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Spatial scale and land use fragmentation in monitoring water processes in the Colombian Andes
Applied Geography 34 (2012) 395e402
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Spatial scale and land use fragmentation in monitoring water processes in the Colombian Andes Mario A. Giraldo* Geography and Anthropology Department, Kennesaw State University, 1000 Chastain rd, Building 22 room 4078, Kennesaw, GA 30144, USA
a b s t r a c t Keywords: Spatial heterogeneity Land use Spatial units Water processes Colombian Andes
Land use spatial heterogeneity limits the applications of point and coarse spatial resolution remote sensing data in the regional representation of environmental processes. In the Colombian central cordillera little research has been conducted at the watershed level to assess the spatial heterogeneity of the landscape and the effects of land use in the spatio-temporal variations of water related processes. This paper evaluates the spatial heterogeneity of a 940 ha site in the Colombian Andes, discussing the feasibility of using point and coarse spatial resolution remote sensing data in producing regional representations of hydrological processes in this landscape. The spatial heterogeneity of vegetation cover and agriculture systems of an agriculture watershed in the mid-range of the central cordillera (1000 e2000 m.a.s.l) is analyzed. Descriptive statistics and Analysis of variance (ANOVA) were used to compare landscape fragmentation within and among three spatial units, 0.5, 1, and 3 km2, equivalents to pixel sizes commonly present in environmental satellite data. The GIS-Remote Sensing analysis showed a heterogeneous landscape rich in agriculture systems with small parcels of monocrops interwoven with six other agriculture systems including different degrees of intercropping and crop association. The results found an average fragment size of 9 ha, equivalent to a 300 m pixel with up to 10% fragments smaller than 0.5 ha (70 m pixel). No one of the seven land uses identified cover more than 50% of any of the three spatial areas under investigation. The results suggest that coarse spatial resolution remote sensing data or single site point data may be unsuitable for water studies for this landscape because different combinations of vegetation cover, agriculture systems, and topography are expected to produce different outcomes of hydrological process. In this case, single site point data or coarse spatial resolution remote sensing data are expected to oversimplify the local environment and produce limited assessments of regional water processes in this landscape. Ó 2012 Elsevier Ltd. All rights reserved.
Introduction In rural areas of the tropics, land use land cover (LULC) change is seen as a dynamic process of transformation from a biologically diverse rich forest covert to a biologically homogeneous landscape of monocrops and grasslands (Hett, Castella, Heinimann, Messerli, & Pfund, 2011; Shulz, Cayuela, Echeverria, Salas, & Rey-Benayas, 2010). These process of transformations has been the consequence of human decisions and actions of local actors motivated by local and global socioeconomic motivations (Mena et al., 2006; Messina, Walsh, Mena, & Delamater, 2006). In the Colombian Andes a long tradition of mountain colonization and agriculture production have developed a mix of agriculture systems interwoven with isolated patches of natural vegetation. More than a century of * Tel.: þ1 678 797 2373. E-mail address: [email protected]. 0143-6228/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.apgeog.2012.01.004
coffee cultivation in the mid-range (1000e2000 m.a.s.l) has brought changes in land use to the region as well as the coffee cultivation technology (Guhl, 2004, p. 18). The latest of these changes consisting on the introduction of high yielding and disease-resistant mono-cropping of coffee varieties promoted by local agencies (Jaramillo, 2006, p. 192). In the mid-range of the Colombian Andes Central Cordillera a long tradition of mountain colonization and deforestation has created a competition between conservation of forest areas for water catchment purposes and human activities, especially agriculture (Garcia & Ramirez, 2002, p. 23). Research in the Colombian mountains (Jaramillo & Chavez, 2000) and in other mountain landscapes (Guswa, Rhodes, & Newell, 2007) shows that changes in LULC affect the regional water cycle (Tong, Sun, Ranatunga, He, & Yang, 2011), altering the dynamics of water process from their original stages under forest cover. Catchments transformed into agriculture and grassland areas affect the water supplied to the stream network and to the local
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human settlements (Colombian Government, 2007, p. 186). In the central Andes of Colombia, little research has been conducted at the sub-basin level to understand the spatio-temporal variations of land use and their effects on environmental processes linked to regional water cycle. Limited number of monitoring ground stations creates the needs to find representative locations that can produce accurate regional assessments. At the same time, the increasing availability of remote sensing data creates the need to find the spatial characteristics for suitable remote sensing datasets in order to assess local conditions of this landscape. Physical variables of the water cycle change behaviors in different spatial areas according to precipitation, vegetation cover, soil, and topography (Casper, Dixon, Steimle, & Hall, 2011; Post & Jones, 2001). In the water balance of a region under similar climate, soil, and topographic conditions, changes in storage, actual evapotranspiration and run off are the results of vegetation dependent processes such as interception, stem, and throughfall (Djokic & Maidment, 2000, p. 213). Consequently, land uses with different vegetation composition are expected to produce spatial changes in the temporal dynamics of a catchment’s water yield ability (Putuhena & Cordery, 2000). In the regional assessment of the water cycle, ground stations are scattered through the landscape to capture the spatial variation of physical variables (Bosch, Sheridan, & Marshall, 2007; Giraldo et al., 2008). The size of the area that each station represents depends on the level of homogeneity of the landscape as it changes with the distance from the station location (Western, Bloschl, & Grayson, 1998). Under high spatial heterogeneity, combined expressions of precipitation, vegetation cover, soil, and topography are expected to change over short distance limiting the area in which data from a ground station is accurate (Giraldo, Madden, Grunstein, & Bosch, 2009). At a specific location, pixels as individual spatial units of the remote sensing data summarize in a single value the local expression of the physical variables of the water cycle (Jackson et al., 2004). Unfortunately the spatial variation of the landscape does not necessarily correspond to the pixel size of most remote sensing data emphasizing the need to design methods to adjust the remote sensing data available at the local conditions under investigation (Kustas et al., 2004). The field of landscape ecology is founded on the recognition of the strong linkage between spatial pattern and ecological processes where processes (repetition) are the product of the relationship between objects that form the landscape structure (Forman & Godron, 1986). Under an ecologic approach, the description of complex systems is accomplished by interpreting the interactions of their fundamental parts or fragments (Hay, Bouchard, & Marceau, 2002). Quantitative measurements of the agriculture landscape derived from LULC change maps (Backhaus & Braum, 1998; Fu, Hu, Chen, Honnay, & Gulinck, 2006) such as patch size, patch density, mean proximity, and largest patch are suggested indicators to determine the transformation of landscape structure and fragmentation (Cayuela, Benayas, & Echeverria, 2006), and can be used as a sustainability measure index for agriculture landscapes (Fu et al., 2006; Ghersa et al., 2002). In this context, the inferential capabilities of Geographic Information Science Remote Sensing (GIS-RS) technology can be used to extract attributes that serve as local and regional indicators, and to produce a quantitative measure of the spatial heterogeneity of the landscape (Nagendra, Munroe, & Southworth, 2004). The aim of this paper is to analyze the spatial heterogeneity of vegetation cover in a section of the Colombian Central Cordillera coffee region and to identify the diversity of agriculture and natural vegetation systems using GIS-RS. The hypothesis is that the landscape conserves a high level of heterogeneity and complexity despite a dynamic land cover change in the area toward mono-
cropping. In this regard, this paper aims to discuss the effects of landscape spatial variation and the potential use of point readings and coarse spatial resolution satellite data in assessing water processes. Methods Study area The study area (Fig. 1) is located in the Chinchina watershed in the municipality of Manizales in the western slope of the Colombian (South America) Central Cordillera approximately 1500 m.a.s.l. At its upper portion (above 3500 m.a.s.l), the watershed borders paramo vegetation in the protected area of the Los Nevados National Park, while its lower portion drains directly into the Cauca river, a large river basin, paralleling the Colombian Andes north-south between its central and western branches. Topography is characterized by steep slopes and deep soils (1 m) of volcanic ash origin with a 30 cm deep organic layer at the surface (Poveda, Jaramillo, Gil, Quiceno, & Mantilla, 2001). The water cycle is regulated by the interactions between local events linked to orographic precipitation and global and regional phenomena such El Niño South Pacific Oscillation (ENSO), and mid Atlantic storms respectively (Jaramillo & Chavez, 2000). Consequently, precipitation has a bimodal distribution caused by the transit of the Inter Tropical Convergence Zone (ITZC) with two defined cycles from February to June and from August to December with peaks of rain in April and October. During the dry periods between ITZC transitions, precipitation events are caused by local conditions influenced by the dynamics of convective and orographic lifting, and the wind from the Pacific Ocean air masses (Guzman & Baldion, 1997). Data and data preprocessing Land use maps were created from November 2004 black and white aerial photographs at a 1:40,000 scale obtained from the Insituto Geografico Agustin Codazzi (IGAC), the national Geography Institute of Colombia. A 2001 Landsat Enhanced Thematic Mapper (ETMþ) image radiometrically and geometrically corrected was obtained from the U.S. Geological Survey (USGS) in standard National Land Archive Processing System Data Format (NDF). This image was used to geometrically correct the aerial photos. Aerial photos were scanned at 600 dots per inch (DPI) using an Epson flatbed Expression 10000 XL scanner (Jensen, 2005). A 1990 1:10,000 scale topographic map also from the IGAC was used as source for ground reference data. The geometrical correction of the images was performed in ERDAS 10. No less than thirty ground control points (GCPs) surrounding the study area were selected to geometrically correct the study area. At least four GCPs were selected as close as possible to each corners of the photo in order to maximize the likelihood of a good match. The images were then warped using a linear transformation, nearest neighborhood intensity interpolation, and a pixel re-sampling size at 1 m (Frazier & Page, 2000). The resulting images have an approximate ground pixel size of 1.7 m (Jensen, 2007, p. 592). Considering the logistical constraints of field work, a 1.75 km by 5.5 km strip of terrain of approximately 940 ha was selected as the study area. A digital elevation model (DEM) with high spatial resolution was not available to correct the aerial photo for elevation displacement, however, to minimize the problems of distortion, the area under study was selected within a small altitudinal (less than 150 m) gradient. Coregistration of the images was followed by on screen digitizing and classification of landscape patches of the study area generating vector files of the landscape fragments (Jensen, 2005).
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397
Fig. 1. Study area location within the departamento (state) of Caldas, Colombia.
The 940 ha area is equivalent to the one covered by a 3 km pixel size of an environmental satellite. Environmental sensors such as the Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS) and NOAA 7 used in assessing variables of the water cycle have spatial resolution between 200 m and 1 km (100 ha). The 940 ha study area was subdivided in 8 sections of about 118 ha and in 32 sections of 29.5 ha equivalent to 1086 m and 530 m pixel size respectively. In this way, the analysis of the spatial heterogeneity of the landscape was conducted at the three spatial levels of 940 ha, 118 ha, and 29.5 ha. Assessment of the accuracy of the image classification was performed by field work during the summer of 2009. During this time the author visited all fragments during a ground-truthing exercise where the land use on each fragment was compared with the one assigned during the image classification. The field work verified the land use information at the fragment level and provided information about the agriculture system dominating each fragment (Fig. 2).
assumptions of normal distribution for the ANOVA and homogeneity of variances were test using Kurtosis and Skewness, and Levenne tests. Where statistical significance was found, a Tukey/ Tamhane post hoc test was performed to detect the groups for which the difference was significance. All statistical analysis were performed. The analysis included summary statistics of the land use and landscape metrics such as fragment size, shape, perimeter, and dominant land use to assess the spatial heterogeneity of the landscape (McGarigal, Cushman, Neel, & Ene, 2002). Perimeter to area ratio is a common metric to measure the variation in geometric shape of the fragments as an indicator of human intervention in the landscape. To produce an entire number rather than a decimal one this project uses the inverse of that ratio (area to perimeter ratio). The land uses were grouped in the classes: Forest, grass, no agriculture, mono-crop, mixed coffee, shade coffee and crops in rows (Table 1). A literature review focused in local studies that addresses the effects of local land uses, particularly coffee production systems in the variation of the variables of the water cycle complemented the analysis.
Statistical analysis Results Data from the GIS system was exported for the statistical analysis used in IBM SPSS 19.0 statistical software package. Descriptive statistics for fragment area such as minimum, maximum and mean area were estimated, as well as, land use frequency, in the way of fragment count per spatial area. One way analysis of variance (ANOVA) was used to compare land use frequency among the 118 ha study areas. In this analysis an F-value is generated and a statistical significance for the difference between and within groups established at the probability level of 0.05, 0.01 or less. The
Analysis at the 940 ha level The results showed a highly fragmented and heterogeneous landscape composed of 197 land use fragments, 70% of them with less than 5 ha, 13% with less than 1 ha and a total average size of 9 ha. The number of fragments by land use ranges between 6% and 32% with mono-crops having the largest count and grass the lowest count. Mono-crops were found to be organized in small parcels and
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Fig. 2. Land use within sampling areas, 940 ha, 118 ha and 29.3 ha.
interwoven with other types of agriculture systems including different degrees of intercropping (mixed crops) and crop association (crops in rows) (Table 2) (Fig. 3). Despite a large number of fragments represented by monocrops, they occupy only 20% of the total area, ranking third after mixed and shade coffee categories that occupy 21.3% and 26% of the area respectively. Shade coffee covers the largest area, while areas under no agriculture, forest, and grass categories combined cover 20%. No agriculture has the lowest cover with only 4% of the total area. These observations confirmed the hypothesis that in the study area, despite the widespread adoption of mono-cropping at the farm level, there is still a high level of heterogeneity at the landscape level. Regarding the variable area to perimeter ratio, values were found in the range between 30 and 45 for most land uses except no agriculture (15.6) suggesting a high level of variation or heterogeneity in the fragments’ shapes. In agricultural landscapes, human intervention is seen as a progression toward geometrization and simplification of the ecosystem structure (Dewan & Yamaguchi, 2009), where human managed areas tend to substitute large areas of natural vegetation (Peterseil et al., 2004). In areas of flat topography, crop production is maximized in large geometric fields with
Table 1 Description of land uses at the study area. Land use
Description
Forest Grass No agriculture
A fragment of native vegetation with a closed tree canopy Area under grass cover. Scattered trees are possible Areas dominate by build-up infrastructure, such as houses and buildings Fragments where coffee, plantain, citrus or bananas are planted as single species A coffee plantation with plant species such as trees, plantains, bananas or citrus scattered randomly throughout the plantation Similar to mixed coffee but in this case the plant species are organized in rows. Two or more species may be present A fragment with two stratus: an open canopy of trees or banana plants and underground coffee plantation
Mono-crop Mixed coffee
Crops in rows Shade coffee
few edges and homogeneous circular or rectangular shapes. The results suggest that the study area is still in an early process of transformation toward a simplified landscape, despite more than hundred years of human intervention. In summary, at the 940 ha analysis level the results indicate that the landscape is a mosaic of mostly agricultural systems, where the four land uses under agricultural cultivation (shade coffee, mixed coffee, mono-crops and row crops), account for 76% of the total fragments and 81% of the total surface area. However it is clear that there is no single dominant land use since none of the eight land uses covers an area, or has a proportion of fragments of at least 50% of the total. The combined effect of high level of fragmentation, interwoven land uses, and heterogeneity in the fragment shapes suggest a high level of landscape complexity. In this manner the use of a single site or a single pixel (3 km) to infer the behavior of the variables of the water cycle has its limitations. Analysis at the 118 ha level For this analysis the 940 ha area was subdivided in eight study areas of approximately 118 ha each. At higher levels of spatial resolution it is expected that individual units decrease their heterogeneity by decreasing the number of individual fragments per land use and by increasing the area that individual land use occupies within a single pixel. Results of this analysis are summarized at the land use level in Table 3 and at the pixel level in Table 4. They show that the process of dividing the area in eight subareas increased the number of fragments in the analysis by 50%. Landscape fragments that where individual units at the 940 ha spatial resolution became part of two or more spatial units at the 118 ha spatial resolution and therefore contributing to the spatial complexity of separate pixels. Similar to what was observed at the 940 ha spatial resolution, mono-crops have the highest number of fragments per pixel (15) but not the highest occupied area per pixel, therefore ranking third after shade coffee and mixed coffee. The four agricultural land uses mono-crop, shade, mixed and rows were the only ones found/illustrated in all eight pixels of the study. This
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399
Table 2 Summary statistics for the spatial analysis of landscape fragments within a 950 ha section. Areas are reported in ha (10,000 m2) and perimeters in meters (m), A:P is area to perimeter ratio. Agriculture system Forest Grass Mixed Mono No Agr Rows Shade Total
Fragments count 18 12 32 64 17 28 26
Min area
Max area
Mean area
Sum area
0.58 1.98 0.61 0.19 0.28 0.50 0.86
11.09 18.85 45.73 11.07 12.98 24.43 30.92
3.35 7.08 6.24 2.94 2.08 4.53 9.35
60.28 84.90 199.66 187.94 35.39 126.79 243.05
197
938
observation indicates a reduction in the level of heterogeneity in some of the pixels that can be used as a criterion in the selection of ground sites to monitor physical processes that present variation between crop and uncultivated areas. When comparing the maximum area that a single land use occupies within a pixel, only shade coffee was observed with values higher than 50% of the total pixel area, becoming the dominant land use of that pixel. Two other land uses, mono-crops and mixed coffee, were between 40% and 50% of a pixel area. In a remote sensing dataset, the dominant land use is expected to produce the strongest signature and therefore the one that will be recorded by the sensor at that pixel location. However, since only one of the land uses dominate at this spatial resolution and even in there, there are more than 40% of the area with high level of heterogeneity, it is feasible to assume that a remote sensing datasets with around 118 ha (w1 km) spatial resolution will be unsuitable to accurately assess physical processes under this landscape conditions. The summary statistics for each pixel at the 118 ha spatial level in Table 4 show six pixels with a relatively similar fragments number, and two pixels with approximately half their number of
% total area 6 9.1 21.3 20.0 3.8 13.5 25.9 100
Sum perimeter 18,019 25,259 49,112 77,139 23,125 29,310 46,783
Min A:P
Mean A:P
18 11 3 2 1 7 18
32.1 34.6 38.1 27.6 15.6 38.6 45.5
268,747
fragments (around 20). However, there were not consistency between number of fragments per pixel and type of land use, since mono-crops have the highest number of fragments in all the pixels except pixel four. The two pixels with half the number of fragments indicate a reduction in the level of complexity in the landscape and a good indicator to consider when establishing ground monitoring stations. However, it is clear that changes in complexity occurs by sectors and not in a general way thus limiting the potential area for which point data can be used. For the comparison among number of fragments within each eight areas, the Levene’s test of Homogeneity of Variance shows a significance value of 0.81 that indicates that the assumption of homogeneity of variances was met. However the ANOVA analysis shows a significance value of 0.99 indicating that no significant differences among the eight spatial units exist in the number of fragments. This analysis suggests a similar level of fragmentation across the study area at the 118 ha and 940 ha spatial resolution. For the comparison of the fragment count between land uses, the assumption of homogeneity of variances was met (test value of 0.247) and an F-value close to 0.0 indicates significant differences among land uses. In this case the
Fig. 3. Three different land uses within the study area.
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Table 3 Summary statistics for land uses at the 118 ha analysis level. Areas are reported in ha (10,000 m2) and perimeters in meters (m) With maximum (Max) and minimum (Min) values per pixel. Land use
Forest Grass Mixed Mono No Agr Rows Shade Total
Number of fragments per pixel 21 26 55 83 27 37 46
Max and Min # of fragments in a pixel
Max and Min area in a pixel
Max and Min % of total pixel area
10 9 10 15 7 7 11
36.1 29.5 55.2 49.7 15.8 35.2 60.4
31.6 25.6 44.4 42.5 13.3 30.5 50.9
0 0 5 6 0 3 1
0 0 7.2 10.2 0 7.1 0.3
0.0 0.0 6.3 8.9 0.0 6.2 0.3
295
post hoc Tukey analysis shows mono-crop to be significantly different from all the land uses except mixed coffee in number of fragments. For the fragments size by land use, the homogeneity of variance test was also met (test value of 0.297) and an ANOVA significance at the 0.99 probability was found. The post hoc Tukey shows significant differences only among forest, shade coffee and noagriculture for this fragment size. All the land uses occupied the highest area in at least one pixel with the exception of noagriculture. However, there is only one pixel where a single land use has more than 50% of the area (which land use dominated, I can’t remember already so it’s worth repeating). Moreover, the results show that the land use with the highest number or fragments has only 25% of the total fragments. The low dominance of a single land use in the total area of the pixel as well as in the total percentage of fragments indicates that the 118 ha spatial area also has a high level of heterogeneity, although with clear reduction in complexity/fragmentation from what was found at the 940 ha level. Comparing the results at the 118 ha spatial level with the observed at the 940 ha scale slight differences in the characteristics of heterogeneity were observed. Although the four agriculture land uses account for 75% of the landscape fragments, a major difference with the 940 ha analysis is that these four land uses were the only ones with a presence in the eight pixels, suggesting a reduction in the level of complexity at this spatial level. The final difference observed between the two spatial units is that at the 118 ha a dominant land use covering between 40% and 50% of the total area was found in three of the pixels. This indicates that a single land use may produce a dominant signature to be recorded by a remote sensor at that pixel location which is the first indication of a threshold for appropriate remote sensing spatial resolution data to be used to monitoring this landscape. Analysis at the 29.3 ha level The frequency distribution of number of fragments within 29.3 ha spatial units shows a normal distribution (Fig. 4a) with an Table 4 Summary statistics for pixels at the 118 ha analysis level. Pixel number 1 2 3 4 5 6 7 8 Total
Total fragments 37 23 40 42 47 41 40 25 295
Highest # fragments
Highest # fragments
Highest % area
Highest % area
Mono Mono Mono Shade Mono Forest and Mono Shade-mono-Mix Mono-Rows
13 9 15 11 15 10 8 7
Shade Rows Mono Shade Mixed Forest Shade Mixed
39 30.5 42.5 38.4 22.2 31.6 50.9 44.4
Fig. 4. Land use fragment frequency and fragment size among 29.3 ha spatial units.
Average and Media value of 13, a Mode of 15 and a standard deviation of 3.8 fragments per unit, where only four spatial areas have less than 10 fragments. The descriptive statistics for the fragment size shows and average, median and mode fragment size of 2.23, 1.14 and 0.2 ha respectively with standard deviation of 3. The total number of fragments was 421 (Fig. 4b). The analysis of variance for the fragments size shows no homogeneity among the variance and no significance difference among the 32 spatial units. The relatively small size of the average fragment corresponding to less than 10% of the spatial unit and the high average in the number of fragments indicates a high level of fragmentation still present at this spatial scale and the limitations in using coarse resolution remote sensing data and point data in the assessment of physical variables in this landscape. Discussion Remote sensing data is considered to be a promising approach to infer variables of the water cycle by providing in a single look regional description of water processes at different temporal and spatial resolutions (McCabe & Wood, 2006). With increased availability of remotely sensed data from satellite and airborne
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platforms and wide distribution of digital data via internet clearinghouses, research efforts have been concentrated on optimizing remote sensing technology for water processes quantification. Under high landscape complexity, remote sensing instruments with coarse spatial resolution summarize in a single pixel mixed expressions of the environmental variables, decreasing their potential for accurate descriptions of the water process at the local scale (Merlin, Chehbouni, Kerr, & Goodrich, 2006). In this research, despite a partial reduction in the level of complexity and heterogeneity of the landscape from the 940 ha to the 118 ha analysis, it is clear that remote sensors in this spatial resolution range (0.5e3 km) are expected to be too coarse to assess physical variables acting in the landscape. The combined effect of high level of fragmentation, interwoven land uses, complex geometric shapes, and heterogeneity in the land use suggest a high level of complexity in the landscape, and therefore limitations to the use of a single site or a single pixel to infer the behavior of the variables of the water cycle. The decrease in the level of complexity and heterogeneity from 940 to 29.5 ha suggest that smaller areas (less than 29 ha) will show a higher level of homogeneity and therefore remote sensing data matching such as spatial resolution will be suitable for analysis. Under these landscape conditions, the four land uses adding the most heterogeneity and complexity are different types of agriculture areas (shade coffee, mixed, mono-crops, and crops in rows) and likely to be relatively similar in their spectral signature. Using higher spatial resolution data will require a detailed ground truthing and rigorous remote sensing image classification to help separate those pixels in the image analysis (Jensen, 2007). Complex landscapes are formed by spatial units or fragments in which combined precipitation, climate, vegetation cover, soil, and terrain are responsible for environmental processes behavior at that particular location. If each of those variables presents uniform characteristics in a given area, point readings taken from that site become good indicators of the underlying process operating in a portion of the landscape (Cosh, Jackson, Bindlish, & Prueger, 2004). Monitoring water variables at the local scale relies on point data provided by ground based stations (Bosch et al., 2007). Under the conditions of the study area, readings collected at the point level are expected to represent only the ecological variables at that site and therefore, unsuitable to create accurate representation at the regional level. Under these landscape conditions, since topography and soil are relatively similar across the landscape, areas that are under the same land use are expected to have similar environmental response to the variables of the water cycle. In this regard, the results suggest that the placement of ground stations should focus on monitoring individual land uses rather than spacing of the stations. The results of previous works summarized in Table 5 (Jaramillo & Chaves, 1999) supports this observation, since coffee plantations under different management system and vegetation cover exhibit great differences in the response of the variables of the water cycle. Since environmental processes are the product of the relationship between landscape fragments that form the structure of the landscape, to create a regional representation of water cycle variables in a complex landscape, the individual behaviors of the land uses should be identified and then integrated in a regional
Table 5 Percentage values for variables of the water cycle for different land uses for a landscape with similar characteristics that the study area (Jaramillo & Chaves, 1999). Land use
Interception
Stemflow throughfall
Infiltration
Run off
Sun coffee Mixed coffee Shaded coffee
46 56 61
54 44 39
48 37 31
6 7 8
401
representation. The results show that pixels do not correspond with ecological units of spatial organization. To capture the spatial complexity of this landscape smaller pixel sizes are required in addition to a very rigorous process of image analysis and ground truthing. As an alternative to the pixel-based image analysis, a classification approach focused on the individual landscape fragments rather than on a grid of pixels may better discriminate individual variations of environmental variables. Under this approach. the remote sensing image is classified according to the spatial units forming the landscape structure (Burnett & Blaschke, 2003; Hay et al., 2002). Conclusion The landscape under study shows a combined effect of high level of fragmentation, interwoven land uses, complex geometric shapes, and heterogeneity suggesting a high level of complexity. This complexity decreased at the smaller spatial units level of analysis. However for the study of physical variables of the water cycle, remote sensing data with a spatial resolution between 0.5 and 3 km, it is unsuitable to discriminate individual landscape behaviors in this landscape. Fine spatial resolution data combined with image analysis that focuses in landscape fragments rather than on pixels is recommended as an alternative to pixel-based image analysis for this landscape. When using ground monitoring stations in this landscape, individual land uses are require to be monitored separately by individual stations and their data integrated later to produce an accurate representation of the physical processes at the regional scale. Acknowledgments Funding for this research was provided by the College of Humanities and Social Science at Kennesaw State University. Many thanks to farmers from the study area that provided logistical support during the field work. The author expresses his gratitude to two anonymous reviewers for their comments and contributions to improve this manuscript and to two external editors that helped in proof reading the final document. References Backhaus, R., & Braun, G. (1998). Integration of remotely sensed and model data to provide the spatial information basis for sustainable land use. Acta Astronautica, 42(9), 541e546. Bosch, D. D., Sheridan, J. M., & Marshall, L. K. (2007). Precipitation, soil moisture, and climate database, Little River Experimental Watershed, Georgia, United States. Water Resources Research, 43(9), W09472. doi:10.1029/2006WR005834. Burnett, C., & Blaschke, T. (2003). A multi scale segmentation object relationship modelling methodology for landscape analysis. Ecological Modelling, 168, 233e249. Casper, A. F., Dixon, B., Steimle, E. T., & Hall, M. L. (2011). Scales of heterogeneity of water quality in rivers: insights from high resolution maps based on integrated geospatial, sensor and ROV technologies. Journal of Applied Geography, 32, 455e464. Cayuela, L., Benayas, J. M., & Echeverria, C. (2006). Clearance and fragmentation of tropical montane forest in the highlands of Chiapas, Mexico (1975e2000). Forest Ecology and Management, 226, 208e218. Colombian Government. (2007). Water report 2007, Office of the Peoples’ advocate. Colombian Government. Cosh, M. H., Jackson, T. J., Bindlish, R., & Prueger, J. H. (2004). Watershed scale temporal and spatial stability of soil moisture and its role in validating satellite estimates. Remote Sensing of Environment, 92(4), 427e435. Dewan, A. M., & Yamaguchi, Y. (2009). Land use and land cover change in Greater Dhaka, Bangladesh: using remote sensing to promote sustainable urbanization. Journal of Applied Geography, 29, 390e401. Djokic, D., & Maidment, D. (2000). Hydrologic and hydraulic modeling support with geographic information systems. Redlands Ca: ESRI Press. Forman, R. T., & Godron, M. (1986). Landscape ecology. New York: Wiley. Frazier, P. S., & Page, K. J. (2000). Water body detection and delineation with Landsat TM data. Photogrammetric Engineering and Remote Sensing, 66, 1461e1467.
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Fu, B. J., Hu, C., Chen, L., Honnay, O., & Gulinck, H. (2006). Evaluating change in agricultural landscape pattern between 1980 and 2000 in the Loess hilly region of Ansai County, China. Agriculture, Ecosystems & Environment, 114(2e4), 387e396. Garcia, J., & Ramirez, J. (2002). Sostenibilidad económica de las pequeñas explotaciones cafeteras Colombianas. Ensayos. Bogota: Federacion Nacional de Cafeteros de Colombia. Ensayos No 18. Ghersa, C. M., Ferraro, D. O., Omacini, M., Martinez-Ghersa, M. A., Perelman, S., Satorre, E. H., et al. (2002). Farm and landscape level variables as indicators of sustainable land-use in the Argentine Inland-Pampa. Agriculture, Ecosystems & Environment, 93(1e3), 279e293. Giraldo, M. A., Bosch, D., Usery, L., & Kvien, C. (2008). Variations of soil moisture point readings in small fields and different land uses in a complex landscape in south Georgia US. Journal of Hydrology, 357, 405e420. Giraldo, M. A., Madden, M., Grunstein, A., & Bosch, D. (2009). Land use/land cover and soil type covariation in a heterogeneous landscape for soil moisture studies using point data. Journal of GIS Science and Remote Sensing, 46, 1e24. Guhl, A. (2004). Coffee and landcover changes in the Colombian coffee region landscape 1970e1997. Bogota: Federacion Nacional de Cafeteros de Colombia. Ensayos No 20. Guswa, A. J., Rhodes, A. L., & Newell, S. E. (2007). Importance of orographic precipitation to the water resources of Monteverde, Costa Rica. Advances in Water Resources, 30(10), 2098e2112. Guzman, O., & Baldion, R. (1997). The pacific warm event and the Colombian coffee region. Cenicafe, 48(3), 141e155. Hay, G. D., Bouchard, A., & Marceau, D. (2002). A scale-space primer for exploring and quantifying complex landscapes. Ecological Modeling, 153, 27e49. Hett, C., Castella, J.-C., Heinimann, A., Messerli, P., & Pfund, J.-L. (2011). A landscape mosaics approach for characterizing swidden systems from a REDDþ perspective. Journal of Applied Geography, 32, 608e618. Jackson, T., Chen, D., Cosh, M., Li, F., Anderson, M., Walthall, C., et al. (2004). Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sensing of Environment, 92(4), 475e482. Jaramillo, A. (2006). Andean climate and coffee in Colombia. Chinchina: National Center for Coffee Research. Jaramillo, R. A., & Chaves, C. A. (1999). Hydrological characteristics of forest and sun and shade coffee plantations. Cenicafé, 50(2), 5e13. Jaramillo, A., & Chavez, B. (2000). Distribution of Colombian precipitation analyzed by statistic clusters. Cenicafe, 51(2), 101e113. Jensen, J. R. (2005). Digital image processing: A remote sensing perspective (3rd ed.). Prentice Hall. Jensen, J. R. (2007). Remote sensing of the environment: An Earth resource perspective (2nd ed.). Upper Saddle River, NJ: Prentice Hall.
Kustas, W., Li, F., Jackson, T., Prueger, J., MacPherson, J., & Wolde, M. (2004). Effects of remote sensing pixel resolution on modeled energy flux variability of croplands in Iowa. Remote Sensing of Environment, 92(4), 535e547. McCabe, M., & Wood, E. (2006). Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors. Remote Sensing of Environment, 105(4), 271e285. McGarigal, K., Cushman, S. A., Neel, M. C., & Ene, E. (2002). FRAGSTATS spatial pattern analysis program for categorical maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at www. umass.edu/landeco/research/fragstats/fragstats.html. Mena, C. F., Barbieri, A. F., Walsh, S. J., Erlien, C. M., Holt, F. L., & Bilsborrow, R. E. (2006). Pressure on the Cuyabeno Wildlife Reserve: development and land use/ cover change in the Northern Ecuadorian Amazon. World Development, 34(10), 1831e1849. Merlin, O., Chehbouni, A., Kerr, Y. H., & Goodrich, D. C. (2006). A downscaling method for distributing surface soil moisture within a microwave pixel: application to the Monsoon’90 data. Remote Sensing of Environment, 101(3), 379e389. Messina, J. P., Walsh, S. J., Mena, C. F., & Delamater, P. L. (2006). Land tenure and deforestation patterns in the Ecuadorian Amazon: conflicts in land conservation in frontier settings. Applied Geography, 26(2), 113e128. Nagendra, H., Munroe, D., & Southworth, J. (2004). From pattern to process landscape fragmentation and the analysis of land use/land cover change. Agriculture, Ecosystems and the Environment, 101, 111e115. Peterseil, J., Wrbka, T., Plutzar, C., Schmitzberger, I., Kiss, A., Szerencsits, E., et al. (2004). Evaluating the ecological sustainability of Austrian agricultural landscapesethe SINUS approach. Land Use Policy, 21(3), 307e320. Post, D., & Jones, J. A. (2001). Hydrologic regimes of forested mountainous headwater basins in New Hampshire, North Carolina, Oregon, and Puerto Rico. Advances in Wáter Resources, 24, 1195e1210. Poveda, G., Jaramillo, A., Gil, M., Quiceno, M., & Mantilla, R. (2001). Seasonality in ENSO related precipitation, river discharges, soil moisture and vegetation index in Colombia. Water Resources Research, 37(8), 2169e2178. Putuhena, W. M., & Cordery, I. (2000). Some hydrological effects of changing forest cover from eucalypts to Pinus radiata. Agricultural and Forest Meteorology, 100, 59e72. Schulz, J. J., Cayuela, L., Echeverria, C., Salas, J., & Rey-Benayas, J. M. (2010). Monitoring land cover change of the dryland forest landscape of Central Chile (1975e2008). Journal of Applied Geography, 30, 436e447. Tong, S., Sun, Y., Ranatunga, T., He, J., & Yang, J.-Y. (2011). Predicting plausible impacts of sets of climate and land use change scenarios on water resources. Journal of Applied Geography, 32, 477e489. Western, A., Bloschl, G., & Grayson, R. (1998). Geostatistical characterization of soil moisture patterns in the tarrawarra catchment. Journal of Hydrology, 205, 20e37.
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Spatial scale and land use fragmentation in monitoring water processes in the Colombian Andes Mario A. Giraldo* Geography and Anthropology Department, Kennesaw State University, 1000 Chastain rd, Building 22 room 4078, Kennesaw, GA 30144, USA
a b s t r a c t Keywords: Spatial heterogeneity Land use Spatial units Water processes Colombian Andes
Land use spatial heterogeneity limits the applications of point and coarse spatial resolution remote sensing data in the regional representation of environmental processes. In the Colombian central cordillera little research has been conducted at the watershed level to assess the spatial heterogeneity of the landscape and the effects of land use in the spatio-temporal variations of water related processes. This paper evaluates the spatial heterogeneity of a 940 ha site in the Colombian Andes, discussing the feasibility of using point and coarse spatial resolution remote sensing data in producing regional representations of hydrological processes in this landscape. The spatial heterogeneity of vegetation cover and agriculture systems of an agriculture watershed in the mid-range of the central cordillera (1000 e2000 m.a.s.l) is analyzed. Descriptive statistics and Analysis of variance (ANOVA) were used to compare landscape fragmentation within and among three spatial units, 0.5, 1, and 3 km2, equivalents to pixel sizes commonly present in environmental satellite data. The GIS-Remote Sensing analysis showed a heterogeneous landscape rich in agriculture systems with small parcels of monocrops interwoven with six other agriculture systems including different degrees of intercropping and crop association. The results found an average fragment size of 9 ha, equivalent to a 300 m pixel with up to 10% fragments smaller than 0.5 ha (70 m pixel). No one of the seven land uses identified cover more than 50% of any of the three spatial areas under investigation. The results suggest that coarse spatial resolution remote sensing data or single site point data may be unsuitable for water studies for this landscape because different combinations of vegetation cover, agriculture systems, and topography are expected to produce different outcomes of hydrological process. In this case, single site point data or coarse spatial resolution remote sensing data are expected to oversimplify the local environment and produce limited assessments of regional water processes in this landscape. Ó 2012 Elsevier Ltd. All rights reserved.
Introduction In rural areas of the tropics, land use land cover (LULC) change is seen as a dynamic process of transformation from a biologically diverse rich forest covert to a biologically homogeneous landscape of monocrops and grasslands (Hett, Castella, Heinimann, Messerli, & Pfund, 2011; Shulz, Cayuela, Echeverria, Salas, & Rey-Benayas, 2010). These process of transformations has been the consequence of human decisions and actions of local actors motivated by local and global socioeconomic motivations (Mena et al., 2006; Messina, Walsh, Mena, & Delamater, 2006). In the Colombian Andes a long tradition of mountain colonization and agriculture production have developed a mix of agriculture systems interwoven with isolated patches of natural vegetation. More than a century of * Tel.: þ1 678 797 2373. E-mail address: [email protected]. 0143-6228/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.apgeog.2012.01.004
coffee cultivation in the mid-range (1000e2000 m.a.s.l) has brought changes in land use to the region as well as the coffee cultivation technology (Guhl, 2004, p. 18). The latest of these changes consisting on the introduction of high yielding and disease-resistant mono-cropping of coffee varieties promoted by local agencies (Jaramillo, 2006, p. 192). In the mid-range of the Colombian Andes Central Cordillera a long tradition of mountain colonization and deforestation has created a competition between conservation of forest areas for water catchment purposes and human activities, especially agriculture (Garcia & Ramirez, 2002, p. 23). Research in the Colombian mountains (Jaramillo & Chavez, 2000) and in other mountain landscapes (Guswa, Rhodes, & Newell, 2007) shows that changes in LULC affect the regional water cycle (Tong, Sun, Ranatunga, He, & Yang, 2011), altering the dynamics of water process from their original stages under forest cover. Catchments transformed into agriculture and grassland areas affect the water supplied to the stream network and to the local
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human settlements (Colombian Government, 2007, p. 186). In the central Andes of Colombia, little research has been conducted at the sub-basin level to understand the spatio-temporal variations of land use and their effects on environmental processes linked to regional water cycle. Limited number of monitoring ground stations creates the needs to find representative locations that can produce accurate regional assessments. At the same time, the increasing availability of remote sensing data creates the need to find the spatial characteristics for suitable remote sensing datasets in order to assess local conditions of this landscape. Physical variables of the water cycle change behaviors in different spatial areas according to precipitation, vegetation cover, soil, and topography (Casper, Dixon, Steimle, & Hall, 2011; Post & Jones, 2001). In the water balance of a region under similar climate, soil, and topographic conditions, changes in storage, actual evapotranspiration and run off are the results of vegetation dependent processes such as interception, stem, and throughfall (Djokic & Maidment, 2000, p. 213). Consequently, land uses with different vegetation composition are expected to produce spatial changes in the temporal dynamics of a catchment’s water yield ability (Putuhena & Cordery, 2000). In the regional assessment of the water cycle, ground stations are scattered through the landscape to capture the spatial variation of physical variables (Bosch, Sheridan, & Marshall, 2007; Giraldo et al., 2008). The size of the area that each station represents depends on the level of homogeneity of the landscape as it changes with the distance from the station location (Western, Bloschl, & Grayson, 1998). Under high spatial heterogeneity, combined expressions of precipitation, vegetation cover, soil, and topography are expected to change over short distance limiting the area in which data from a ground station is accurate (Giraldo, Madden, Grunstein, & Bosch, 2009). At a specific location, pixels as individual spatial units of the remote sensing data summarize in a single value the local expression of the physical variables of the water cycle (Jackson et al., 2004). Unfortunately the spatial variation of the landscape does not necessarily correspond to the pixel size of most remote sensing data emphasizing the need to design methods to adjust the remote sensing data available at the local conditions under investigation (Kustas et al., 2004). The field of landscape ecology is founded on the recognition of the strong linkage between spatial pattern and ecological processes where processes (repetition) are the product of the relationship between objects that form the landscape structure (Forman & Godron, 1986). Under an ecologic approach, the description of complex systems is accomplished by interpreting the interactions of their fundamental parts or fragments (Hay, Bouchard, & Marceau, 2002). Quantitative measurements of the agriculture landscape derived from LULC change maps (Backhaus & Braum, 1998; Fu, Hu, Chen, Honnay, & Gulinck, 2006) such as patch size, patch density, mean proximity, and largest patch are suggested indicators to determine the transformation of landscape structure and fragmentation (Cayuela, Benayas, & Echeverria, 2006), and can be used as a sustainability measure index for agriculture landscapes (Fu et al., 2006; Ghersa et al., 2002). In this context, the inferential capabilities of Geographic Information Science Remote Sensing (GIS-RS) technology can be used to extract attributes that serve as local and regional indicators, and to produce a quantitative measure of the spatial heterogeneity of the landscape (Nagendra, Munroe, & Southworth, 2004). The aim of this paper is to analyze the spatial heterogeneity of vegetation cover in a section of the Colombian Central Cordillera coffee region and to identify the diversity of agriculture and natural vegetation systems using GIS-RS. The hypothesis is that the landscape conserves a high level of heterogeneity and complexity despite a dynamic land cover change in the area toward mono-
cropping. In this regard, this paper aims to discuss the effects of landscape spatial variation and the potential use of point readings and coarse spatial resolution satellite data in assessing water processes. Methods Study area The study area (Fig. 1) is located in the Chinchina watershed in the municipality of Manizales in the western slope of the Colombian (South America) Central Cordillera approximately 1500 m.a.s.l. At its upper portion (above 3500 m.a.s.l), the watershed borders paramo vegetation in the protected area of the Los Nevados National Park, while its lower portion drains directly into the Cauca river, a large river basin, paralleling the Colombian Andes north-south between its central and western branches. Topography is characterized by steep slopes and deep soils (1 m) of volcanic ash origin with a 30 cm deep organic layer at the surface (Poveda, Jaramillo, Gil, Quiceno, & Mantilla, 2001). The water cycle is regulated by the interactions between local events linked to orographic precipitation and global and regional phenomena such El Niño South Pacific Oscillation (ENSO), and mid Atlantic storms respectively (Jaramillo & Chavez, 2000). Consequently, precipitation has a bimodal distribution caused by the transit of the Inter Tropical Convergence Zone (ITZC) with two defined cycles from February to June and from August to December with peaks of rain in April and October. During the dry periods between ITZC transitions, precipitation events are caused by local conditions influenced by the dynamics of convective and orographic lifting, and the wind from the Pacific Ocean air masses (Guzman & Baldion, 1997). Data and data preprocessing Land use maps were created from November 2004 black and white aerial photographs at a 1:40,000 scale obtained from the Insituto Geografico Agustin Codazzi (IGAC), the national Geography Institute of Colombia. A 2001 Landsat Enhanced Thematic Mapper (ETMþ) image radiometrically and geometrically corrected was obtained from the U.S. Geological Survey (USGS) in standard National Land Archive Processing System Data Format (NDF). This image was used to geometrically correct the aerial photos. Aerial photos were scanned at 600 dots per inch (DPI) using an Epson flatbed Expression 10000 XL scanner (Jensen, 2005). A 1990 1:10,000 scale topographic map also from the IGAC was used as source for ground reference data. The geometrical correction of the images was performed in ERDAS 10. No less than thirty ground control points (GCPs) surrounding the study area were selected to geometrically correct the study area. At least four GCPs were selected as close as possible to each corners of the photo in order to maximize the likelihood of a good match. The images were then warped using a linear transformation, nearest neighborhood intensity interpolation, and a pixel re-sampling size at 1 m (Frazier & Page, 2000). The resulting images have an approximate ground pixel size of 1.7 m (Jensen, 2007, p. 592). Considering the logistical constraints of field work, a 1.75 km by 5.5 km strip of terrain of approximately 940 ha was selected as the study area. A digital elevation model (DEM) with high spatial resolution was not available to correct the aerial photo for elevation displacement, however, to minimize the problems of distortion, the area under study was selected within a small altitudinal (less than 150 m) gradient. Coregistration of the images was followed by on screen digitizing and classification of landscape patches of the study area generating vector files of the landscape fragments (Jensen, 2005).
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397
Fig. 1. Study area location within the departamento (state) of Caldas, Colombia.
The 940 ha area is equivalent to the one covered by a 3 km pixel size of an environmental satellite. Environmental sensors such as the Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS) and NOAA 7 used in assessing variables of the water cycle have spatial resolution between 200 m and 1 km (100 ha). The 940 ha study area was subdivided in 8 sections of about 118 ha and in 32 sections of 29.5 ha equivalent to 1086 m and 530 m pixel size respectively. In this way, the analysis of the spatial heterogeneity of the landscape was conducted at the three spatial levels of 940 ha, 118 ha, and 29.5 ha. Assessment of the accuracy of the image classification was performed by field work during the summer of 2009. During this time the author visited all fragments during a ground-truthing exercise where the land use on each fragment was compared with the one assigned during the image classification. The field work verified the land use information at the fragment level and provided information about the agriculture system dominating each fragment (Fig. 2).
assumptions of normal distribution for the ANOVA and homogeneity of variances were test using Kurtosis and Skewness, and Levenne tests. Where statistical significance was found, a Tukey/ Tamhane post hoc test was performed to detect the groups for which the difference was significance. All statistical analysis were performed. The analysis included summary statistics of the land use and landscape metrics such as fragment size, shape, perimeter, and dominant land use to assess the spatial heterogeneity of the landscape (McGarigal, Cushman, Neel, & Ene, 2002). Perimeter to area ratio is a common metric to measure the variation in geometric shape of the fragments as an indicator of human intervention in the landscape. To produce an entire number rather than a decimal one this project uses the inverse of that ratio (area to perimeter ratio). The land uses were grouped in the classes: Forest, grass, no agriculture, mono-crop, mixed coffee, shade coffee and crops in rows (Table 1). A literature review focused in local studies that addresses the effects of local land uses, particularly coffee production systems in the variation of the variables of the water cycle complemented the analysis.
Statistical analysis Results Data from the GIS system was exported for the statistical analysis used in IBM SPSS 19.0 statistical software package. Descriptive statistics for fragment area such as minimum, maximum and mean area were estimated, as well as, land use frequency, in the way of fragment count per spatial area. One way analysis of variance (ANOVA) was used to compare land use frequency among the 118 ha study areas. In this analysis an F-value is generated and a statistical significance for the difference between and within groups established at the probability level of 0.05, 0.01 or less. The
Analysis at the 940 ha level The results showed a highly fragmented and heterogeneous landscape composed of 197 land use fragments, 70% of them with less than 5 ha, 13% with less than 1 ha and a total average size of 9 ha. The number of fragments by land use ranges between 6% and 32% with mono-crops having the largest count and grass the lowest count. Mono-crops were found to be organized in small parcels and
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Fig. 2. Land use within sampling areas, 940 ha, 118 ha and 29.3 ha.
interwoven with other types of agriculture systems including different degrees of intercropping (mixed crops) and crop association (crops in rows) (Table 2) (Fig. 3). Despite a large number of fragments represented by monocrops, they occupy only 20% of the total area, ranking third after mixed and shade coffee categories that occupy 21.3% and 26% of the area respectively. Shade coffee covers the largest area, while areas under no agriculture, forest, and grass categories combined cover 20%. No agriculture has the lowest cover with only 4% of the total area. These observations confirmed the hypothesis that in the study area, despite the widespread adoption of mono-cropping at the farm level, there is still a high level of heterogeneity at the landscape level. Regarding the variable area to perimeter ratio, values were found in the range between 30 and 45 for most land uses except no agriculture (15.6) suggesting a high level of variation or heterogeneity in the fragments’ shapes. In agricultural landscapes, human intervention is seen as a progression toward geometrization and simplification of the ecosystem structure (Dewan & Yamaguchi, 2009), where human managed areas tend to substitute large areas of natural vegetation (Peterseil et al., 2004). In areas of flat topography, crop production is maximized in large geometric fields with
Table 1 Description of land uses at the study area. Land use
Description
Forest Grass No agriculture
A fragment of native vegetation with a closed tree canopy Area under grass cover. Scattered trees are possible Areas dominate by build-up infrastructure, such as houses and buildings Fragments where coffee, plantain, citrus or bananas are planted as single species A coffee plantation with plant species such as trees, plantains, bananas or citrus scattered randomly throughout the plantation Similar to mixed coffee but in this case the plant species are organized in rows. Two or more species may be present A fragment with two stratus: an open canopy of trees or banana plants and underground coffee plantation
Mono-crop Mixed coffee
Crops in rows Shade coffee
few edges and homogeneous circular or rectangular shapes. The results suggest that the study area is still in an early process of transformation toward a simplified landscape, despite more than hundred years of human intervention. In summary, at the 940 ha analysis level the results indicate that the landscape is a mosaic of mostly agricultural systems, where the four land uses under agricultural cultivation (shade coffee, mixed coffee, mono-crops and row crops), account for 76% of the total fragments and 81% of the total surface area. However it is clear that there is no single dominant land use since none of the eight land uses covers an area, or has a proportion of fragments of at least 50% of the total. The combined effect of high level of fragmentation, interwoven land uses, and heterogeneity in the fragment shapes suggest a high level of landscape complexity. In this manner the use of a single site or a single pixel (3 km) to infer the behavior of the variables of the water cycle has its limitations. Analysis at the 118 ha level For this analysis the 940 ha area was subdivided in eight study areas of approximately 118 ha each. At higher levels of spatial resolution it is expected that individual units decrease their heterogeneity by decreasing the number of individual fragments per land use and by increasing the area that individual land use occupies within a single pixel. Results of this analysis are summarized at the land use level in Table 3 and at the pixel level in Table 4. They show that the process of dividing the area in eight subareas increased the number of fragments in the analysis by 50%. Landscape fragments that where individual units at the 940 ha spatial resolution became part of two or more spatial units at the 118 ha spatial resolution and therefore contributing to the spatial complexity of separate pixels. Similar to what was observed at the 940 ha spatial resolution, mono-crops have the highest number of fragments per pixel (15) but not the highest occupied area per pixel, therefore ranking third after shade coffee and mixed coffee. The four agricultural land uses mono-crop, shade, mixed and rows were the only ones found/illustrated in all eight pixels of the study. This
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399
Table 2 Summary statistics for the spatial analysis of landscape fragments within a 950 ha section. Areas are reported in ha (10,000 m2) and perimeters in meters (m), A:P is area to perimeter ratio. Agriculture system Forest Grass Mixed Mono No Agr Rows Shade Total
Fragments count 18 12 32 64 17 28 26
Min area
Max area
Mean area
Sum area
0.58 1.98 0.61 0.19 0.28 0.50 0.86
11.09 18.85 45.73 11.07 12.98 24.43 30.92
3.35 7.08 6.24 2.94 2.08 4.53 9.35
60.28 84.90 199.66 187.94 35.39 126.79 243.05
197
938
observation indicates a reduction in the level of heterogeneity in some of the pixels that can be used as a criterion in the selection of ground sites to monitor physical processes that present variation between crop and uncultivated areas. When comparing the maximum area that a single land use occupies within a pixel, only shade coffee was observed with values higher than 50% of the total pixel area, becoming the dominant land use of that pixel. Two other land uses, mono-crops and mixed coffee, were between 40% and 50% of a pixel area. In a remote sensing dataset, the dominant land use is expected to produce the strongest signature and therefore the one that will be recorded by the sensor at that pixel location. However, since only one of the land uses dominate at this spatial resolution and even in there, there are more than 40% of the area with high level of heterogeneity, it is feasible to assume that a remote sensing datasets with around 118 ha (w1 km) spatial resolution will be unsuitable to accurately assess physical processes under this landscape conditions. The summary statistics for each pixel at the 118 ha spatial level in Table 4 show six pixels with a relatively similar fragments number, and two pixels with approximately half their number of
% total area 6 9.1 21.3 20.0 3.8 13.5 25.9 100
Sum perimeter 18,019 25,259 49,112 77,139 23,125 29,310 46,783
Min A:P
Mean A:P
18 11 3 2 1 7 18
32.1 34.6 38.1 27.6 15.6 38.6 45.5
268,747
fragments (around 20). However, there were not consistency between number of fragments per pixel and type of land use, since mono-crops have the highest number of fragments in all the pixels except pixel four. The two pixels with half the number of fragments indicate a reduction in the level of complexity in the landscape and a good indicator to consider when establishing ground monitoring stations. However, it is clear that changes in complexity occurs by sectors and not in a general way thus limiting the potential area for which point data can be used. For the comparison among number of fragments within each eight areas, the Levene’s test of Homogeneity of Variance shows a significance value of 0.81 that indicates that the assumption of homogeneity of variances was met. However the ANOVA analysis shows a significance value of 0.99 indicating that no significant differences among the eight spatial units exist in the number of fragments. This analysis suggests a similar level of fragmentation across the study area at the 118 ha and 940 ha spatial resolution. For the comparison of the fragment count between land uses, the assumption of homogeneity of variances was met (test value of 0.247) and an F-value close to 0.0 indicates significant differences among land uses. In this case the
Fig. 3. Three different land uses within the study area.
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Table 3 Summary statistics for land uses at the 118 ha analysis level. Areas are reported in ha (10,000 m2) and perimeters in meters (m) With maximum (Max) and minimum (Min) values per pixel. Land use
Forest Grass Mixed Mono No Agr Rows Shade Total
Number of fragments per pixel 21 26 55 83 27 37 46
Max and Min # of fragments in a pixel
Max and Min area in a pixel
Max and Min % of total pixel area
10 9 10 15 7 7 11
36.1 29.5 55.2 49.7 15.8 35.2 60.4
31.6 25.6 44.4 42.5 13.3 30.5 50.9
0 0 5 6 0 3 1
0 0 7.2 10.2 0 7.1 0.3
0.0 0.0 6.3 8.9 0.0 6.2 0.3
295
post hoc Tukey analysis shows mono-crop to be significantly different from all the land uses except mixed coffee in number of fragments. For the fragments size by land use, the homogeneity of variance test was also met (test value of 0.297) and an ANOVA significance at the 0.99 probability was found. The post hoc Tukey shows significant differences only among forest, shade coffee and noagriculture for this fragment size. All the land uses occupied the highest area in at least one pixel with the exception of noagriculture. However, there is only one pixel where a single land use has more than 50% of the area (which land use dominated, I can’t remember already so it’s worth repeating). Moreover, the results show that the land use with the highest number or fragments has only 25% of the total fragments. The low dominance of a single land use in the total area of the pixel as well as in the total percentage of fragments indicates that the 118 ha spatial area also has a high level of heterogeneity, although with clear reduction in complexity/fragmentation from what was found at the 940 ha level. Comparing the results at the 118 ha spatial level with the observed at the 940 ha scale slight differences in the characteristics of heterogeneity were observed. Although the four agriculture land uses account for 75% of the landscape fragments, a major difference with the 940 ha analysis is that these four land uses were the only ones with a presence in the eight pixels, suggesting a reduction in the level of complexity at this spatial level. The final difference observed between the two spatial units is that at the 118 ha a dominant land use covering between 40% and 50% of the total area was found in three of the pixels. This indicates that a single land use may produce a dominant signature to be recorded by a remote sensor at that pixel location which is the first indication of a threshold for appropriate remote sensing spatial resolution data to be used to monitoring this landscape. Analysis at the 29.3 ha level The frequency distribution of number of fragments within 29.3 ha spatial units shows a normal distribution (Fig. 4a) with an Table 4 Summary statistics for pixels at the 118 ha analysis level. Pixel number 1 2 3 4 5 6 7 8 Total
Total fragments 37 23 40 42 47 41 40 25 295
Highest # fragments
Highest # fragments
Highest % area
Highest % area
Mono Mono Mono Shade Mono Forest and Mono Shade-mono-Mix Mono-Rows
13 9 15 11 15 10 8 7
Shade Rows Mono Shade Mixed Forest Shade Mixed
39 30.5 42.5 38.4 22.2 31.6 50.9 44.4
Fig. 4. Land use fragment frequency and fragment size among 29.3 ha spatial units.
Average and Media value of 13, a Mode of 15 and a standard deviation of 3.8 fragments per unit, where only four spatial areas have less than 10 fragments. The descriptive statistics for the fragment size shows and average, median and mode fragment size of 2.23, 1.14 and 0.2 ha respectively with standard deviation of 3. The total number of fragments was 421 (Fig. 4b). The analysis of variance for the fragments size shows no homogeneity among the variance and no significance difference among the 32 spatial units. The relatively small size of the average fragment corresponding to less than 10% of the spatial unit and the high average in the number of fragments indicates a high level of fragmentation still present at this spatial scale and the limitations in using coarse resolution remote sensing data and point data in the assessment of physical variables in this landscape. Discussion Remote sensing data is considered to be a promising approach to infer variables of the water cycle by providing in a single look regional description of water processes at different temporal and spatial resolutions (McCabe & Wood, 2006). With increased availability of remotely sensed data from satellite and airborne
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platforms and wide distribution of digital data via internet clearinghouses, research efforts have been concentrated on optimizing remote sensing technology for water processes quantification. Under high landscape complexity, remote sensing instruments with coarse spatial resolution summarize in a single pixel mixed expressions of the environmental variables, decreasing their potential for accurate descriptions of the water process at the local scale (Merlin, Chehbouni, Kerr, & Goodrich, 2006). In this research, despite a partial reduction in the level of complexity and heterogeneity of the landscape from the 940 ha to the 118 ha analysis, it is clear that remote sensors in this spatial resolution range (0.5e3 km) are expected to be too coarse to assess physical variables acting in the landscape. The combined effect of high level of fragmentation, interwoven land uses, complex geometric shapes, and heterogeneity in the land use suggest a high level of complexity in the landscape, and therefore limitations to the use of a single site or a single pixel to infer the behavior of the variables of the water cycle. The decrease in the level of complexity and heterogeneity from 940 to 29.5 ha suggest that smaller areas (less than 29 ha) will show a higher level of homogeneity and therefore remote sensing data matching such as spatial resolution will be suitable for analysis. Under these landscape conditions, the four land uses adding the most heterogeneity and complexity are different types of agriculture areas (shade coffee, mixed, mono-crops, and crops in rows) and likely to be relatively similar in their spectral signature. Using higher spatial resolution data will require a detailed ground truthing and rigorous remote sensing image classification to help separate those pixels in the image analysis (Jensen, 2007). Complex landscapes are formed by spatial units or fragments in which combined precipitation, climate, vegetation cover, soil, and terrain are responsible for environmental processes behavior at that particular location. If each of those variables presents uniform characteristics in a given area, point readings taken from that site become good indicators of the underlying process operating in a portion of the landscape (Cosh, Jackson, Bindlish, & Prueger, 2004). Monitoring water variables at the local scale relies on point data provided by ground based stations (Bosch et al., 2007). Under the conditions of the study area, readings collected at the point level are expected to represent only the ecological variables at that site and therefore, unsuitable to create accurate representation at the regional level. Under these landscape conditions, since topography and soil are relatively similar across the landscape, areas that are under the same land use are expected to have similar environmental response to the variables of the water cycle. In this regard, the results suggest that the placement of ground stations should focus on monitoring individual land uses rather than spacing of the stations. The results of previous works summarized in Table 5 (Jaramillo & Chaves, 1999) supports this observation, since coffee plantations under different management system and vegetation cover exhibit great differences in the response of the variables of the water cycle. Since environmental processes are the product of the relationship between landscape fragments that form the structure of the landscape, to create a regional representation of water cycle variables in a complex landscape, the individual behaviors of the land uses should be identified and then integrated in a regional
Table 5 Percentage values for variables of the water cycle for different land uses for a landscape with similar characteristics that the study area (Jaramillo & Chaves, 1999). Land use
Interception
Stemflow throughfall
Infiltration
Run off
Sun coffee Mixed coffee Shaded coffee
46 56 61
54 44 39
48 37 31
6 7 8
401
representation. The results show that pixels do not correspond with ecological units of spatial organization. To capture the spatial complexity of this landscape smaller pixel sizes are required in addition to a very rigorous process of image analysis and ground truthing. As an alternative to the pixel-based image analysis, a classification approach focused on the individual landscape fragments rather than on a grid of pixels may better discriminate individual variations of environmental variables. Under this approach. the remote sensing image is classified according to the spatial units forming the landscape structure (Burnett & Blaschke, 2003; Hay et al., 2002). Conclusion The landscape under study shows a combined effect of high level of fragmentation, interwoven land uses, complex geometric shapes, and heterogeneity suggesting a high level of complexity. This complexity decreased at the smaller spatial units level of analysis. However for the study of physical variables of the water cycle, remote sensing data with a spatial resolution between 0.5 and 3 km, it is unsuitable to discriminate individual landscape behaviors in this landscape. Fine spatial resolution data combined with image analysis that focuses in landscape fragments rather than on pixels is recommended as an alternative to pixel-based image analysis for this landscape. When using ground monitoring stations in this landscape, individual land uses are require to be monitored separately by individual stations and their data integrated later to produce an accurate representation of the physical processes at the regional scale. Acknowledgments Funding for this research was provided by the College of Humanities and Social Science at Kennesaw State University. Many thanks to farmers from the study area that provided logistical support during the field work. The author expresses his gratitude to two anonymous reviewers for their comments and contributions to improve this manuscript and to two external editors that helped in proof reading the final document. References Backhaus, R., & Braun, G. (1998). Integration of remotely sensed and model data to provide the spatial information basis for sustainable land use. Acta Astronautica, 42(9), 541e546. Bosch, D. D., Sheridan, J. M., & Marshall, L. K. (2007). Precipitation, soil moisture, and climate database, Little River Experimental Watershed, Georgia, United States. Water Resources Research, 43(9), W09472. doi:10.1029/2006WR005834. Burnett, C., & Blaschke, T. (2003). A multi scale segmentation object relationship modelling methodology for landscape analysis. Ecological Modelling, 168, 233e249. Casper, A. F., Dixon, B., Steimle, E. T., & Hall, M. L. (2011). Scales of heterogeneity of water quality in rivers: insights from high resolution maps based on integrated geospatial, sensor and ROV technologies. Journal of Applied Geography, 32, 455e464. Cayuela, L., Benayas, J. M., & Echeverria, C. (2006). Clearance and fragmentation of tropical montane forest in the highlands of Chiapas, Mexico (1975e2000). Forest Ecology and Management, 226, 208e218. Colombian Government. (2007). Water report 2007, Office of the Peoples’ advocate. Colombian Government. Cosh, M. H., Jackson, T. J., Bindlish, R., & Prueger, J. H. (2004). Watershed scale temporal and spatial stability of soil moisture and its role in validating satellite estimates. Remote Sensing of Environment, 92(4), 427e435. Dewan, A. M., & Yamaguchi, Y. (2009). Land use and land cover change in Greater Dhaka, Bangladesh: using remote sensing to promote sustainable urbanization. Journal of Applied Geography, 29, 390e401. Djokic, D., & Maidment, D. (2000). Hydrologic and hydraulic modeling support with geographic information systems. Redlands Ca: ESRI Press. Forman, R. T., & Godron, M. (1986). Landscape ecology. New York: Wiley. Frazier, P. S., & Page, K. J. (2000). Water body detection and delineation with Landsat TM data. Photogrammetric Engineering and Remote Sensing, 66, 1461e1467.
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