012). Countless satellite-borne earth observation systems with a variety of sensor specifications have emerged since then. Table 1 provides some examples of contemporary sensors for each scale region. The rough categorization in the tables is based on the general relationship between sensor capabilities (in terms of spatial, spectral and temporal resolution), the size of the investigation area and the minimum mapping unit (MMU). The MMU determines the size of the smallest features, which are differentiated as discrete areas in a map (Lillesand et al., 2014; Warner et al., 2009). However, many sensors may be suitable for LULC mapping endeavors at multiple scales. The spatial resolution region of the Landsat sensors of about 30 m or finer (
Landsat-4) can be considered as a quasistandard as input data for regional to nationwide LULC mapping. Other popular moderate spatial resolution sensors (ca. 10–100 m), which are

Table 2

Selection of popular satellite sensors that are frequently used for land use/land cover mapping, at different spatial scales

Scale region

Spatial resolution

Satellite (sensor)

Swath width (km)

Spatial resolution (m)

Spectral range (nm)

Continental to global

Coarse (>100 m)

NOAA 17 (AVHRR) SPOT (VGT) Terra (MODIS)

2940 2250 2330

1100 1000 250–500

500–1250 430–1750 366– 14,385 450–2350 450–2350 443–842 500–1730 520–1700 530–1165 400–1040 440–850

Regional to countrywide Regional to national

Moderate (10–100 m)

Local

Fine (<10 m)

Landsat-5 (TM) Landsat-8 (OLI) Sentinel-2A (VNIR) SPOT 5 (HRG) IRS-P6 Terra (ASTER) WorldView-3 (VNIR) RapidEye

185 185 290 60 141 60 13.1 77

30 30 (MS); 15 (pan) 10 10 (MS); 20 (SWIR) 23.5 15 (VNIR) 1.2 (MS); 0.3 (pan) 5 m (L3A)

Revisit frequency (days) 1 1 1 16 16 10 26 (3) 14 16 1–4.5 1–5.5

Modified and complemented after Wulder, M.A., White, J.C., Goward, S.N., Masek, J.G., Irons, J.R., Herold, M., Cohen, W.B., Loveland, T.R., Woodcock, C.E. (2008). Landsat continuity: issues and opportunities for land cover monitoring. Remote Sensing of Environment 112, 955–969, doi:10.1016/j.rse.2007.07.004.

GIS for Mapping Vegetation

11

nowadays used for regional scale LULC mapping, are sensors of the Satellite Pour l’Observation de la Terre (SPOT) program, RapidEye (Adam et al., 2014; Conrad et al., 2014; Low et al., 2013) (although having 5 m spatial resolution) or more recently Sentinel-2 (Drusch et al., 2012; Immitzer et al., 2016). While traditionally only optical data was used for land cover mapping, especially for crop type mapping, nowadays many studies exist that combine optical and synthetic aperture radar (SAR) data or even solely rely on the latter (Bargiel and Herrmann, 2011; Inglada et al., 2016; Lussem et al., 2016; McNairn et al., 2002, 2009; Waske and Braun, 2009). For continental to global scale LULC mapping, usually data of optical sensors with shorter revisiting time, but coarser spatial resolution, is used. Tucker et al. (1985) created a continent-scale land cover map for Africa using data of the meteorological sensor Advances Very-High Resolution Radiometer (AVHRR) with 4 km spatial resolution. After that, the spatial resolution of land cover maps based on AVHRR data with actual global coverage was gradually increased, for example, by Defries and Townshend (1994) (1  1 ), De Fries et al. (1998) (8 km) and (De Fries et al., 1998; Hansen et al., 2000) to a 1-km scale. With sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS), the data quality for global LC classifications was increased significantly (Friedl et al., 2002; Giri et al., 2005). Wang et al. (2015) use MODIS data to map global LC at 250 m spatial resolution. Through the significant increase in computation power, currently Landsat-type data with a spatial resolution of 30 m is (finally) used for global land cover classifications (Ban et al., 2015; Chen et al., 2015; Giri et al., 2013; Gong et al., 2013; Liang and Gong, 2015; Mora et al., 2014). For LULC studies at local scales, usually high or very high spatial resolution sensors like Worldview-2/3 are incorporated (Aguilar et al., 2013; Castillejo-González et al., 2009; Jawak and Luis, 2013; Pacifici et al., 2009). Such sensors provide data with a spatial resolution comparable to aerial photographs, while containing much more spectral information.

2.01.4.2

Methods for Remote Sensing-Based Land Use/Land Cover Mapping

LULC data based on remote sensing imagery can be created by visual image interpretation (especially with high or moderate spatial resolution data) using a GIS and/or by the incorporation of supervised or unsupervised (semi-)automated image classification methods. In the case of visual image interpretation, areas of a specific land cover type are identified and delineated by an analyst in a GIS environment (Lillesand et al., 2014). Here, the analyst has full control of the analysis. It has, therefore, been the principal approach for the creation of the CLC datasets (EEA, 2007). However, this procedure is still very time consuming and cost intensive. Furthermore, multiple analysts may allocate a certain area to different categories. To account for these shortcomings, most remote sensing data analysis software packages include various image classification algorithms, which differentiate LULC types based on their spectral or backscatter patterns recorded in the imagery (Richards, 2013). In most cases, supervised classification approaches are applied (e.g., the widely known parametric maximum likelihood classification (MLC)). In supervised approaches, ground reference information is incorporated to connect areas of known land cover types with their corresponding patterns in the remote sensing data to train the classifier (Lillesand et al., 2014). Fig. 7 depicts the general work flow of a standard remote sensing classification analysis. In order to better exploit the information content provided by newly emerging satellite sensors, more and more sophisticated classification algorithms have been continuously introduced to remote sensing. In the past decades, nonparametric methods in particular have been frequently used, such as artificial neural networks (Benediktsson et al., 1990; Kanellopoulos et al., 1992; Kavzoglu and Mather, 2003), support vector machines (Huang et al., 2002; Mountrakis et al., 2011) or random forests (Gislason et al., 2006). These algorithms are generally known to cope better with issues like limited availability of training data or high data dimensionality than the traditional parametric methods (Adam et al., 2014; Dixon and Candade, 2008; Foody and Mathur, 2006; Rodriguez-Galiano et al., 2012; Shao and Lunetta, 2012).

Remote Sensing Image

Preprocessing - radiometric - geometric

Reference Data (Training)

Training of Classifier

Reference Data (Validation)

Supervised Classification

NO

Fig. 7

Validation of Result

Accuracy sufficient?

YES

Post-Classification Refinement

Final Classification Result

Standard work flow for remote sensing-based land use and land cover mapping using supervised classification methods.

12

GIS for Mapping Vegetation

Although the quality of remote sensing data and performance of classification algorithms steadily increases, there are fundamental limitations of solely remote sensing-based land cover mapping that have not been overcome. First, many land cover types exhibit a very similar appearance to other types in remote sensing imagery. Although the resolution capabilities for land cover discrimination differ from sensor to sensor (and, for instance, from optical to SAR systems), often many classes remain that are hard or even impossible to differentiate. For example, many crop types or tree species have a high spectral similarity at many stages of the growing season (Odenweller and Johnson, 1984). Additionally, the spectral response of a road pavement can be very similar to the appearance of surrounding surfaces in multispectral remote sensing images and is therefore frequently confused by classification algorithms (Long and Zhao, 2005). Second, specific land cover types derived from remote sensing can be related to different land uses (as stated earlier). For instance, it is usually impossible to differentiate the lawn of a sports field from pasture, especially when single observation imagery is used. In both cases the land cover is grass, but the land use is totally different. Consequently, in many cases it is impossible to derive the actual land use directly without additional information.

2.01.4.3

Integration of Remote Sensing and GIS for Land Use/Land Cover Mapping

Geographic information systems and (digital) remote sensing analysis systems were developed independently and for different purposes (roughly at the same time). However, they rely on very similar software and hardware technologies and were both designed to process large amounts of spatial data at various scales (Ehlers, 1992). GISs were designed to handle and combine different kinds of spatial data like topographic data, soil or elevation data and imagery, while remote sensing systems had a strong focus on imagery. In the field of visual image interpretation, the usage of additional information is well established at least since the middle of the 20th century (Merchant and Narumalani, 2009). Accordingly, GIS-based LULC mapping by visual image interpretation usually incorporates additional information like topographic maps (e.g., Büttner et al. (2012)). Consequently, by the 1970s data and methods available in GIS environments were recognized as a way to enhance LULC mapping based on image classification methods (Strahler et al., 1978). From the remote sensing perspective, this is often referred to as integration or usage of ancillary or additional data into the image classification analysis (Hutchinson, 1982). This integration can be conducted (i) before (preclassification), (ii) within (classification modification) or (iii) after the classification process (postclassification) (Lillesand et al., 2014). Preclassification methods (i) often comprise the stratification of the input image into different areas (strata) based on the spatial information provided by the ancillary data layer. The intention behind this procedure is to increase the homogeneity within the strata, e.g. based on boundaries of principal land use types or soil types (Hutchinson, 1982; Janssen et al., 1990). Especially for crop type mapping these fairly simple methods have proven to be very effective in restricting the crop classification to arable land (De Wit and Clevers, 2004; Smith and Fuller, 2001; Turker and Arikan, 2005; Waldhoff et al., 2012). The integration of ancillary data within the classification process (ii) often refers to the combination of the ancillary data layers (e.g.) with bands of one or multiple remote sensing images into a single dataset for the classification (stacked vector approach) (Richards, 2013). For such approaches, recently random forests (Corcoran et al., 2013) or earlier artificial neural networks (Kavzoglu and Mather, 2003; Lillesand et al., 2014) have successfully been used. However, these analysis set-ups are usually very computation intensive, if many data layers are combined (Wu et al., 2016). Furthermore, the analyst only has little insight into why or how the algorithm solves a certain classification problem. More control by the analyst and the possibility of combining different data sources and expert knowledge in the classification process provide classification or decision trees (DT) (Friedl and Brodley, 1997; Hansen et al., 1996; Homer et al., 2015). Here, a series of hierarchical decisions are applied to the different data layers to segment images at the pixel level (Richards, 2013). However, DT can get very complex and Pal and Mather (2003) report that DT may not perform well with high-dimensional input data. A similar concept concerning the application of hierarchical decision rules provides object-based image analysis (OBIA) methods (Benz et al., 2004; Blaschke et al., 2014; Blaschke and Strobl, 2001). In OBIA, remote sensing images are segmented into groups of pixels (objects) with specific properties. Such approaches are especially valuable for very high spatial resolution data, where the size of the pixels is smaller than size of the mapped objects (Blaschke, 2010). To improve the segmentation process, several additional data sources like building footprints can be integrated (Moskal et al., 2011). Through the adaptation of basic concepts of visual image interpretation, GIScience and landscape ecology (Blaschke et al., 2014), such methods have a stronger GIS-like “look and feel.” However, similar to DT, the generation of rule sets is not always straightforward and can be time consuming. Furthermore, when using moderate spatial resolution data, the advantages over pixel-based methods may disappear (Duro et al., 2012). In most cases, postclassification methods (iii) make use of classic GIS overlay analysis functionalities (Burrough and McDonnell, 1998). Thus, the analysis is usually switched from a remote sensing to a GIS software. However, GIS functionalities are, in many cases, only used for minor modifications of a LULC map like reclassification or postclassification sorting (Campbell and Wynne, 2011b). From a remote sensing perspective, such procedures are usually the end of the analysis. Nevertheless, GISs have the capabilities to also realize sophisticated mapping strategies like iterative/sequential masking approaches for multitemporal crop type mapping (Turker and Arikan, 2005; Van Niel and McVicar, 2004; Waldhoff et al., 2012), time series analyses (Yang and Lo, 2002) or change detection (Coppin et al., 2004; Lu et al., 2004; Shalaby and Tateishi, 2007; Wu et al., 2006; Yuan et al., 2005).

2.01.4.4

Data and GIS Methods for Enhanced Land Use/Land Cover Mapping

Although the usefulness of GIS concepts for LULC mapping has been known for decades, they aredto datedcomparatively seldom exploited to the fullest in the LULC mapping community. However, from a GIS perspective, methods applied after the classification

GIS for Mapping Vegetation

13

process can even become the dominant part of the LULC map production. Nowadays, popular remote sensing software such as ENVI also contain basic GIS functionalities. Nevertheless, matured GI-Systems contain much more powerful overlay or data integration tools for the handling of categorical information (cf. Heywood et al. (2011); Longley et al. (2015)). Furthermore, manifold sophisticated geodata are available for the different scale levels, which already contain high-quality information for many LULC categories (see previous section, section “Vegetation Data in Official Information Systems”). Good examples for the information richness, such datasets are vector-based DLMs, which are usually intended for the scale range of 1:10,000 to 1:25,000. In general, such datasets can be used for regional to nation-wide LULC mapping. DLMs not only provide basic topographic information, they also contain a lot of land use information. Nowadays, DLMs are available as open data for many countries. Example datasets are the Topologically Integrated Geographic Encoding and Referencing (TIGER) data for the USA (www.census.gov), the Authorative Topographic-Cartographic Information System (ATKIS) for Germany (AdV, 2006) or the TOP10NL for the Netherlands (Kadaster, 2016). In addition, for very high spatial scales (i.e., ca. 1:1000 or larger) cadastral data like the Authoritative Real Estate Cadastre Information System (ALKIS) for Germany (AdV, 2016) is available. The ATKIS, for example, describes the landscape in the form of point line or polygon features. Due to its origin from the official state survey and mapping agency, it is characterized by a high geometric accuracy (AdV, 2006). The ATKIS includes diverse information, for instance on the transportation network, but it also differentiates built-up areas into different types of residential or industrial areas, forests into deciduous and coniferous tree populations as well as agricultural areas into arable land, grassland or orchards. Fig. 8 shows a selection of the ATKIS (Basis DLM) information content occurring in this area. The categorical information (cf. Table 1) in such datasets, which indicates the actual land use, is especially valuable. In this regard, the ATKIS conveys detailed information, if a certain area is (e.g.) a golf course or an airfield, while the basic land cover may be grassland in both cases. Other spatial information like the boundaries of protection areas may also help to narrow down the actual land use or the land use intensity (Bareth and Waldhoff, 2012). As stated earlier, much of this information cannot be obtained from remote sensing at all, or not in the provided geometric and thematic quality. The combination of data and methods available in GIS environments can therefore substantially enrich the information content, especially concerning land use, and even increase the geometric quality of LULC maps.

2.01.4.5

Multi-Data Approach for Enhanced Land Use/Land Cover Mapping

An approach, which combines many data integration strategies and which will be used as an example with German geodata is the Multi-Data Approach (MDA) introduced by Bareth (2008). However, approaches with similar concepts can be found, for example, for the Netherlands (Hazeu, 2014) or again for Germany (DeCover, 2012; Hovenbitzer et al., 2014). The MDA has been

1

54001 Vegetation Characteristic 75009 Area Border 44004 Water Center Line 54001 Vegetation Characteristic 42003 Road Center Line 42008 Roadway Center Line 53003 Road Path Steep Track 53001 Building in Traffic Area 53009 Building in Water Area 54001 Vegetation Characteristic 42001 Road Traffic 42009 Square 43003 Copse 43007 Uncultivated-Area 43001 Agriculture C C 43002 Wood 42010 Rail Traffic 41001 Residential Area 41002 Industrial and Commercial Area 41006 Combined Use Area 41007 Area With Special Functional Characteristic 41008 Sport Leisure And Recreation Area 41009 Cemetery 44006 Standing Water km

Fig. 8 Original visualization of selected ATKIS classes concerning the information provided by DLMs for enhanced land use/land cover mapping. Data source: Land NRW (2017).

14

GIS for Mapping Vegetation

continuously further developed since the mid-1990s and has been adapted to several study cases in Germany and China (Bareth, 2001; Rohierse and Bareth, 2004; Waldhoff, 2014; Waldhoff and Bareth, 2009; Waldhoff et al., 2015). The basic principle of the MDA is to use the best available source of information for each land use class to comprehensively enhance the information content of the final LU map. In this context, remote sensing-based classification results can be regarded as the basis for the integration or even as (just) one of many input data sources. Fig. 9 displays the basic workflow of the MDA.

Remote Sensing Part Import of Satellite Data Time 1

Classified Satellite Image T1

Supervised Classification

Classified Satellite Image T1

Supervised Classification

Import of Satellite1 Data Time 1

Classified Satellite Image T2

Supervised Classification

Import of Satellite2 Data Time 2

Classified Satellite Image Tn

Supervised Classification

Import of Satelliten Data Time n

Image Overlay Multitemporal/ Multisensoral Classification

Singletemporal Classification

GIS Part Import

Official or available sources of land use data, e.g. in Germany the official topographic-cartographic information system called ATKIS

Official or available sources of data about protected areas

Official or available sources of other land use information

Classified Land Use

Forest

Overlay

Arable Land

Overlay

Multidata Land Use 1

Residental

Overlay

Multidata Land Use 2

Wetlands

Overlay

Other

Overlay

Water Conservation Area

Overlay

Multidata Land Use n

NatureReNature Reservation servation Area Area

Overlay

Multidata Land Use n+1

Biotope Protection Area

Overlay

Multidata Land Use n+2

Other

Overlay

Multidata Land Use n+3

Other

Overlay

Multidata Land Use n+m+l

Mutlidata Land Use 3 Multidata Land Use 4

Multidata Land Use n+m

Final Land Use Data

Fig. 9 Basic workflow of the multi-data approach. Modified from Bareth, G. (2008). Multi-data approach (MDA) for enhanced land use/land cover mapping, the international archives of the photogrammetry, remote sensing and spatial information sciences, vol. XXXVII. Part B8. Beijing 2008. International Society of the Photogrammetry and Remote Sensing Beijing, pp. 1059–1066.

GIS for Mapping Vegetation

15

In the initial RS part of the MDA, any classification method for single or multiple input datasets can be used. Preclassification methods like image stratification may be applied beforehand (please see above). The GIS part starts with a homogenization of all datasets (classification results and ancillary data) to the same data model. Subsequently, all dataset are combined into a multi-layer stack. MDA-GIS analyses can be conducted in the vector (Rohierse and Bareth, 2004) or in the raster data model (Waldhoff et al., 2012). However, in the following the raster-based analysis will be focused on. Based on the MDA workflow, the final LU map is created by integrating the desired information of each layer in a sequential manner by using knowledge-based production rules and GIS overlay functionalities (Bareth, 2008). For the application of the MDA in the raster data model, data formats like ESRI GRID allow working with categorical information in a similar manner as within the vector data model (ESRI, 2012a). This means that the information in a multisource raster stack (combine) is also organized into columns and rows in an attribute table (ESRI, 2012b). On such a raster attribute table, knowledge-based production rules can be executed in the form of Standard Query Language (SQL) queries to select the desired information from the data layers. Fig. 10 shows a part of an attribute table of a multilayer stack, where a simple query led to the selection of carriage ways in an ATKIS data layer. By using a Raster Field Calculator (in this case in ArcGIS), the selected information is transferred to the target layer. Production rules can be formulated for simple information transfer from the source to the target layer or they can include multiple queries or calculations incorporating multiple layers to even derive new information (Rohierse and Bareth, 2004).

2.01.4.6

Examples for Land Use/Land Cover Map Enhancement With the MDA

To demonstrate how LULC maps can be enhanced by using the framework of the MDA, some examples of the integration of German ATKIS data with multiple remote sensing classifications are given. However, the spatial information used here should be available in most DLMs or comparable datasets for many other countries. Additionally, other spatial datasets, for example on biotopes, protection areas or soil maps, can be incorporated to further increase the information content of LULC datasets concerning land management or land use intensity (Bareth and Waldhoff, 2012). The MDA procedures for integration of (multitemporal) remote sensing classification results and additional basic geodata (in this case ATKIS data) generally address the following aspects: – – – –

facilitate and exploit the usage of multitemporal or multisource data (i); improvement of geometrical accuracy (ii); increase categorical detail (iii); reduction of classification errors (iv).

Fig. 10

Example for the integration of information of multiple sources via raster attribute tables in a GIS.

16

GIS for Mapping Vegetation

(B)

(A)

Summer Crops

Water Body

Bare Ground

Winter Rapeseed

Ashalt

Spring Barley

Coniferous Trees

Winter Wheat

Vegatable

Deciduous Trees

Winter Barley

500

(C)

m

Winter Rapeseed

Sugar Beet

Potato

Winter Wheat

Maize

Winter Barley

(D)

Field (Sports)

Residential Area, enclosed

Sports Facilities

Root Crops

Winter Wheat

Public Place

Decidous Trees

Commercial Area

Recreation Centre

Cereal

Winter Barley

Summer Crop

Copse

Market Garden

Public Park

Pasture

Agricultural Field

Path / Track

Railway Line

Sewage Treatment

Allotment

Rapeseed

Urban Green Area

Field Path / Carriageway

Federal Motorway

Waste Disposal Facility

Cemetery

Coniferous Trees

Bare Ground

Residential Area

Federal Road

Industrial and Commercial Area

Road Traffic

Deciduous Trees

Tree Nursery

Commercial Area

Country Road

Combined Use Area, open

Square

Asphalt

Market Garden

Road

District Road

Combined Use Area, enclosed

Farmland

Potato

Copse

Railroad

Municipal Road

Administration Area

Grassland

Maize

Public Facilities Area

Country Road

Other Road

Research and Education

Tree Nursery

Sugar Beet

Mixed Usage Area

Federal Road

Carriageway

Health Care and Cure

Deciduous Trees

Path/Track

Social Amenities

Copse

Residential Area, open

Other Functional Area

Waste Disposal Facility

Fig. 11 Examples for enhanced land use/land cover mapping using the Multi-Data Approach (MDA): (A) monotemporal land use classification result; (B) multitemporal crop classification; (C) illustration of the spatial information provided by the German ATKIS (already rasterized; some classes are aggregated); (D) final MDA land use classification. All subsets display the same area, at the identical scale. The ATKIS data was provided by Land NRW (2017).

Fig. 11A shows a classification result derived from a single remote sensing image. The crop classification in Fig. 11B was generated from the combination of several monotemporal classification results using the MDA methodology for the same area (Waldhoff et al., 2012). For this purpose, a sequential crop type mapping strategy was applied. The final class-specific mapping results were compiled from the classifications of selected observation dates to improve the crop type differentiation. Furthermore, when incorporating multiannual remote sensing data, also crop rotations can be derived using the MDA methodology (Waldhoff, 2014; Waldhoff et al., 2017). Fig. 11C illustrates selected and already rasterized ATKIS classes for the same area. As can be seen in Fig. 11A, apart from arable land only principal LC types like asphalt/impervious surface, grassland, forest or bare ground could be differentiated. Yet, more precise information, for example, on the transportation network, nonagricultural vegetation and the land use within built-up areas is already available in the ATKIS (Fig. 11C). However, concerning arable land only highly aggregated information is provided. The final MDA-LU for the same area is depicted in Fig. 11D, where the different data layers were combined. By just transferring the road network information, for example, to the final LU map, the borders of

GIS for Mapping Vegetation

17

LU classes appear much sharper. Moreover, different categories of roads are now differentiated in the map. In combination with the transfer of land use information on different settlement land use types, the differentiation of urban area is also significantly improved.

2.01.4.7

Summary and Conclusion

For many LULC classes, and especially for areas characterized by high land use change rates like arable land, remote sensing data is still the only reasonable source to acquire accurate and up-to-date information for wider areas. However, in many cases land use types cannot be clearly differentiated from other land use types solely based on spectral or backscatter properties recorded from the remote sensing systems. Thus, numerous strategies are available to complement or to enhance remote sensing-based LULC mapping. The different approaches range from the analysis of multitemporal and/or multisensor remote sensing data to the incorporation of additional data sources at all stages of the mapping process. Methods and geodata available in GIS environments can greatly contribute to enhance the mapping results at every stage of the LULC analysis. Preclassification stratification of remote sensing data based on additional information can initially increase the workload or the computation time for comprehensive LULC mapping endeavors. However, this can also significantly increase the classification accuracy by splitting the classification problem into smaller tasks. The integration of data within the classification process is another way to improve the classification results. Here, multitemporal optical and SAR data (Inglada et al., 2016; Solberg et al., 1994) as well as other geodata (Strahler et al., 1978) can be combined for the classification. However, even when using sophisticated classification algorithms, machines with advanced computation power may be necessary to analyze the large data stacks efficiently. Classification strategies where the analyst generally has more control of the mapping process include decision trees, OBIA or “comprehensive” GIS approaches. Multisource GIS methods used in the preclassification and predominantly in the postclassification stage were favored in this contribution. GIS methods facilitate the efficient combination of multidate/multisensor classification results, for example to apply sequential crop type mapping strategies. Concerning LU classes, which can barely be differentiated from remote sensing, GIS and the available geodata can be used to specifically enhance the information content for certain LU types significantly. For instance, even with sophisticated approaches like OBIA, it should be hard to distinguish a football pitch from pasture in many cases. DLMs usually contain information that make this differentiation quite easy. In addition, even estimates of the land use intensity and management of agricultural land can be integrated into LULC maps by using geodata on protection areas or statistical data (Rohierse, 2004). The methods presented for the MDA were conducted manually by an analyst using standard GIS Software (ArcGIS). Nevertheless, most of the applied knowledge-based production rules can be transferred to a script to run automated in an operational setting. Finally, the examples presented in this chapter are mostly focused on regional scale LULC mapping. However, DLM data (for example) are usually also available for coarser scales with aggregated information content, appropriate for continental or global LULC mapping approaches. For local scales, cadastral data should be used instead. Even at this scale, this can lead to significant increase degree of LULC differentiation compared to solely remote sensing based approaches (Waldhoff et al., 2015).

2.01.5

Analysis of High-Resolution DSMs in Forestry and Agriculture

Besides the usage of GIS-based analysis for land use and land cover change as well as for species identification or vitality assessment, numerous GIS tools can be applied for structural vegetation analysis. The latter is a quite new research field and is enabled by new sensor technologies, by new software developments from computer vision, and by the miniaturization of electronics enabling lowweight and capable unmanned aerial vehicles (UASs). In agriculture and forestry, the analysis of multi- or hyperspectral remote sensing data for species identification, vitality, yield, biomass, stress, etc. is well established since the 1970s and started in the 1960s (Carneggie and Lauer, 1966; Walsh, 1980). Numerous vegetation indices (VI) are developed for multi- and hyperspectral data to derive vitality, biomass, yield, nitrogen (N) content, and stresses (Kumar et al., 2003; Thenkabail et al., 2000). But there are also limitations of spectral data analysis, e.g., VIs tend to saturate in later growing stages. Therefore, the use of remote sensing methods which can provide structural information like plant height, plant density or vegetation layers have become more important in the last two decades. The most prominent are (i) microwave methods and a younger development is (ii) laser scanning. While (i) is well described for vegetation purposes by Hütt et al. (2016); Koppe et al. (2013); Paloscia and Pampaloni (1988), (ii) revolutionized the airborne data acquisition due to its high spatial resolution and precision. With laser scanning approaches, it is possible to derive plant height and plant growth in centimeter accuracy in crops (Hoffmeister et al., 2010, 2016) and robustly estimate nondestructively crop biomass during crop growth (Tilly et al., 2015). In forests, canopy height and canopy biomass can also be well determined (Naesset and Gobakken, 2008; Nelson, 1997; Reitberger et al., 2009). For both approaches, GIS tools play a minor role due to the processing of backscatter data or of a 3D point cloud. A new opportunity for mapping of vegetation arose with the emergence of more easily applicable UASs and structure from motion (SfM) software. The introduction of multicopter UASs led to an increase of related remote sensing activities (Aasen et al., 2015; Bendig et al., 2015; Colomina and Molina, 2014; Turner et al., 2012). Remotely controlled helicopter-based remote sensing has been able to carry multisensor systems since 2008 (Jaakkola et al., 2010) but piloting such systems still demanded a very high expertise. Nevertheless, a further key development was the introduction of SfM software which enables stereo image

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analysis without knowing the precise camera parameters nor the camera position, resulting in the generation of 3D point clouds, DSMs, and DOPs from overlapping images (Turner et al., 2012). The new opportunities to generate DSMs with standard digital cameras mounted on low-cost, easy to use UASs enabled a new research field in vegetation monitoring. The spatial resolution of such UAS-derived DSMs is well under 0.05 m having a vertical accuracy of approximately 0.03 m (Bareth et al., 2015; Bendig et al., 2013). With such systems, it is now possible to obtain multitemporal DSMs for a study area of up to 1000 ha in the mentioned resolution. The analysis of such DSMs is now clearly again within the GIS domain. Hoffmeister et al. (2010) introduced for crops a multitemporal analysis approach of DSMs derived by terrestrial laser scanning, the crop surface models (CSMs). And Bendig et al. (2013) proved the applicability of this CSM approach for UASderived CSMs. The CSM approach shown in Fig. 12 (Bendig et al., 2013) for pixelwise plant height determination requires the acquisition of a digital terrain model (DTM) also often named a digital elevation model (DEM) after sowing and before emergence of sown crops. This period is time t0 in Fig. 12. During crop growth and development additional UAS campaigns are conducted, resulting in multitemporal DSMs named in Fig. 12 CSM_1, CSM_2, and CSM_3 for t1, t2, and t3, respectively. With GIS software it is now possible to subtract the DEM for t0 from each of the CSMs, resulting in absolute plant height per pixel for t1, t2, and t3. Additionally, plant growth within crop development can be computed with GIS software by subtracting CSM_1 from CSM_2 or CSM_3, and subtracting CSM_2 from CSM_3. The latter is very helpful to investigate crop development during, e.g., drought or nutrient stress or if regreening occurs. An additional very useful GIS function for the CSM approach is zonal statistics which computes descriptive statistics of all pixel values for a defined area of interest (Bareth et al., 2015; Bendig et al., 2013). In Fig. 12, this would be, e.g., the plot shown for the four time windows. Finally, the UAS-based image acquisition results in orthophotos with a very high resolution. The SfM software usually also creates digital orthophotos (DOPs). In a 3D GIS environment, those DOPs can be draped over the generated DSMs. But for mapping of vegetation, the DOPs have an additional value. While surveying vegetation in a small area with very high detail on the species level, such orthophotos of the surveyed area and surrounding can support the work of the surveyors. The detail of such DOPs which are acquired with low-cost, standard digital cameras is shown in Fig. 13 for the Rengen Long-term Grassland Study Site in Germany (Bareth et al., 2015). In the right part of Fig. 13, the potential of the 1 cm resolution is clearly visible for supporting field mapping campaigns. The small flowers (< 3 cm) of single plants are clearly visible. Similarly to the structural vegetation information extraction for agricultural applications using super high-resolution DSMs, the same, even longer-lasting trend can be observed for applications in forestry. In forestry, the term canopy height model (CHM) is of central importance and, e.g., single tree classifications are based on local maxima or watershed segments (Fig. 14) within the CHM (Reitberger et al., 2009). Corresponding to CSMs, CHMs are DSMs or even contain, if derived with laser-scanning, volumetric information. Airborne laser scanning (ALS) became very popular two decades ago for applications in forestry even though the first publications indicating the potential for deriving canpoy height and biomass date back to the laste 1970s (Cecchi et al., 1984; Hyyppa et al., 2001; Lefsky et al., 1990; Vanderbilt et al., 1990). Especially, the development of full waveform ALS improved the quality for forest parameters and single tree extraction by capturing multiple responses from vertical structures within a forest (Koenig and Höfle, 2016; Mallet and Bretar, 2009; Reitberger et al., 2009). A very interesting and in our opinion key publication in this context is the work of Hirschmugl et al. (2007) on improved DSM generation from multiple overlapping image acquisition. The authors state that an enhanced spatial resolution and an improved quality of the DSM result in higher accuracies of single tree detection. With the previously mentioned developments in software, sensor, and UAS engineering, DSM generation with airborne methods improved significantly in the last 10 years since the paper of Hirschmugl et al. (2007) was published. Consequently, GIS-based surface analysis of super-high spatial resolution DSMs will develop as a new research field for applications in forestry.

Multitemporal Crop Surface Models

Plant Height

z

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Layout: G. Bareth, A. Bolten, J. Bendig, N. Tilly Design: R. Spohner

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Fig. 12 Concept of the analysis of multitemporal crop surface models. From Bendig, J., Bolten, A., Bareth, G. (2013). UAV-based imaging for multi-temporal, very high resolution crop surface models to monitor crop growth variability. Photogrammetrie Fernerkundung Geoinformation 2013(6), 551–562, with permission.

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0

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Orthophoto in 1 cm resolution of the Rengen Long-term Grassland Site in Germany derived with a low-cost UAS.

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Fig. 14 CHM with (A) local maxima and (B) watershed segments. From Reitberger, J., Krzystek, P., Stilla, U. (2008). Analysis of full waveform LIDAR data for the classification of deciduous and coniferous trees. International Journal of Remote Sensing 29(5), 1407–1431, with permission.

In this context, a remote sensing field campaign was conducted on a Dehesa in Southern Spain (Andalucia) in March 2016 (Bareth et al., 2017). One of the objectives was the acquisition of overlapping RGB images in super-high resolution (< 5 cm) for SfM analysis. Therefore, for a selected area of the Dehesa, approximately 150 ha were covered by a UAS campaign with a spatial resolution of approximately 1.7 cm (Fig. 15). Altitude above ground for this campaign was approximately 100 m. Additionally, the complete Dehesa (approximately 700 ha) was covered by a gyrocopter RGB campaign with a spatial resolution of approximately 3 cm for evaluating the applicability of the approach on a more regional scale (see Fig. 15). A gyropcopter is a small manned aerial vehicle which allows low-altitude RS. Altitude above ground for this campaign ranged between 150 and 250 m depending on

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DOP UAV

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Fig. 15 DOPs in super-high spatial resolution acquired with (left) a multirotor UAS and a Sony Alpha 5100 (spatial resolution approximately 1.7 cm) and (right) a gyrocopter with a Nikon D800E (spatial resolution approximately 3 cm). From Bareth, G., Bolten, A., Bongartz, J., Jenal, A., Kneer, C., Lussem, U., Waldhoff, G., Weber, I. (2017). Single tree detection in agro-silvo-pastoral systems from high resolution digital surface models obtained from UAV- and gyrocopter-based RGB-imaging. Zenodo. doi:10.5281/zenodo.375603, with permission.

topography. By comparing the DOPs from both sensing approaches, it is clearly evident that the quality and captured spatial detail is comparable. The white area within the DOP derived from the gyrocopter campaign is a no-data area due to a nonperfect overlapping of the images for the SfM analysis. In Fig. 16 it is clearly visible that the vertical accuracy of the UAV-derived DSM enables identification of single trees and groups of trees visually. The vertical accuracy of the UAV-based DSM is approximately 8 cm. The character of Dehesa-like landscapes also becomes obvious in Fig. 16; Dehesas are silvo-pastoral systems which are characterized by scattered single trees or small numbers of grouped trees and intercanopy areas which are used as pasture. Economically important trees in Dehesas are cork oak and holm oak. In Portugal, such systems are named Montado. The acorns of the holm oak are important as animal feed for the Iberian Pig. Besides the previously mentioned methods for deriving forest parameters using forest canopy analysis, an additional DSM approach is applied for Dehesas, which are savanna-like landscapes to derive tree canopy and intercanopy (pastural) areas. For this purpose,

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Fig. 16 (left) Single trees and groups of up to five trees are already visible in the SfM-created DSM; (right) Single tree and groups of up to five trees extraction using slope. From Bareth, G., Bolten, A., Bongartz, J., Jenal, A., Kneer, C., Lussem, U., Waldhoff, G., Weber, I. (2017). Single tree detection in agro-silvo-pastoral systems from high resolution digital surface models obtained from UAV- and gyrocopter-based RGB-imaging. Zenodo. doi:10.5281/zenodo.375603, with permission.

we used the increase of slope from intercanopy pastures towards the tree crown area and applied a threshold value to automatically identify tree crowns. The results are visualized in Fig. 16. It is shown that for the first test of this approach, > 90% of the tree canopy area was automatically classified with GIS surface analyses based on super-high resolution DSMs derived by low-cost UASs. The potential of such GIS-based morphometric analysis of canopy surfaces offers major opportunities. In particular, the combined analysis of DSMs for structural vegetation parameters with (hyper-) spectral analysis for physiological vegetation parameters opens a new and promising research field for mapping of vegetation (Aasen et al., 2015).

2.01.6

Conclusion and Outlook

The application of GIS for mapping of vegetation is manifold. From supporting data acquisition on field surveys to advances in spatial analysis for species extraction, GIS technologies are almost omnipresent in vegetation mapping and the same is true for remote sensing technologies. The use of GIS for vegetation-related research was a key focus from the very beginning of GIS developments in the early 1960s. Actually, the “father of GIS,” Dr. Roger F. Tomlinson, developed a land information system for the Canada Land Survey which is considered to be the first implemented and conceptualized GIS ever including vegetation, and land use cover, for spatial decision making (Rura et al., 2014). Hence, the spatial data handling capabilities of GIS led to applications and spatial data analyses developments in botany, landscape ecology, and geography and are nowadays key tools for mapping of vegetation in all possible forms. The ongoing miniaturization of sensors and mobile computing devices, plus the ongoing multisensor integration in proximal, airborne, and satellite sensing platforms, plus the ongoing significant enhancement of spatial, temporal, and spectral resolution, has led and will lead to an increasing data acquisition of vegetation in any context. In remote sensing, three current developments or initiatives will have a severe and challenging impact on vegetation mapping in the next 5–10 years. The first is ESA’s Copernicus program with the Sentinel satellite family in combination with its open data policy. The new and unique potentials of the Sentinel data are within its spatial resolution from 10 m on, the temporal repetition

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from several days on, and the multisensor data acquisition comprising multispectral, thermal, and C-band data. Multitemporal and multisensoral data analysis is of key importance to investigate phenology and consequently plant vitality. Therefore, the Sentinel data in combination with already operating and planned satellite sensors will improve spaceborne vegetation mapping and sensing on a global scale. The second development is the increasing number of satellite sensors providing very high panchromatic (< 1 m), multispectral (< 2 m), or X-band (< 2 m) resolution with a potential repetition of several days (e.g., WorldView-3/-4, TerraSAR-X). Even so, these data products still require substantial cost investments; for regional and local scale the value of the data for mapping of vegetation has an unexploited potential, especially for tree or forest monitoring and for precision agriculture purposes. Finally, the third development is related to the improvements of low-altitude remote sensing using manned (e.g., gyrocopters) or UASs (e.g., fixed-wing or multirotor UASs). The lately introduced new low-weight multispectral and hyperspectral sensors (e.g., Cubert’s UHD185 or Parrot’s Sequoia) which can be mounted to low-weight UASs (< 5 kg) are very capable and provide for a spatial resolution of < 0.01 m data with an unseen-before information richness. These ongoing and coming remote sensing developments in combination with recent photogrammetric software developments (e.g., Pix4D, Photoscan, SURE) for DSM and 3D point cloud generation will result in an enhanced demand for established and new GIS analysis methods. A prominent example are the GIS-based surface analysis tools (e.g., slope, aspect, morphometry) which are established methods in geomorphology and have been introduced to vegetation mapping only in the last few years. In our opinion the surface analysis of DSMs having a super-high spatial resolution (< 5 cm) and a high temporal resolution (repetition < 10 days) will be one of the key research areas of vegetation mapping in the next few years. Finally, the combination of such structural vegetation parameters with physiological vegetation parameters like chlorophyll content or nutrient status, or spectral properties in general, has also been only a few years in the focus of research and bears a still-unexploited potential. The identification and monitoring of single plant species or plant communities will be enhanced significantly by these approaches. Therefore, GIS technologies for mapping of vegetation will face a prosperous future, because the new technologies, resolutions, and amount of spatial data produced are demanding new GIS solutions.

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Further Reading Reitberger, J., Krzystek, P., Stilla, U., 2008. Analysis of full waveform LIDAR data for the classification of deciduous and coniferous trees. International Journal of Remote Sensing 29 (5), 1407–1431. Wulder, M.A., White, J.C., Goward, S.N., Masek, J.G., Irons, J.R., Herold, M., Cohen, W.B., Loveland, T.R., Woodcock, C.E., 2008. Landsat continuity: issues and opportunities for land cover monitoring. Remote Sensing of Environment 112, 955–969. http://dx.doi.org/10.1016/j.rse.2007.07.004.