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Gross primary productivity in areas of different land cover in the western Brazilian Amazon
Remote Sensing Applications: Society and Environment 16 (2019) 100259
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Gross primary productivity in areas of different land cover in the western Brazilian Amazon Mariana C. Chagas a, Rafael C. Delgado a, Leonardo P. de Souza b, Daniel C. de Carvalho a, Marcos G. Pereira c, Paulo E. Teodoro d, Carlos A. Silva Junior e, * a
Department of Environmental Sciences, Forest Institute, Federal Rural University of Rio de Janeiro (UFRRJ), 23897-000, Serop�edica, Rio de Janeiro, Brazil Center for Biological and Natural Sciences, Federal University of Acre (UFAC), 69920-900, Rio Branco, Acre, Brazil Department of Soils of the Federal Rural University of Rio de Janeiro (UFRRJ), 23897-000, Serop�edica, Rio de Janeiro, Brazil d Department of Crop Science, Federal University of Mato Grosso do Sul (UFMS), 79560000, Chapad~ ao do Sul, Mato Grosso do Sul, Brazil e Department of Geography, State University of Mato Grosso (UNEMAT), 78555-000, Sinop, Mato Grosso, Brazil b c
A R T I C L E I N F O
A B S T R A C T
Keywords: Carbon in forests Orbital sensors Climate change Algorithms
Uncertainty regarding gross carbon sequestration at local, regional, and global scales can be reduced by moni toring the land surface processes at high spatial and temporal resolutions. In this sense, the objective of this study was to estimate gross primary productivity (GPP) in a region of the western Brazilian Amazon using Landsat 8 OLI/TIRS images, and to evaluate possible changes in estimated productivity among areas of different land use and in different seasonal conditions (August 04, 2013, representing the dry season, and October 10, 2014, representing the rainy season). The images were subjected to atmospheric and radiometric corrections. As the basic input for the model, the balance of radiation and other components of the energy balance were calculated using the Surface Energy Balance Algorithm for Land. The GPP estimated through the OLI/TIRS sensor was compared with the MOD17A2 product of the MODIS sensor. The comparison of GPP by the OLI/TIRS sensors and the MOD17A2H product were based on the mean error (ME), simple linear regression (r2) and correlation co efficient (r). The estimated GPP indicated distinctions among land use types, however, similarities with MOD17A2H were detected only with the image from the rainy season (r ¼ 0.53), with a slight underestimation for all land uses. The dry season image showed r2 ¼ 0.11, r ¼ 0.33 and ME ¼ 0.48 gCm 2day 1 for extractivism, while the land use types were overestimated by the model. The OLI/TIRS sensor estimates need to be validated with data from flow towers. The focus on carbon sequestration by forest ecosystems and the reduction of CO2 emissions is the foundation for mitigating climate change damage and consequences at regional and global levels. Currently deforestation of the Amazon rainforest is growing absurdly. Policies contrary to the control of deforestation will only cause accelerated climate change, which will endanger the lives of future generations.
1. Introduction The increase in the concentration of CO2 in the atmosphere and the resulting increase in the temperature of the land surface cause different responses in the biogeochemical processes of the soil-plant-atmosphere system. Deforestation and forest fires are largely responsible for the ~o et al., 2018). The last major highest carbon emission rates (Araga drought in 2015 associated with extreme fire events in the Amazon ~o resulted in high carbon emissions 989 � 504 Tg CO2 year 1 (Araga et al., 2018). Not only in the Amazon, but in other regions of the world, CO2
emissions are worrisome in recurring years of high deforestation and burning rates as in the forests of India and Mexico’s Ecoregions (Man �pez et al., 2019) and ojkumar and Srimuruganandam, 2019; Cruz-Lo other regions. Therefore, there is a global interest in fixing CO2 and limiting emission rates as mitigating measures of climate change at regional and global levels (Gustavsson et al., 2015). In terrestrial ecosystems, plants use CO2 as a source for photosyn thetic processes, with the partial storage function of atmospheric car bon. The total rate at which producers turn solar energy into chemical energy in the form of biomass through photosynthesis is called gross primary productivity (GPP) (Zhang et al., 2016). GPP is essential in the
* Corresponding author. State University of Mato Grosso, 78555000, Brazil. E-mail address: [email protected] (C.A. Silva Junior). https://doi.org/10.1016/j.rsase.2019.100259 Received 24 July 2019; Received in revised form 20 August 2019; Accepted 23 August 2019 Available online 24 August 2019 2352-9385/© 2019 Elsevier B.V. All rights reserved.
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Fig. 1. Geographic localization of the Legal Amazon and highlighted the state of Rond^ onia and vegetation cover.
ecosystems (Saleska al., 2009). The frequency and speed with which satellite data are obtained and processed, together with the possibility of regional and global studies, has provided an excellent cost-benefit relation for the study of ecosystems (Oliveira et al., 2017). Several orbital sensors are applied in the determination of GPP. The Moderate Resolution Imaging Spectroradiometer - MODIS sensor has several products, among them the MOD17A2, that estimates the GPP every 8 days with spatial resolution of 1 km and 500 m (Sakamoto et al., 2011). The LANDSAT (Land Remote Sensing Satellite) satellite series sensors can estimate GPP through the development of models and al gorithms associated with geoprocessing and remote sensing techniques, with a wide spatial coverage and continuity in time (Sims et al., 2008; Wu et al., 2009). LANDSAT and MODIS stand out for detecting long-term spatiotemporal variations in land cover due to their high data availability, global coverage and continuity over 40 years (Gitelson et al., 2008; Silva et al., 2013). The newly available multispectral images of the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) sensors from the Landsat 8 satellite, launched in 2013, have enhanced signal-to-noise ratio, as well as radiometric and spatial resolution, with potential to significantly improve the satellite’s forest monitoring capability (Irons et al., 2012; Roy et al., 2014). In spite of the potential of remote sensing, it is necessary to observe the observations of the surface of the radiation balance and of the energy and carbon fluxes to parameterize or even propose sensorial methods. Thus, it is through the combination of observations from the remote sensing together with a terrestrial network that one can better under stand the carbon balance of the Amazon forest and its patterns of resilience and vulnerability in response to climate change. In this sense, the objective of this study was to estimate gross primary productivity (GPP) in a region in Rond^ onia, in the Brazilian Western Amazon, using Landsat 8 OLI/TIRS satellite images to evaluate possible
Table 1 Surface meteorological data needed to calculate the GPPOLI/TIRS of the con ventional Ariquemes station. Parameter
04 Aug 2013
10 Oct 2014
Altitude of the weather station (m) Average air temperature (� C) Average air temperature (K) Global solar radiation (W m 2)
140 21 294 239.81
29 302 226.27
context of climate change, since it is directly related to the carbon effectively extracted from the atmosphere by different terrestrial eco systems (Almeida, 2016; Almeida et al., 2018). Therefore, it is important to understand the dynamics of productivity in the ecosystem and its performance in the global carbon balance (Pullens et al., 2016). Tropical forests contribute significantly to the overall GPP and are strategic regions for carbon offsetting and forest restoration projects (Beer et al., 2010; Restrepo-Coupe et al., 2013). In the Amazon rainforest, the expansion of agriculture and pasture has increased deforestation rates over the last decades, significantly changing the dynamics of energy and water flow, and acting as a source of CO2 emission into the atmosphere (Brown et al., 2016). Some methods such as the frequently used turbulent covariance method employs micrometeorological flow towers to analyze carbon flow and the atmospheric and climatic factors that regulate GPP (Saleska et al., 2009). However, this technique may be impractical because of its high operating costs and its low representativeness for heterogeneous sites. Therefore, numerical models with the aid of remote sensing tech niques are an important tool for the precise and large-scale monitoring of changes in surface coverage and their climatic consequences, as well as for the continuous observation of the dynamics of GPPs in forest 2
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Fig. 2. Spatial variability of Gross Primary Productivity (gC m
Remote Sensing Applications: Society and Environment 16 (2019) 100259
2
d
1
) estimated from the GPPOLI/TIRS sensor for 04 August 2013 (a) and 10 October 2014 (b).
changes in estimated productivity in areas of different uses of the soil and in different seasonal conditions.
extractivism was based on the denomination given by the National System of Nature Conservation Units (SNUC) (Law no. 9.985, of July 18, 2000) and (IBGE, 2018a,b).
2. Material and methods
2.2. Climatic classification and topographic variation
2.1. Characterization and location of the research area
€ppen, the state fits in Am According to the climatic classification of Ko type, tropical monsoon (Alvares et al., 2013), with annual mean air temperature of 26 � C and annual average rainfall of 2300 mm. The rain shows great seasonality, with a season of greater rainfall between November and March, and the dry season between May and September. April and October are months of transition between one regime and another. The relative humidity of the air varies between 80 and 90% from November to May and, in July and August, it remains around 75%. The topographic variation comprises reliefs ranging from flat to undulating between 80 and 140 m above sea level, and the dominant vegetation is of moist tropical forest, with presence of palm trees and lianas. The main plant formations include: Semideciduous Seasonal
The study area is located in southwest of the western Brazilian ^nia, between (8� and 15� S), and (60� and Amazon, in the state of Rondo � 65 W) (Fig. 1). It comprises an area of approximately 24,847 km2 covering part of 13 municipalities (Cujubim, Rio Crespo, Machadinho �, Ouro Preto do Oeste, D’oeste, Ariquemes, Vale do Anari, JI-Parana ~o, Governador Jorge Teixeira and Theobroma, Jarú, Nova Unia ^ndia). Cacaula The main classes of land cover and use in the study area are for vegetable extractiviness in forest areas, conservation units for integral protection and sustainable use, farming, and cities (IBGE, 2018a,b). The separation between the classes of conservation units and vegetable 3
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Fig. 3. Analysis boxplot of GPPOLI/TIRS and GPPMOD estimated for coverage classes on 04 August 2013.
Fig. 4. Analysis boxplot of GPPOLI/TIRS and GPPMOD estimated for coverage classes on 10 October 2014.
Forest, Open Ombrophilous Forest, Dense Ombrophilous Forest, Savannah and the Pioneer Formations of River Influence (IBGE, 2018a, b).
To compare the GPPOLI/TIRS estimation for the Landsat 8 images, data from the MODIS sensor, MOD17A2H product, were used. Initially, the GPP data of two micrometeorological flow towers, located in the area of pasture and forest of The Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA), near the study area, would be used for comparison. However, due to the maintenance of the towers, the data were only released until 2006, making it impossible to compare them with Landsat 8 images, available from 2013. The MOD17A2H product was obtained from (LP DAAC, 2018) for the ^nia, for the years 2013 and 2014. In the choice of images state of Rondo of each sensor, criteria such as air temperature and similar rainfall were also considered. It was not possible to compare the images with the same date, due to the MODIS sensor revisit period (every 8 days), and the difficulty of finding an OLI/TIRS image, without cloud cover, with a corresponding date. The images were preprocessed in the MRT (Modis Reprojection Tools) software of the HDF format for GEOTIFF, and the Sinusoidal projection for the geographic coordinate system in WGS 84 datum. The digital numbers of the MODIS images were converted into biophysical values (kg C m 2) through multiplication by the scale factor (0.0001) according to Heinsch et al. (2003). The GPPMOD values were also transformed from the accumulated value every 8 days to mean GPPMOD every 8 days, and converted from kg C m 2 day 1 to g C m 2 day 1 (Eq. (1)).
2.3. Remote sensing and weather data The images used come from the Landsat 8 satellite, obtained by the OLI and TIRS sensors, (USGS, 2016). The images acquired by these sensors consist of nine multispectral bands with spatial resolution of 30 m (bands 1 to 7 and 9), a panchromatic band with spatial resolution of 15 m (band 8) and two thermal bands with spatial resolution of 100 m (bands 10 and 11). The approximate size of the scene is 170 km to the north-south by 183 km to the east-west, being selected the scenes of August 04, 2013 (orbit 231 and point 67), representing the dry season, and October 10, 2014 (orbit 231 and point 67), representing the rainy season, both without the presence of cloudiness. For the calculation GPPOLI/TIRS, the variables of average air tem perature (Tar in � C) and global solar radiation (KJ m 2) were also necessary for the same dates of the OLI/TIRS images at the time of the satellite’s passage. These data were acquired from INMET (Instituto Nacional de Meteorologia), referring to the Automatic Meteorological Station (EMA) of Ariquemes. To facilitate calculations, the Tar was transformed from � C to Kelvin and the global solar radiation from KJ m 2 to Watt m 2 (Table 1). 4
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Fig. 5. Mean Error (g C m
2
d
1
) for 04 August 2013 (dry season) and 10 October 2014 (rainy season).
In the ERDAS IMAGINE 2015 software, the union of the satellite bands were calculated (bands 1, 2, 3, 4, 5, 6 and 7), calculation of the monochromatic reflectance of each specific band for the radiometric coefficients of the Landsat 8 satellite (USGS, 2016), union of reflectance (bands 1, 2, 3, 4, 5, 6 and 7), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Leaf Area Index (LAI) (bands 4 and 5) (Rouse et al., 1973; Huete, 1988; Allen et al., 2002), emissivity of each pixel in the spectral domain of the thermal band 10 (Allen et al., 2002), broadband emissivity (Allen et al., 2002), albedo at the top of the atmosphere with equation adjusted to the bands of the satellite (Ruhoff et al., 2015) and surface albedo (Allen et al., 2002). The surface temperature was obtained by function of the spectral radiance of the band 10 (Lλt), through the equations provided by the American Geological Service (USGS, 2016), long-wave radiation emitted by the atmosphere and surface, short-wave downward radiation emitted by the atmosphere, the balance of radiation, soil heat flux, sensible heat flow. For the estimation of GPPOLI/TIRS sensors (Eq. (2)), the method pro posed by Silva et al. (2013) was adopted, its determination by remote sensing can be done by combining the model of estimation of the Absorbed Photosynthetically Active Radiation - RFAA by vegetation, proposed by Monteith (1972), with the model of light use efficiency in Field et al. (1995) photosynthesis, adapted by Bastiaanssen and Ali (2003). More information about raw primary productivity estimation (GPPOLI/TIRS) using part of the SEBAL algorithm can be found in Chagas (2018).
Table 2 Mean error of the dry and rainy season for evaluated soil uses. 2
Land cover category
ME_04 Aug 2013 (g C m day 1)
Conservation units Vegetable extractiveness Farming Cities
0.30 0.48
2.06 1.70
1.11 2.96
0.74 3.62
GPPMOD ¼ scale factor x digital value
ME_10 Oct 2014 (g C m day 1)
2
(1)
where in the scale factor ¼ 0.0001 (kg C m 2) and digital value is the numeric value of a file pixel. In order to obtain a daily estimate of GPPMOD, we must divide this number by 8 and to move to g C m 2 day 1 the final value is multiplied by 1000. The GPPMOD data of the MOD17A2H product used for the compari son were the mean of the pixels of the same area delimited for the OLI/ TIRS images. Only good quality pixels obtained by “MODIS Quality Control” (QC) were used. In this case, a pixel was only considered when QC met the following requirements: i. “CLOUDSTATE” category is equal to 0 (significant clouds not pre sent) or 3 (cloud cover status not defined, light sky is assumed); ii. “SCF_QC” category equal to 0 (method used with best possible re sults) or 1 (method used with saturation).
GPPOLI=TIRS ¼ RFAA x ε
For comparison with the MODIS sensor data, the OLI/TIRS pixels were resampled to 500 m, according to the spatial resolution of the MOD17A2H product. Pixel resampling was performed in ArcGIS 10.5 software. Components of the Surface Radiation Balance are required as input data for the calculation of GPPOLI/TIRS. For its determination, the pro cedures adopted in the SEBAL algorithm (Surface Energy Balance Al gorithm for Land) proposed by Bastiaanssen et al. (1998) were used as standard.
(2)
where RFAA is the Absorbed Photosynthetically Active Radiation by vegetation (W m 2) and Ɛ is the model of light use efficiency (g C MJ 1). 2.4. Statistical analysis For the comparison of the data calculated from the Landsat 8 OLI/ TIRS satellite with the values derived from the MOD17A2H product, we used the statistical parameters Mean Error (ME) (Eq. (3)), determination 5
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units (48%), and vegetable extractiveness (42%), which characterizes the extremely heterogeneous dry season GPP series (Fig. 3). In the dry season, the average GPP estimated by GPPOLI/TIRS sensor was higher than GPPMOD for all land uses, except for farming and cities uses. The GPPMOD product presented minimum standard deviation for the conservation units class of 0.37 g C m 2 d 1 to a maximum of 0.80 g C m 2 d 1 for the cities use. The coefficient of variation was very low, indicating a greater homogeneity with low dispersion of the data when compared to the calculated GPP data using GPPOLI/TIRS sensors. The same pattern was observed in the rainy season (Fig. 4). The averages obtained for conservation units and vegetable extractiveness were 7.47 g C m 2 d 1 and 6.54 g C m 2 d 1, respectively, with greater variation for cities (80%), followed by the uses of farming (72%), con servation units (34%) and vegetable extractiveness (32%). The uses of farming and cities presented the lowest values of GPP in comparison with the other uses, in each period. 3.2. GPP performance The averages estimated for all uses of the GPPMOD are greater than the GPPOLI/TIRS (Fig. 4), there is an underestimate of GPPOLI/TIRS in relation to GPPMOD. The mean error found was significantly lower than in the dry season (2013) (Fig. 5), showing a closer distribution of the MOD17A2H product, in addition to the lower ME values for vegetated areas (Table 2). In the analysis of regression and correlation of the GPPOLI/TIRS data compared to the GPPMOD product data (Fig. 6a–b), the rainy season was the one with the highest correlation values, where the values of r2 ¼ 0.28 and r ¼ 0.53 were slightly larger than the dry season with r2 ¼ 0.11 and r ¼ 0.33. From the GPP values for the whole image, the tendency to over estimate in relation to the GPPMOD product in the dry season is observed. In the rainy season, there is a greater predominance of higher values of GPPMOD (Fig. 7a–b). 3.3. Biophysical variables
Fig. 6. Regression analysis (R2) and correlation (r) of the GPPOLI/TIRS data compared to the GPPMOD data for 04 August 2013 (a) and 10 October 2014 (b).
coefficients (r2) (Eq. (4)) and correlation (r) (Eq. (5)). PN ðPi Oi Þ EM ¼ i¼1 N Pn ðPi r2 ¼ Pni¼1 ðO i i¼1
OÞ2 OÞ2
P Oi Pi ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi r ¼ rffiffiffiffiffiffiffiffiffiffiffiffiq P 2 P P2i Oi
In the analysis of vegetation indexes, NDVI presented 0.76 and 0.74 for the uses of conservation units and vegetable extractives, respectively, and 0.54 and 0.32 for farming and cities uses (Fig. 8a). For the rainy season, the NDVI varied from 0.70 to 0.71 for the uses of conservation units and vegetable extractiveness and 0.56 to 0.36 for the farming and cities uses (Fig. 8b). In contrast to NDVI, the albedo values of the surface and surface temperature found show the differences in the coverage of the region, presenting the highest values for farming and cities and the lowest values for conservation units and vegetable extractiveness for both seasons (Fig. 8a–b).
(3) (4) (5)
4. Discussion
where N is the number of observations, Pi is the estimated GPPOLI/TIRS, Oi is the observed GPPMOD, and O are the mean values of Pi and Oi.
The greater variability of values for the use of vegetable extractive and conservation units for the two scenes occurred due to antropic intervention in this use and phenological characteristics of the vegeta tion. The main activities are the extraction of wood, the cultivation of chestnut and extraction of the rubber trees (Hevea brasiliensis Muell. Agr.) (IBGE, 2018a,b). In addition, they are characterized as forest remnants in the shape of fragments in which productivity is reduced also by edge effect, compared to areas with homogeneous vegetation, high lighting the presence of pixels with such low productivity. First, the greater variability of GPP found for GPPOLI/TIRS can occur due to the low resolution of the 500 m MOD17A2H product, which prevents more accurate detection of the targets in the image, compared to the high resolution of the OLI/TIRS sensor, even after resampling. The 30 m GPPOLI/TIRS resolution can capture more clearly the maximum and minimum extremes, which are smoothed by the coarser MODIS
3. Results 3.1. GPP and conservation units The value of GPP is substantially higher for the areas that comprise conservation units with values above 16 g C m 2 d 1 for 2013 (Fig. 2a) and above 12 gC m 2 d 1 in 2014 (Fig. 2b). The conservation units and the vegetal extractiveness presented the highest GPP values in the dry season, with values close to 16 gC m 2 d 1. Close to GPP values were determined for both uses because these are areas with forest cover, but the cities had the highest variation of the data with CV higher than 80%, followed by farming (65%), conservation 6
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Fig. 7. GPPOLI/TIRS and GPPMOD pixel frequency data for 04 August 2013 (a) and 10 October 2014 (b).
resolution. In addition, the MOD17A2H product is a global product and presents reanalysis data in grid points, which limits the specificity of local characteristics (Silva et al., 2013). Despite its limitations, MODIS and other sensors that estimate the GPP are still essential for spatial scale analysis, once there is a great uncertainty of the models that estimate the GPP in spatial scale, since the input of the data is totally compromised (Tramontana et al., 2015). Although the terrestrial carbon dynamics is well known, it still has a great uncertainty of data as the primary source for the models that es timate GPP at a global and/or regional level (Tramontana et al., 2015). By calculating GPP from Landsat TM satellite data in irrigated areas and native vegetation in the cerrado biome in the Federal District, Braga (2013) also found an overestimate in relation to the MOD17A2 product, mainly in the irrigated perimeters, indicating that the algorithm based
on Landsat images can saturate GPP values in regions with high water availability. NDVI saturation in the dry season occurred as the canopy approached its stage of maximum vegetative growth. The NDVI reaches its saturation with high values of the Leaf Area Index (LAI). This pattern can be attributed by the influence of radiance on the atmospheric tra jectory, increasing the brightness and spectral effect of the soil, which interferes with the evaluation of the vegetation (Jensen, 2009) and may explain the high productivity found for the representative image of the dry season. By calculating the radiation balance from Landsat 8 images in the Pajeú River Basin on the border between the states of Cear� a and Paraíba, Alves et al. (2017) observed that albedo values increase together with the surface temperature in different uses. Ruhoff et al. (2015) also with 7
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Fig. 8. Analysis of histograms of the NDVI pixels, temperature and surface albedo for each land cover data for 04 August 2013 (a) and 10 October 2014 (b).
Landsat 8 images, in a hydrographic basin in the Serra do Mar State ~o Paulo, found albedo values for dense ombro Park, in the state of Sa philous forest areas between 0.11 and 0.12, and for urban areas, fields and pastures, they found values of albedo above 0.14, corroborating with the values found in this study. The albedo values correspond to the type of surface coverage where part of the incident energy is absorbed and part is reflected. Areas without vegetation cover have high diffuse reflectivity and low energy absorption, increasing the albedo and consequently the surface temperature. Conservation units and Vegetable extractiveness areas are the re gions that have the largest amounts of carbon, undoubtedly also have the greatest biodiversity of this region, which in fact shows the impor tance of preserving these environments. Knowledge of carbon seques tration in these areas is important to guide climate change mitigation and adaptation.
5. Conclusion The GPP estimated by the Landsat 8 OLI/TIRS satellite made it possible to analyze the spatial distinctions between the land uses in the region. However, it presented similarities of estimation with the MOD17A2H product only for image referring to the rainy period. The image relating to rainy season underestimated the GPPOLI/TIRS in relation to the GPPMOD for all uses, due to lower radiation and surface moisture indexes found for this image. The high resolution of the sat ellite increases the differences of the calculated maximum and minimum values, with a greater variability of surface target. The proposed algo rithm, due to its applicability to Landsat 8 OLI/TIRS satellite, may be useful for more accurate analysis of GPP for local study areas such as hydrographic basins and forest fragments. The focus on carbon sequestration by forest ecosystems and the 8
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reduction of CO2 emissions is the foundation for mitigating climate change damage and consequences at regional and global levels. Currently deforestation of the Amazon rainforest is growing absurdly. Policies contrary to the control of deforestation will only cause accelerated climate change, which will endanger the lives of future generations.
Gitelson, A.A., Vi~ na, A., Masek, J.G., Verma, S.B., Suyker, A.E., 2008. Synoptic monitoring of gross primary productivity of maize using Landsat data. IEEE Geosci. Remote Sens. Lett. 5, 133–137. Gustavsson, L., Haus, S., Ortiz, C.A., Sathre, R., Le Truong, N., 2015. Climate effects of bioenergy from forest residues in comparison to fossil energy. Appl. Energy 138, 36–50. Heinsch, F.A., Reeves, M., Votava, P., Kang, S.Y., Milesi, C., Zhao, M.S., et al., 2003. User’s Guide, GPP and NPP (MOD17A2/A3) Products. NASA MODIS land algorithm [online]. http://www.ntsg.umt.edu/modis/MOD17UsersGuide.pdf. (Accessed 27 March 2014). Huete, A.R., 1988. Adjusting vegetation indices for soil influences. Int. Agrophys. 4, 367–376. IBGE, 2018. Instituto Brasileiro de Geografia e Estatística. Manual t� ecnico da vegetaç~ ao brasileira. https://biblioteca.ibge.gov.br/visualizacao/livros/liv63011.pdf. (Accessed 1 March 2018). IBGE, 2018. Instituto Brasileiro de Geografia e Estatística. http://downloads.ibge.gov. br/downloads_geociencias.htm. Acesso em 01/03/2018. Irons, J.R., Dwyer, J.L., Barsi, J.A., 2012. The next Landsat satellite: the Landsat data continuity mission. Remote Sens. Environ. 122, 11–21. Jensen, J.R., 2009. Sensoriamento remoto do ambiente: uma perspectiva em recursos terrestres. 2a Ediç~ ao traduzida pelo Instituto Nacional de Pesquisas Espaciais–INPE. S~ ao Paulo, p. 672. Par^ entese. LP DAAC, 2018. Land Processes Distributed Active Archive Center. https://lpdaac.usgs. gov/data_access/data_pool. Acesso em 01/03/2018. Manojkumar, N., Srimuruganandam, B., 2019. Assessment of gaseous emissions and radiative forcing in Indian forest fires. Int. J. Environ. Stud. 76, 541–557. Monteith, J.L., 1972. Solar radiation and productivity in tropical ecosystems. J. Appl. Ecol. 9, 747–766. Oliveira, G., Moraes, E.C., Shimabukuro, Y.E., Oliveira, L.E., Arag~ ao, C., Mataveli, G.A. V., 2017. Dados de sensoriamento remoto para o estudo das interaç~ oes biosferaatmosfera em ecossistemas amaz^ onicos: uma revis~ ao. Rev. Bras. Cartogr. 69, 1189–1210. Pullens, J.W.M., Sottocornola, M., Kiely, G., Toscano, P., Gianelle, D., 2016. Carbon fluxes of an alpine peatland in Northern Italy. Agric. For. Meteorol. 220, 69–82. Restrepo-Coupe, N., da Rocha, H.R., Hutyra, L.R., da Araujo, A.C., Borma, L.S., Christoffersen, B., Fitzjarrald, D.R., 2013. What drives the seasonality of photosynthesis across the Amazon basin? A cross-site analysis of eddy flux tower measurements from the Brasil flux network. Agric. For. Meteorol. 182, 128–144. Rouse Jr., J.W., Haas, R.H., Schell, J.A., Deering, D.W., 1973. Monitoring vegetation systems in the great plains with ERTS. In: 3rd ERTS Symposium, NASA, SP-251 I, Washington, pp. 309–317. Roy, D.P., Wulder, M.A., Loveland, T.R., Woodcock, C.E., Allen, R.G., Anderson, M.C., Scambos, T.A., 2014. Landsat-8: Science and product vision for terrestrial global change research. Remote Sens. Environ. 145, 154–172. Ruhoff, A.L., Novo, B.D.S., Rocha, H.D., 2015. Determinaç~ ao da irradi^ ancia solar no topo da atmosfera para c� alculo do albedo e balanço de energia a partir de imagens LANDSAT 8 OLI. Anais Online XVII Simp� osio Brasileiro de Sensoriamento RemotoSBSR. Jo~ ao Pessoa-PB, Brasil, INPE 4496–4502. http://www.dsr.inpe.br/sbsr2015/ files/p0883.pdf. (Accessed 20 February 2019). Sakamoto, T., Gitelson, A.A., Wardlow, B.D., Verma, S.B., Suyker, A.E., 2011. Estimating daily gross primary production of maize based only on MODIS WDRVI and shortwave radiation data. Remote Sens. Environ. 115, 3091–3101. Saleska, S., da Rocha, H., Kruijt, B., Nobre, A., 2009. Fluxos de Carbono do Ecossistema e Metabolismo da Floresta Amaz^ onica. Amazonia Glob. Change Geophys. Monograph Series 186, 389–407. Silva, B.D., Galvíncio, J.D., Montenegro, S.M.G.L., Machado, C.C.C., Oliveira, L.D., Moura, M.D., 2013. Determinaç~ ao por sensoriamento remoto da produtividade prim� aria bruta do perímetro irrigado S~ ao Gonçalo–PB. Revista Brasileira de Meteorologia 28, 57–64. Sims, D.A., Rahman, A.F., Cordova, V.D., El-Masri, B.Z., Baldocchi, D.D., Bolstad, P.V., Monson, R.K., 2008. A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS. Remote Sens. Environ. 112, 1633–1646. Tramontana, G., Ichii, K., Camps-Valls, G., Tomelleri, E., Papale, D., 2015. Uncertainty analysis of gross primary production upscaling using Random Forests, remote sensing and eddy covariance data. Remote Sens. Environ. 168, 360–373. USGS, 2016. Landsat 8 (L8) data users handbook, 2015, LSDS-1574, version 2.0. Sioux Falls, USA: USGS EROS. https://landsat.usgs.gov/documents/Landsat8DataUsers Handbook.pdf. (Accessed 23 June 2016). Wu, C., Niu, Z., Tang, Q., Huang, W., Rivard, B., Feng, J., 2009. Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices. Agric. For. Meteorol. 149, 1015–1021. Zhang, Y., Song, C., Sun, G., Band, L.E., McNulty, S., Noormets, A., Zhang, Z., 2016. Development of a coupled carbon and water model for estimating global gross primary productivity and evapotranspiration based on eddy flux and remote sensing data. Agric. For. Meteorol. 223, 116–131.
Ethical All ethical practices have been followed in relation to the develop ment, writing, and publication of the article entitled Gross primary productivity in areas of different land cover in the western Brazilian Amazon. Acknowledgment We thank the National Council for Scientific and Technological Development - CNPq (nº 442966/2014-8) for funding the project related to this study. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.rsase.2019.100259. References Allen, R., Bastiaanssen, W., Waters, R., Tasumi, M., Trezza, R., 2002. (Surface Energy Balance Algorithms for Land). Advance Training andUsers Manual – IdahoImplementation, p. 97 version 1.0. Almeida, C.T., 2016. Produtividade Prim� aria Bruta na Amaz^ onia Legal: relaç~ ao com vari� aveis meteorol� ogicas e validaç~ ao do produto MOD17A2. Master’s dissertation. Universidade Federal Rural do Rio de Janeiro, Serop� edica, Brasil, p. 77p. Almeida, C.T., Delgado, R.C., Galv~ ao, L.S., Ramos, M.C., 2018. Improvements of the MODIS Gross Primary Productivity model based on a comprehensive uncertainty assessment over the Brazilian Amazonia. ISPRS J. Photogrammetry Remote Sens. 145, 268–283. Alvares, C.A., Stape, J.L., Sentelhas, P.C., de Moraes, G., Leonardo, J., Sparovek, G., 2013. K€ oppen’s climate classification map for Brazil. Meteorol. Z. 22, 711–728. Alves, L.E.R., Gomes, H.B., dos Santos, M.N., de Freitas, I.G.F., 2017. Balanço de radiaç~ ao atrav� es do sat� elite Landsat-8 na bacia do Rio Pajeú. Revista do Departamento de Geografia 33, 117–127. Arag~ ao, L.E., Anderson, L.O., Fonseca, M.G., Rosan, T.M., Vedovato, L.B., Wagner, F.H., Barlow, J., Berenguer, E., Deeter, M.N., Domingues, L.G., Gatti, L., Gloor, M., Yadvinder, M., Marengo, J.A., Miller, J.B., Phillips, O.L., Saatchi, S., 2018. 21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions. Nat. Commun. 9, 1–12. Bastiaanssen, W.G., Ali, S., 2003. A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agric. Ecosyst. Environ. 94, 321–340. Bastiaanssen, W.G., Menenti, M., Feddes, R.A., Holtslag, A.A.M., 1998. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. J. Hydrol. 212, 198–212. Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais, N., Bondeau, A., 2010. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834–838. � Braga, A.P., 2013. Estimativa da Produtividade Prim� aria Bruta em Areas Agrícolas e de Vegetaç~ ao Prim� aria no Cerrado por Sensoriamento Remoto. Master’s dissertation. Universidade Federal de Campina Grande, Campina Grande, p. 66. Brown, D.S., Brown, J.C., Brown, C., 2016. Land occupations and deforestation in the Brazilian Amazon. Land Use Policy 54, 331–338. Chagas, M.C., 2018. Produtividade Prim� aria Bruta na Amaz^ onia Legal: relaç~ ao com vari� aveis meteorol� ogicas e validaç~ ao do produto MOD17A2. Master’s dissertation. Universidade Federal Rural do Rio de Janeiro, Serop� edica, Brasil, p. 53. Cruz-L� opez, M.I., Manzo-Delgado, L.D.L., Aguirre-G� omez, R., Chuvieco, E., EquihuaBenítez, J.A., 2019. Spatial distribution of forest fire emissions: a case study in three Mexican Ecoregions. Remote Sens. 11, 1185. Field, C.B., Randerson, J.T., Malmstrom, C.M., 1995. Global net primary production: combining ecology and remote sensing. Remote Sens. Environ. 51, 74–88.
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Remote Sensing Applications: Society and Environment journal homepage: http://www.elsevier.com/locate/rsase
Gross primary productivity in areas of different land cover in the western Brazilian Amazon Mariana C. Chagas a, Rafael C. Delgado a, Leonardo P. de Souza b, Daniel C. de Carvalho a, Marcos G. Pereira c, Paulo E. Teodoro d, Carlos A. Silva Junior e, * a
Department of Environmental Sciences, Forest Institute, Federal Rural University of Rio de Janeiro (UFRRJ), 23897-000, Serop�edica, Rio de Janeiro, Brazil Center for Biological and Natural Sciences, Federal University of Acre (UFAC), 69920-900, Rio Branco, Acre, Brazil Department of Soils of the Federal Rural University of Rio de Janeiro (UFRRJ), 23897-000, Serop�edica, Rio de Janeiro, Brazil d Department of Crop Science, Federal University of Mato Grosso do Sul (UFMS), 79560000, Chapad~ ao do Sul, Mato Grosso do Sul, Brazil e Department of Geography, State University of Mato Grosso (UNEMAT), 78555-000, Sinop, Mato Grosso, Brazil b c
A R T I C L E I N F O
A B S T R A C T
Keywords: Carbon in forests Orbital sensors Climate change Algorithms
Uncertainty regarding gross carbon sequestration at local, regional, and global scales can be reduced by moni toring the land surface processes at high spatial and temporal resolutions. In this sense, the objective of this study was to estimate gross primary productivity (GPP) in a region of the western Brazilian Amazon using Landsat 8 OLI/TIRS images, and to evaluate possible changes in estimated productivity among areas of different land use and in different seasonal conditions (August 04, 2013, representing the dry season, and October 10, 2014, representing the rainy season). The images were subjected to atmospheric and radiometric corrections. As the basic input for the model, the balance of radiation and other components of the energy balance were calculated using the Surface Energy Balance Algorithm for Land. The GPP estimated through the OLI/TIRS sensor was compared with the MOD17A2 product of the MODIS sensor. The comparison of GPP by the OLI/TIRS sensors and the MOD17A2H product were based on the mean error (ME), simple linear regression (r2) and correlation co efficient (r). The estimated GPP indicated distinctions among land use types, however, similarities with MOD17A2H were detected only with the image from the rainy season (r ¼ 0.53), with a slight underestimation for all land uses. The dry season image showed r2 ¼ 0.11, r ¼ 0.33 and ME ¼ 0.48 gCm 2day 1 for extractivism, while the land use types were overestimated by the model. The OLI/TIRS sensor estimates need to be validated with data from flow towers. The focus on carbon sequestration by forest ecosystems and the reduction of CO2 emissions is the foundation for mitigating climate change damage and consequences at regional and global levels. Currently deforestation of the Amazon rainforest is growing absurdly. Policies contrary to the control of deforestation will only cause accelerated climate change, which will endanger the lives of future generations.
1. Introduction The increase in the concentration of CO2 in the atmosphere and the resulting increase in the temperature of the land surface cause different responses in the biogeochemical processes of the soil-plant-atmosphere system. Deforestation and forest fires are largely responsible for the ~o et al., 2018). The last major highest carbon emission rates (Araga drought in 2015 associated with extreme fire events in the Amazon ~o resulted in high carbon emissions 989 � 504 Tg CO2 year 1 (Araga et al., 2018). Not only in the Amazon, but in other regions of the world, CO2
emissions are worrisome in recurring years of high deforestation and burning rates as in the forests of India and Mexico’s Ecoregions (Man �pez et al., 2019) and ojkumar and Srimuruganandam, 2019; Cruz-Lo other regions. Therefore, there is a global interest in fixing CO2 and limiting emission rates as mitigating measures of climate change at regional and global levels (Gustavsson et al., 2015). In terrestrial ecosystems, plants use CO2 as a source for photosyn thetic processes, with the partial storage function of atmospheric car bon. The total rate at which producers turn solar energy into chemical energy in the form of biomass through photosynthesis is called gross primary productivity (GPP) (Zhang et al., 2016). GPP is essential in the
* Corresponding author. State University of Mato Grosso, 78555000, Brazil. E-mail address: [email protected] (C.A. Silva Junior). https://doi.org/10.1016/j.rsase.2019.100259 Received 24 July 2019; Received in revised form 20 August 2019; Accepted 23 August 2019 Available online 24 August 2019 2352-9385/© 2019 Elsevier B.V. All rights reserved.
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Fig. 1. Geographic localization of the Legal Amazon and highlighted the state of Rond^ onia and vegetation cover.
ecosystems (Saleska al., 2009). The frequency and speed with which satellite data are obtained and processed, together with the possibility of regional and global studies, has provided an excellent cost-benefit relation for the study of ecosystems (Oliveira et al., 2017). Several orbital sensors are applied in the determination of GPP. The Moderate Resolution Imaging Spectroradiometer - MODIS sensor has several products, among them the MOD17A2, that estimates the GPP every 8 days with spatial resolution of 1 km and 500 m (Sakamoto et al., 2011). The LANDSAT (Land Remote Sensing Satellite) satellite series sensors can estimate GPP through the development of models and al gorithms associated with geoprocessing and remote sensing techniques, with a wide spatial coverage and continuity in time (Sims et al., 2008; Wu et al., 2009). LANDSAT and MODIS stand out for detecting long-term spatiotemporal variations in land cover due to their high data availability, global coverage and continuity over 40 years (Gitelson et al., 2008; Silva et al., 2013). The newly available multispectral images of the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) sensors from the Landsat 8 satellite, launched in 2013, have enhanced signal-to-noise ratio, as well as radiometric and spatial resolution, with potential to significantly improve the satellite’s forest monitoring capability (Irons et al., 2012; Roy et al., 2014). In spite of the potential of remote sensing, it is necessary to observe the observations of the surface of the radiation balance and of the energy and carbon fluxes to parameterize or even propose sensorial methods. Thus, it is through the combination of observations from the remote sensing together with a terrestrial network that one can better under stand the carbon balance of the Amazon forest and its patterns of resilience and vulnerability in response to climate change. In this sense, the objective of this study was to estimate gross primary productivity (GPP) in a region in Rond^ onia, in the Brazilian Western Amazon, using Landsat 8 OLI/TIRS satellite images to evaluate possible
Table 1 Surface meteorological data needed to calculate the GPPOLI/TIRS of the con ventional Ariquemes station. Parameter
04 Aug 2013
10 Oct 2014
Altitude of the weather station (m) Average air temperature (� C) Average air temperature (K) Global solar radiation (W m 2)
140 21 294 239.81
29 302 226.27
context of climate change, since it is directly related to the carbon effectively extracted from the atmosphere by different terrestrial eco systems (Almeida, 2016; Almeida et al., 2018). Therefore, it is important to understand the dynamics of productivity in the ecosystem and its performance in the global carbon balance (Pullens et al., 2016). Tropical forests contribute significantly to the overall GPP and are strategic regions for carbon offsetting and forest restoration projects (Beer et al., 2010; Restrepo-Coupe et al., 2013). In the Amazon rainforest, the expansion of agriculture and pasture has increased deforestation rates over the last decades, significantly changing the dynamics of energy and water flow, and acting as a source of CO2 emission into the atmosphere (Brown et al., 2016). Some methods such as the frequently used turbulent covariance method employs micrometeorological flow towers to analyze carbon flow and the atmospheric and climatic factors that regulate GPP (Saleska et al., 2009). However, this technique may be impractical because of its high operating costs and its low representativeness for heterogeneous sites. Therefore, numerical models with the aid of remote sensing tech niques are an important tool for the precise and large-scale monitoring of changes in surface coverage and their climatic consequences, as well as for the continuous observation of the dynamics of GPPs in forest 2
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Fig. 2. Spatial variability of Gross Primary Productivity (gC m
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2
d
1
) estimated from the GPPOLI/TIRS sensor for 04 August 2013 (a) and 10 October 2014 (b).
changes in estimated productivity in areas of different uses of the soil and in different seasonal conditions.
extractivism was based on the denomination given by the National System of Nature Conservation Units (SNUC) (Law no. 9.985, of July 18, 2000) and (IBGE, 2018a,b).
2. Material and methods
2.2. Climatic classification and topographic variation
2.1. Characterization and location of the research area
€ppen, the state fits in Am According to the climatic classification of Ko type, tropical monsoon (Alvares et al., 2013), with annual mean air temperature of 26 � C and annual average rainfall of 2300 mm. The rain shows great seasonality, with a season of greater rainfall between November and March, and the dry season between May and September. April and October are months of transition between one regime and another. The relative humidity of the air varies between 80 and 90% from November to May and, in July and August, it remains around 75%. The topographic variation comprises reliefs ranging from flat to undulating between 80 and 140 m above sea level, and the dominant vegetation is of moist tropical forest, with presence of palm trees and lianas. The main plant formations include: Semideciduous Seasonal
The study area is located in southwest of the western Brazilian ^nia, between (8� and 15� S), and (60� and Amazon, in the state of Rondo � 65 W) (Fig. 1). It comprises an area of approximately 24,847 km2 covering part of 13 municipalities (Cujubim, Rio Crespo, Machadinho �, Ouro Preto do Oeste, D’oeste, Ariquemes, Vale do Anari, JI-Parana ~o, Governador Jorge Teixeira and Theobroma, Jarú, Nova Unia ^ndia). Cacaula The main classes of land cover and use in the study area are for vegetable extractiviness in forest areas, conservation units for integral protection and sustainable use, farming, and cities (IBGE, 2018a,b). The separation between the classes of conservation units and vegetable 3
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Fig. 3. Analysis boxplot of GPPOLI/TIRS and GPPMOD estimated for coverage classes on 04 August 2013.
Fig. 4. Analysis boxplot of GPPOLI/TIRS and GPPMOD estimated for coverage classes on 10 October 2014.
Forest, Open Ombrophilous Forest, Dense Ombrophilous Forest, Savannah and the Pioneer Formations of River Influence (IBGE, 2018a, b).
To compare the GPPOLI/TIRS estimation for the Landsat 8 images, data from the MODIS sensor, MOD17A2H product, were used. Initially, the GPP data of two micrometeorological flow towers, located in the area of pasture and forest of The Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA), near the study area, would be used for comparison. However, due to the maintenance of the towers, the data were only released until 2006, making it impossible to compare them with Landsat 8 images, available from 2013. The MOD17A2H product was obtained from (LP DAAC, 2018) for the ^nia, for the years 2013 and 2014. In the choice of images state of Rondo of each sensor, criteria such as air temperature and similar rainfall were also considered. It was not possible to compare the images with the same date, due to the MODIS sensor revisit period (every 8 days), and the difficulty of finding an OLI/TIRS image, without cloud cover, with a corresponding date. The images were preprocessed in the MRT (Modis Reprojection Tools) software of the HDF format for GEOTIFF, and the Sinusoidal projection for the geographic coordinate system in WGS 84 datum. The digital numbers of the MODIS images were converted into biophysical values (kg C m 2) through multiplication by the scale factor (0.0001) according to Heinsch et al. (2003). The GPPMOD values were also transformed from the accumulated value every 8 days to mean GPPMOD every 8 days, and converted from kg C m 2 day 1 to g C m 2 day 1 (Eq. (1)).
2.3. Remote sensing and weather data The images used come from the Landsat 8 satellite, obtained by the OLI and TIRS sensors, (USGS, 2016). The images acquired by these sensors consist of nine multispectral bands with spatial resolution of 30 m (bands 1 to 7 and 9), a panchromatic band with spatial resolution of 15 m (band 8) and two thermal bands with spatial resolution of 100 m (bands 10 and 11). The approximate size of the scene is 170 km to the north-south by 183 km to the east-west, being selected the scenes of August 04, 2013 (orbit 231 and point 67), representing the dry season, and October 10, 2014 (orbit 231 and point 67), representing the rainy season, both without the presence of cloudiness. For the calculation GPPOLI/TIRS, the variables of average air tem perature (Tar in � C) and global solar radiation (KJ m 2) were also necessary for the same dates of the OLI/TIRS images at the time of the satellite’s passage. These data were acquired from INMET (Instituto Nacional de Meteorologia), referring to the Automatic Meteorological Station (EMA) of Ariquemes. To facilitate calculations, the Tar was transformed from � C to Kelvin and the global solar radiation from KJ m 2 to Watt m 2 (Table 1). 4
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Fig. 5. Mean Error (g C m
2
d
1
) for 04 August 2013 (dry season) and 10 October 2014 (rainy season).
In the ERDAS IMAGINE 2015 software, the union of the satellite bands were calculated (bands 1, 2, 3, 4, 5, 6 and 7), calculation of the monochromatic reflectance of each specific band for the radiometric coefficients of the Landsat 8 satellite (USGS, 2016), union of reflectance (bands 1, 2, 3, 4, 5, 6 and 7), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Leaf Area Index (LAI) (bands 4 and 5) (Rouse et al., 1973; Huete, 1988; Allen et al., 2002), emissivity of each pixel in the spectral domain of the thermal band 10 (Allen et al., 2002), broadband emissivity (Allen et al., 2002), albedo at the top of the atmosphere with equation adjusted to the bands of the satellite (Ruhoff et al., 2015) and surface albedo (Allen et al., 2002). The surface temperature was obtained by function of the spectral radiance of the band 10 (Lλt), through the equations provided by the American Geological Service (USGS, 2016), long-wave radiation emitted by the atmosphere and surface, short-wave downward radiation emitted by the atmosphere, the balance of radiation, soil heat flux, sensible heat flow. For the estimation of GPPOLI/TIRS sensors (Eq. (2)), the method pro posed by Silva et al. (2013) was adopted, its determination by remote sensing can be done by combining the model of estimation of the Absorbed Photosynthetically Active Radiation - RFAA by vegetation, proposed by Monteith (1972), with the model of light use efficiency in Field et al. (1995) photosynthesis, adapted by Bastiaanssen and Ali (2003). More information about raw primary productivity estimation (GPPOLI/TIRS) using part of the SEBAL algorithm can be found in Chagas (2018).
Table 2 Mean error of the dry and rainy season for evaluated soil uses. 2
Land cover category
ME_04 Aug 2013 (g C m day 1)
Conservation units Vegetable extractiveness Farming Cities
0.30 0.48
2.06 1.70
1.11 2.96
0.74 3.62
GPPMOD ¼ scale factor x digital value
ME_10 Oct 2014 (g C m day 1)
2
(1)
where in the scale factor ¼ 0.0001 (kg C m 2) and digital value is the numeric value of a file pixel. In order to obtain a daily estimate of GPPMOD, we must divide this number by 8 and to move to g C m 2 day 1 the final value is multiplied by 1000. The GPPMOD data of the MOD17A2H product used for the compari son were the mean of the pixels of the same area delimited for the OLI/ TIRS images. Only good quality pixels obtained by “MODIS Quality Control” (QC) were used. In this case, a pixel was only considered when QC met the following requirements: i. “CLOUDSTATE” category is equal to 0 (significant clouds not pre sent) or 3 (cloud cover status not defined, light sky is assumed); ii. “SCF_QC” category equal to 0 (method used with best possible re sults) or 1 (method used with saturation).
GPPOLI=TIRS ¼ RFAA x ε
For comparison with the MODIS sensor data, the OLI/TIRS pixels were resampled to 500 m, according to the spatial resolution of the MOD17A2H product. Pixel resampling was performed in ArcGIS 10.5 software. Components of the Surface Radiation Balance are required as input data for the calculation of GPPOLI/TIRS. For its determination, the pro cedures adopted in the SEBAL algorithm (Surface Energy Balance Al gorithm for Land) proposed by Bastiaanssen et al. (1998) were used as standard.
(2)
where RFAA is the Absorbed Photosynthetically Active Radiation by vegetation (W m 2) and Ɛ is the model of light use efficiency (g C MJ 1). 2.4. Statistical analysis For the comparison of the data calculated from the Landsat 8 OLI/ TIRS satellite with the values derived from the MOD17A2H product, we used the statistical parameters Mean Error (ME) (Eq. (3)), determination 5
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units (48%), and vegetable extractiveness (42%), which characterizes the extremely heterogeneous dry season GPP series (Fig. 3). In the dry season, the average GPP estimated by GPPOLI/TIRS sensor was higher than GPPMOD for all land uses, except for farming and cities uses. The GPPMOD product presented minimum standard deviation for the conservation units class of 0.37 g C m 2 d 1 to a maximum of 0.80 g C m 2 d 1 for the cities use. The coefficient of variation was very low, indicating a greater homogeneity with low dispersion of the data when compared to the calculated GPP data using GPPOLI/TIRS sensors. The same pattern was observed in the rainy season (Fig. 4). The averages obtained for conservation units and vegetable extractiveness were 7.47 g C m 2 d 1 and 6.54 g C m 2 d 1, respectively, with greater variation for cities (80%), followed by the uses of farming (72%), con servation units (34%) and vegetable extractiveness (32%). The uses of farming and cities presented the lowest values of GPP in comparison with the other uses, in each period. 3.2. GPP performance The averages estimated for all uses of the GPPMOD are greater than the GPPOLI/TIRS (Fig. 4), there is an underestimate of GPPOLI/TIRS in relation to GPPMOD. The mean error found was significantly lower than in the dry season (2013) (Fig. 5), showing a closer distribution of the MOD17A2H product, in addition to the lower ME values for vegetated areas (Table 2). In the analysis of regression and correlation of the GPPOLI/TIRS data compared to the GPPMOD product data (Fig. 6a–b), the rainy season was the one with the highest correlation values, where the values of r2 ¼ 0.28 and r ¼ 0.53 were slightly larger than the dry season with r2 ¼ 0.11 and r ¼ 0.33. From the GPP values for the whole image, the tendency to over estimate in relation to the GPPMOD product in the dry season is observed. In the rainy season, there is a greater predominance of higher values of GPPMOD (Fig. 7a–b). 3.3. Biophysical variables
Fig. 6. Regression analysis (R2) and correlation (r) of the GPPOLI/TIRS data compared to the GPPMOD data for 04 August 2013 (a) and 10 October 2014 (b).
coefficients (r2) (Eq. (4)) and correlation (r) (Eq. (5)). PN ðPi Oi Þ EM ¼ i¼1 N Pn ðPi r2 ¼ Pni¼1 ðO i i¼1
OÞ2 OÞ2
P Oi Pi ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi r ¼ rffiffiffiffiffiffiffiffiffiffiffiffiq P 2 P P2i Oi
In the analysis of vegetation indexes, NDVI presented 0.76 and 0.74 for the uses of conservation units and vegetable extractives, respectively, and 0.54 and 0.32 for farming and cities uses (Fig. 8a). For the rainy season, the NDVI varied from 0.70 to 0.71 for the uses of conservation units and vegetable extractiveness and 0.56 to 0.36 for the farming and cities uses (Fig. 8b). In contrast to NDVI, the albedo values of the surface and surface temperature found show the differences in the coverage of the region, presenting the highest values for farming and cities and the lowest values for conservation units and vegetable extractiveness for both seasons (Fig. 8a–b).
(3) (4) (5)
4. Discussion
where N is the number of observations, Pi is the estimated GPPOLI/TIRS, Oi is the observed GPPMOD, and O are the mean values of Pi and Oi.
The greater variability of values for the use of vegetable extractive and conservation units for the two scenes occurred due to antropic intervention in this use and phenological characteristics of the vegeta tion. The main activities are the extraction of wood, the cultivation of chestnut and extraction of the rubber trees (Hevea brasiliensis Muell. Agr.) (IBGE, 2018a,b). In addition, they are characterized as forest remnants in the shape of fragments in which productivity is reduced also by edge effect, compared to areas with homogeneous vegetation, high lighting the presence of pixels with such low productivity. First, the greater variability of GPP found for GPPOLI/TIRS can occur due to the low resolution of the 500 m MOD17A2H product, which prevents more accurate detection of the targets in the image, compared to the high resolution of the OLI/TIRS sensor, even after resampling. The 30 m GPPOLI/TIRS resolution can capture more clearly the maximum and minimum extremes, which are smoothed by the coarser MODIS
3. Results 3.1. GPP and conservation units The value of GPP is substantially higher for the areas that comprise conservation units with values above 16 g C m 2 d 1 for 2013 (Fig. 2a) and above 12 gC m 2 d 1 in 2014 (Fig. 2b). The conservation units and the vegetal extractiveness presented the highest GPP values in the dry season, with values close to 16 gC m 2 d 1. Close to GPP values were determined for both uses because these are areas with forest cover, but the cities had the highest variation of the data with CV higher than 80%, followed by farming (65%), conservation 6
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Fig. 7. GPPOLI/TIRS and GPPMOD pixel frequency data for 04 August 2013 (a) and 10 October 2014 (b).
resolution. In addition, the MOD17A2H product is a global product and presents reanalysis data in grid points, which limits the specificity of local characteristics (Silva et al., 2013). Despite its limitations, MODIS and other sensors that estimate the GPP are still essential for spatial scale analysis, once there is a great uncertainty of the models that estimate the GPP in spatial scale, since the input of the data is totally compromised (Tramontana et al., 2015). Although the terrestrial carbon dynamics is well known, it still has a great uncertainty of data as the primary source for the models that es timate GPP at a global and/or regional level (Tramontana et al., 2015). By calculating GPP from Landsat TM satellite data in irrigated areas and native vegetation in the cerrado biome in the Federal District, Braga (2013) also found an overestimate in relation to the MOD17A2 product, mainly in the irrigated perimeters, indicating that the algorithm based
on Landsat images can saturate GPP values in regions with high water availability. NDVI saturation in the dry season occurred as the canopy approached its stage of maximum vegetative growth. The NDVI reaches its saturation with high values of the Leaf Area Index (LAI). This pattern can be attributed by the influence of radiance on the atmospheric tra jectory, increasing the brightness and spectral effect of the soil, which interferes with the evaluation of the vegetation (Jensen, 2009) and may explain the high productivity found for the representative image of the dry season. By calculating the radiation balance from Landsat 8 images in the Pajeú River Basin on the border between the states of Cear� a and Paraíba, Alves et al. (2017) observed that albedo values increase together with the surface temperature in different uses. Ruhoff et al. (2015) also with 7
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Fig. 8. Analysis of histograms of the NDVI pixels, temperature and surface albedo for each land cover data for 04 August 2013 (a) and 10 October 2014 (b).
Landsat 8 images, in a hydrographic basin in the Serra do Mar State ~o Paulo, found albedo values for dense ombro Park, in the state of Sa philous forest areas between 0.11 and 0.12, and for urban areas, fields and pastures, they found values of albedo above 0.14, corroborating with the values found in this study. The albedo values correspond to the type of surface coverage where part of the incident energy is absorbed and part is reflected. Areas without vegetation cover have high diffuse reflectivity and low energy absorption, increasing the albedo and consequently the surface temperature. Conservation units and Vegetable extractiveness areas are the re gions that have the largest amounts of carbon, undoubtedly also have the greatest biodiversity of this region, which in fact shows the impor tance of preserving these environments. Knowledge of carbon seques tration in these areas is important to guide climate change mitigation and adaptation.
5. Conclusion The GPP estimated by the Landsat 8 OLI/TIRS satellite made it possible to analyze the spatial distinctions between the land uses in the region. However, it presented similarities of estimation with the MOD17A2H product only for image referring to the rainy period. The image relating to rainy season underestimated the GPPOLI/TIRS in relation to the GPPMOD for all uses, due to lower radiation and surface moisture indexes found for this image. The high resolution of the sat ellite increases the differences of the calculated maximum and minimum values, with a greater variability of surface target. The proposed algo rithm, due to its applicability to Landsat 8 OLI/TIRS satellite, may be useful for more accurate analysis of GPP for local study areas such as hydrographic basins and forest fragments. The focus on carbon sequestration by forest ecosystems and the 8
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reduction of CO2 emissions is the foundation for mitigating climate change damage and consequences at regional and global levels. Currently deforestation of the Amazon rainforest is growing absurdly. Policies contrary to the control of deforestation will only cause accelerated climate change, which will endanger the lives of future generations.
Gitelson, A.A., Vi~ na, A., Masek, J.G., Verma, S.B., Suyker, A.E., 2008. Synoptic monitoring of gross primary productivity of maize using Landsat data. IEEE Geosci. Remote Sens. Lett. 5, 133–137. Gustavsson, L., Haus, S., Ortiz, C.A., Sathre, R., Le Truong, N., 2015. Climate effects of bioenergy from forest residues in comparison to fossil energy. Appl. Energy 138, 36–50. Heinsch, F.A., Reeves, M., Votava, P., Kang, S.Y., Milesi, C., Zhao, M.S., et al., 2003. User’s Guide, GPP and NPP (MOD17A2/A3) Products. NASA MODIS land algorithm [online]. http://www.ntsg.umt.edu/modis/MOD17UsersGuide.pdf. (Accessed 27 March 2014). Huete, A.R., 1988. Adjusting vegetation indices for soil influences. Int. Agrophys. 4, 367–376. IBGE, 2018. Instituto Brasileiro de Geografia e Estatística. Manual t� ecnico da vegetaç~ ao brasileira. https://biblioteca.ibge.gov.br/visualizacao/livros/liv63011.pdf. (Accessed 1 March 2018). IBGE, 2018. Instituto Brasileiro de Geografia e Estatística. http://downloads.ibge.gov. br/downloads_geociencias.htm. Acesso em 01/03/2018. Irons, J.R., Dwyer, J.L., Barsi, J.A., 2012. The next Landsat satellite: the Landsat data continuity mission. Remote Sens. Environ. 122, 11–21. Jensen, J.R., 2009. Sensoriamento remoto do ambiente: uma perspectiva em recursos terrestres. 2a Ediç~ ao traduzida pelo Instituto Nacional de Pesquisas Espaciais–INPE. S~ ao Paulo, p. 672. Par^ entese. LP DAAC, 2018. Land Processes Distributed Active Archive Center. https://lpdaac.usgs. gov/data_access/data_pool. Acesso em 01/03/2018. Manojkumar, N., Srimuruganandam, B., 2019. Assessment of gaseous emissions and radiative forcing in Indian forest fires. Int. J. Environ. Stud. 76, 541–557. Monteith, J.L., 1972. Solar radiation and productivity in tropical ecosystems. J. Appl. Ecol. 9, 747–766. Oliveira, G., Moraes, E.C., Shimabukuro, Y.E., Oliveira, L.E., Arag~ ao, C., Mataveli, G.A. V., 2017. Dados de sensoriamento remoto para o estudo das interaç~ oes biosferaatmosfera em ecossistemas amaz^ onicos: uma revis~ ao. Rev. Bras. Cartogr. 69, 1189–1210. Pullens, J.W.M., Sottocornola, M., Kiely, G., Toscano, P., Gianelle, D., 2016. Carbon fluxes of an alpine peatland in Northern Italy. Agric. For. Meteorol. 220, 69–82. Restrepo-Coupe, N., da Rocha, H.R., Hutyra, L.R., da Araujo, A.C., Borma, L.S., Christoffersen, B., Fitzjarrald, D.R., 2013. What drives the seasonality of photosynthesis across the Amazon basin? A cross-site analysis of eddy flux tower measurements from the Brasil flux network. Agric. For. Meteorol. 182, 128–144. Rouse Jr., J.W., Haas, R.H., Schell, J.A., Deering, D.W., 1973. Monitoring vegetation systems in the great plains with ERTS. In: 3rd ERTS Symposium, NASA, SP-251 I, Washington, pp. 309–317. Roy, D.P., Wulder, M.A., Loveland, T.R., Woodcock, C.E., Allen, R.G., Anderson, M.C., Scambos, T.A., 2014. Landsat-8: Science and product vision for terrestrial global change research. Remote Sens. Environ. 145, 154–172. Ruhoff, A.L., Novo, B.D.S., Rocha, H.D., 2015. Determinaç~ ao da irradi^ ancia solar no topo da atmosfera para c� alculo do albedo e balanço de energia a partir de imagens LANDSAT 8 OLI. Anais Online XVII Simp� osio Brasileiro de Sensoriamento RemotoSBSR. Jo~ ao Pessoa-PB, Brasil, INPE 4496–4502. http://www.dsr.inpe.br/sbsr2015/ files/p0883.pdf. (Accessed 20 February 2019). Sakamoto, T., Gitelson, A.A., Wardlow, B.D., Verma, S.B., Suyker, A.E., 2011. Estimating daily gross primary production of maize based only on MODIS WDRVI and shortwave radiation data. Remote Sens. Environ. 115, 3091–3101. Saleska, S., da Rocha, H., Kruijt, B., Nobre, A., 2009. Fluxos de Carbono do Ecossistema e Metabolismo da Floresta Amaz^ onica. Amazonia Glob. Change Geophys. Monograph Series 186, 389–407. Silva, B.D., Galvíncio, J.D., Montenegro, S.M.G.L., Machado, C.C.C., Oliveira, L.D., Moura, M.D., 2013. Determinaç~ ao por sensoriamento remoto da produtividade prim� aria bruta do perímetro irrigado S~ ao Gonçalo–PB. Revista Brasileira de Meteorologia 28, 57–64. Sims, D.A., Rahman, A.F., Cordova, V.D., El-Masri, B.Z., Baldocchi, D.D., Bolstad, P.V., Monson, R.K., 2008. A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS. Remote Sens. Environ. 112, 1633–1646. Tramontana, G., Ichii, K., Camps-Valls, G., Tomelleri, E., Papale, D., 2015. Uncertainty analysis of gross primary production upscaling using Random Forests, remote sensing and eddy covariance data. Remote Sens. Environ. 168, 360–373. USGS, 2016. Landsat 8 (L8) data users handbook, 2015, LSDS-1574, version 2.0. Sioux Falls, USA: USGS EROS. https://landsat.usgs.gov/documents/Landsat8DataUsers Handbook.pdf. (Accessed 23 June 2016). Wu, C., Niu, Z., Tang, Q., Huang, W., Rivard, B., Feng, J., 2009. Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices. Agric. For. Meteorol. 149, 1015–1021. Zhang, Y., Song, C., Sun, G., Band, L.E., McNulty, S., Noormets, A., Zhang, Z., 2016. Development of a coupled carbon and water model for estimating global gross primary productivity and evapotranspiration based on eddy flux and remote sensing data. Agric. For. Meteorol. 223, 116–131.
Ethical All ethical practices have been followed in relation to the develop ment, writing, and publication of the article entitled Gross primary productivity in areas of different land cover in the western Brazilian Amazon. Acknowledgment We thank the National Council for Scientific and Technological Development - CNPq (nº 442966/2014-8) for funding the project related to this study. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.rsase.2019.100259. References Allen, R., Bastiaanssen, W., Waters, R., Tasumi, M., Trezza, R., 2002. (Surface Energy Balance Algorithms for Land). Advance Training andUsers Manual – IdahoImplementation, p. 97 version 1.0. Almeida, C.T., 2016. Produtividade Prim� aria Bruta na Amaz^ onia Legal: relaç~ ao com vari� aveis meteorol� ogicas e validaç~ ao do produto MOD17A2. Master’s dissertation. Universidade Federal Rural do Rio de Janeiro, Serop� edica, Brasil, p. 77p. Almeida, C.T., Delgado, R.C., Galv~ ao, L.S., Ramos, M.C., 2018. Improvements of the MODIS Gross Primary Productivity model based on a comprehensive uncertainty assessment over the Brazilian Amazonia. ISPRS J. Photogrammetry Remote Sens. 145, 268–283. Alvares, C.A., Stape, J.L., Sentelhas, P.C., de Moraes, G., Leonardo, J., Sparovek, G., 2013. K€ oppen’s climate classification map for Brazil. Meteorol. Z. 22, 711–728. Alves, L.E.R., Gomes, H.B., dos Santos, M.N., de Freitas, I.G.F., 2017. Balanço de radiaç~ ao atrav� es do sat� elite Landsat-8 na bacia do Rio Pajeú. Revista do Departamento de Geografia 33, 117–127. Arag~ ao, L.E., Anderson, L.O., Fonseca, M.G., Rosan, T.M., Vedovato, L.B., Wagner, F.H., Barlow, J., Berenguer, E., Deeter, M.N., Domingues, L.G., Gatti, L., Gloor, M., Yadvinder, M., Marengo, J.A., Miller, J.B., Phillips, O.L., Saatchi, S., 2018. 21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions. Nat. Commun. 9, 1–12. Bastiaanssen, W.G., Ali, S., 2003. A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agric. Ecosyst. Environ. 94, 321–340. Bastiaanssen, W.G., Menenti, M., Feddes, R.A., Holtslag, A.A.M., 1998. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. J. Hydrol. 212, 198–212. Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais, N., Bondeau, A., 2010. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834–838. � Braga, A.P., 2013. Estimativa da Produtividade Prim� aria Bruta em Areas Agrícolas e de Vegetaç~ ao Prim� aria no Cerrado por Sensoriamento Remoto. Master’s dissertation. Universidade Federal de Campina Grande, Campina Grande, p. 66. Brown, D.S., Brown, J.C., Brown, C., 2016. Land occupations and deforestation in the Brazilian Amazon. Land Use Policy 54, 331–338. Chagas, M.C., 2018. Produtividade Prim� aria Bruta na Amaz^ onia Legal: relaç~ ao com vari� aveis meteorol� ogicas e validaç~ ao do produto MOD17A2. Master’s dissertation. Universidade Federal Rural do Rio de Janeiro, Serop� edica, Brasil, p. 53. Cruz-L� opez, M.I., Manzo-Delgado, L.D.L., Aguirre-G� omez, R., Chuvieco, E., EquihuaBenítez, J.A., 2019. Spatial distribution of forest fire emissions: a case study in three Mexican Ecoregions. Remote Sens. 11, 1185. Field, C.B., Randerson, J.T., Malmstrom, C.M., 1995. Global net primary production: combining ecology and remote sensing. Remote Sens. Environ. 51, 74–88.
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