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Abstract
Fire influences global change and tropical ecosystems through its connection to land-cover dynamics, atmospheric composition, and the global carbon cycle. As such, the climate change community, the Brazilian government, and the Large-Scale Biosphere–Atmosphere (LBA) Experiment in Amazonia are interested in the use of satellites to monitor and quantify fire occurrence throughout Brazil. Because multiple satellites and algorithms are being utilized, it is important to quantify the accuracy of the derived products. In this paper the characteristics of two fire detection algorithms are evaluated, both of which are applied to Terra’s Moderate Resolution Imagine Spectroradiometer (MODIS) data and with both operationally producing publicly available fire locations. The two algorithms are NASA’s operational Earth Observing System (EOS) MODIS fire detection product and Brazil’s Instituto Nacional de Pesquisas Espaciais (INPE) algorithm. Both algorithms are compared to fire maps that are derived independently from 30-m spatial resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. A quantitative comparison is accomplished through logistic regression and error matrices. Results show that the likelihood of MODIS fire detection, for either algorithm, is a function of both the number of ASTER fire pixels within the MODIS pixel as well as the contiguity of those pixels. Both algorithms have similar omission errors and each has a fairly high likelihood of detecting relatively small fires, as observed in the ASTER data. However, INPE’s commission error is roughly 3 times more than that of the EOS algorithm.
Abstract
Fire influences global change and tropical ecosystems through its connection to land-cover dynamics, atmospheric composition, and the global carbon cycle. As such, the climate change community, the Brazilian government, and the Large-Scale Biosphere–Atmosphere (LBA) Experiment in Amazonia are interested in the use of satellites to monitor and quantify fire occurrence throughout Brazil. Because multiple satellites and algorithms are being utilized, it is important to quantify the accuracy of the derived products. In this paper the characteristics of two fire detection algorithms are evaluated, both of which are applied to Terra’s Moderate Resolution Imagine Spectroradiometer (MODIS) data and with both operationally producing publicly available fire locations. The two algorithms are NASA’s operational Earth Observing System (EOS) MODIS fire detection product and Brazil’s Instituto Nacional de Pesquisas Espaciais (INPE) algorithm. Both algorithms are compared to fire maps that are derived independently from 30-m spatial resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. A quantitative comparison is accomplished through logistic regression and error matrices. Results show that the likelihood of MODIS fire detection, for either algorithm, is a function of both the number of ASTER fire pixels within the MODIS pixel as well as the contiguity of those pixels. Both algorithms have similar omission errors and each has a fairly high likelihood of detecting relatively small fires, as observed in the ASTER data. However, INPE’s commission error is roughly 3 times more than that of the EOS algorithm.
Abstract
The area of secondary forest (SF) regenerating from pastures is increasing in the Amazon basin; however, the return of forest and canopy structure following abandonment is not well understood. This study examined the development of leaf area index (LAI), canopy cover, aboveground biomass, stem density, diameter at breast height (DBH), and basal area (BA) by growth form and diameter class for 10 SFs regenerating from abandoned pastures. Biomass accrual was tree dominated, constituting ≥94% of the total measured biomass in all forests abandoned ≥4 to 6 yr. Vine biomass increased with forest age, but its relative contribution to total biomass decreased with time. The forests were dominated by the tree Vismia spp. (>50%). Tree stem density peaked after 6 to 8 yr (10 320 stems per hectare) before declining by 42% in the 12- to 14-yr-old SFs. Small-diameter tree stems in the 1–5-cm size class composed >58% of the total stems for all forests. After 12 to 14 yr, there was no significant leaf area below 150-cm height. Leaf area return (LAI = 3.2 after 12 to 14 yr) relative to biomass was slower than literature-reported recovery following slash-and-burn, where LAI can reach primary forest levels (LAI = 4–6) in 5 yr. After 12 to 14 yr, the colonizing vegetation returned some components of forest structure to values reported for primary forest. Basal area and LAI were 50%–60%, canopy cover and stem density were nearly 100%, and the rapid tree-dominated biomass accrual was 25%–50% of values reported for primary forest. Biomass accumulation may reach an asymptote earlier than expected because of even-aged, monospecific, untiered stand structure. The very slow leaf area accumulation relative to biomass and to reported values for recovery following slash-and-burn indicates a different canopy development pathway that warrants further investigation of causes (e.g., nutrient limitations, competition) and effects on processes such as evapotranspiration and soil water uptake, which would influence long-term recovery rates and have regional implications.
Abstract
The area of secondary forest (SF) regenerating from pastures is increasing in the Amazon basin; however, the return of forest and canopy structure following abandonment is not well understood. This study examined the development of leaf area index (LAI), canopy cover, aboveground biomass, stem density, diameter at breast height (DBH), and basal area (BA) by growth form and diameter class for 10 SFs regenerating from abandoned pastures. Biomass accrual was tree dominated, constituting ≥94% of the total measured biomass in all forests abandoned ≥4 to 6 yr. Vine biomass increased with forest age, but its relative contribution to total biomass decreased with time. The forests were dominated by the tree Vismia spp. (>50%). Tree stem density peaked after 6 to 8 yr (10 320 stems per hectare) before declining by 42% in the 12- to 14-yr-old SFs. Small-diameter tree stems in the 1–5-cm size class composed >58% of the total stems for all forests. After 12 to 14 yr, there was no significant leaf area below 150-cm height. Leaf area return (LAI = 3.2 after 12 to 14 yr) relative to biomass was slower than literature-reported recovery following slash-and-burn, where LAI can reach primary forest levels (LAI = 4–6) in 5 yr. After 12 to 14 yr, the colonizing vegetation returned some components of forest structure to values reported for primary forest. Basal area and LAI were 50%–60%, canopy cover and stem density were nearly 100%, and the rapid tree-dominated biomass accrual was 25%–50% of values reported for primary forest. Biomass accumulation may reach an asymptote earlier than expected because of even-aged, monospecific, untiered stand structure. The very slow leaf area accumulation relative to biomass and to reported values for recovery following slash-and-burn indicates a different canopy development pathway that warrants further investigation of causes (e.g., nutrient limitations, competition) and effects on processes such as evapotranspiration and soil water uptake, which would influence long-term recovery rates and have regional implications.
Abstract
The Brazilian Amazon forest and cerrado savanna encompasses a region of enormous ecological, climatic, and land-use variation. Satellite remote sensing is the only tractable means to measure the biophysical attributes of vegetation throughout this region, but coarse-resolution sensors cannot resolve the details of forest structure and land-cover change deemed critical to many land-use, ecological, and conservation-oriented studies. The Carnegie Landsat Analysis System (CLAS) was developed for studies of forest and savanna structural attributes using widely available Landsat Enhanced Thematic Mapper Plus (ETM+) satellite data and advanced methods in automated spectral mixture analysis. The methodology of the CLAS approach is presented along with a study of its sensitivity to atmospheric correction errors. CLAS is then applied to a mosaic of Landsat images spanning the years 1999–2001 as a proof of concept and capability for large-scale, very high resolution mapping of the Amazon and bordering cerrado savanna. A total of 197 images were analyzed for fractional photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and bare substrate covers using a probabilistic spectral mixture model. Results from areas without significant land use, clouds, cloud shadows, and water bodies were compiled by the Brazilian state and vegetation class to understand the baseline structural typology of forests and savannas using this new system. Conversion of the satellite-derived PV data to woody canopy gap fraction was made to highlight major differences by vegetation and ecosystem classes. The results indicate important differences in fractional photosynthetic cover and canopy gap fraction that can now be accounted for in future studies of land-cover change, ecological variability, and biogeochemical processes across the Amazon and bordering cerrado regions of Brazil.
Abstract
The Brazilian Amazon forest and cerrado savanna encompasses a region of enormous ecological, climatic, and land-use variation. Satellite remote sensing is the only tractable means to measure the biophysical attributes of vegetation throughout this region, but coarse-resolution sensors cannot resolve the details of forest structure and land-cover change deemed critical to many land-use, ecological, and conservation-oriented studies. The Carnegie Landsat Analysis System (CLAS) was developed for studies of forest and savanna structural attributes using widely available Landsat Enhanced Thematic Mapper Plus (ETM+) satellite data and advanced methods in automated spectral mixture analysis. The methodology of the CLAS approach is presented along with a study of its sensitivity to atmospheric correction errors. CLAS is then applied to a mosaic of Landsat images spanning the years 1999–2001 as a proof of concept and capability for large-scale, very high resolution mapping of the Amazon and bordering cerrado savanna. A total of 197 images were analyzed for fractional photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and bare substrate covers using a probabilistic spectral mixture model. Results from areas without significant land use, clouds, cloud shadows, and water bodies were compiled by the Brazilian state and vegetation class to understand the baseline structural typology of forests and savannas using this new system. Conversion of the satellite-derived PV data to woody canopy gap fraction was made to highlight major differences by vegetation and ecosystem classes. The results indicate important differences in fractional photosynthetic cover and canopy gap fraction that can now be accounted for in future studies of land-cover change, ecological variability, and biogeochemical processes across the Amazon and bordering cerrado regions of Brazil.
Abstract
The Brazilian government annually assesses the extent of deforestation in the Legal Amazon for a variety of scientific and policy applications. Currently, the assessment requires the processing and storing of large volumes of Landsat satellite data. The potential for efficient, accurate, and less data-intensive assessment of annual deforestation using data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) at 250-m resolution is evaluated. Landsat-derived deforestation estimates are compared to MODIS-derived estimates for six Landsat scenes with five change-detection algorithms and a variety of input data—Surface Reflectance (MOD09), Vegetation Indices (MOD13), fraction images derived from a linear mixing model, Vegetation Cover Conversion (MOD44A), and percent tree cover from the Vegetation Continuous Fields (MOD44B) product. Several algorithms generated consistently low commission errors (positive predictive value near 90%) and identified more than 80% of deforestation polygons larger than 3 ha. All methods accurately identified polygons larger than 20 ha. However, no method consistently detected a high percent of Landsat-derived deforestation area across all six scenes. Field validation in central Mato Grosso confirmed that all MODIS-derived deforestation clusters larger than three 250-m pixels were true deforestation. Application of this field-validated method to the state of Mato Grosso for 2001–04 highlighted a change in deforestation dynamics; the number of large clusters (>10 MODIS pixels) that were detected doubled, from 750 between August 2001 and August 2002 to over 1500 between August 2003 and August 2004. These analyses demonstrate that MODIS data are appropriate for rapid identification of the location of deforestation areas and trends in deforestation dynamics with greatly reduced storage and processing requirements compared to Landsat-derived assessments. However, the MODIS-based analyses evaluated in this study are not a replacement for high-resolution analyses that estimate the total area of deforestation and identify small clearings.
Abstract
The Brazilian government annually assesses the extent of deforestation in the Legal Amazon for a variety of scientific and policy applications. Currently, the assessment requires the processing and storing of large volumes of Landsat satellite data. The potential for efficient, accurate, and less data-intensive assessment of annual deforestation using data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) at 250-m resolution is evaluated. Landsat-derived deforestation estimates are compared to MODIS-derived estimates for six Landsat scenes with five change-detection algorithms and a variety of input data—Surface Reflectance (MOD09), Vegetation Indices (MOD13), fraction images derived from a linear mixing model, Vegetation Cover Conversion (MOD44A), and percent tree cover from the Vegetation Continuous Fields (MOD44B) product. Several algorithms generated consistently low commission errors (positive predictive value near 90%) and identified more than 80% of deforestation polygons larger than 3 ha. All methods accurately identified polygons larger than 20 ha. However, no method consistently detected a high percent of Landsat-derived deforestation area across all six scenes. Field validation in central Mato Grosso confirmed that all MODIS-derived deforestation clusters larger than three 250-m pixels were true deforestation. Application of this field-validated method to the state of Mato Grosso for 2001–04 highlighted a change in deforestation dynamics; the number of large clusters (>10 MODIS pixels) that were detected doubled, from 750 between August 2001 and August 2002 to over 1500 between August 2003 and August 2004. These analyses demonstrate that MODIS data are appropriate for rapid identification of the location of deforestation areas and trends in deforestation dynamics with greatly reduced storage and processing requirements compared to Landsat-derived assessments. However, the MODIS-based analyses evaluated in this study are not a replacement for high-resolution analyses that estimate the total area of deforestation and identify small clearings.
Abstract
The “cerrado” biome in central Brazil is rapidly being converted into pasture and agricultural crops with important consequences for local and regional climate change and regional carbon fluxes between the atmosphere and land surface. Satellite remote sensing provides an opportunity to monitor the highly diverse and complex cerrado biome, encompassing grassland, shrubland, woodland and gallery forests, and converted areas. In this study, the potential of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data is analyzed to discriminate among these diverse cerrado physiognomies and converted pastures based on their seasonal dynamics and phenology. Four years (2000–03) of MODIS 16-day composited, 250-m resolution vegetation index (VI) data were extracted over a series of biophysically sampled field study sites representing the major cerrado types. The temporal VI profiles over the cerrado formations exhibited high seasonal contrasts with a pronounced dry season from June to August and a wet growing season from November to March. The converted pasture areas showed the highest seasonal contrasts while the gallery forest formation had the lowest contrast. Seasonal VI variations were negatively correlated with woody canopy crown cover and provided a method to discriminate among converted cerrado areas, gallery forests, and the woody and herbaceous cerrado formations. The grassland and shrub cerrado formations, however, were difficult to separate based on their seasonal VI profiles. Maximum discrimination among the cerrado types occurred during the dry season where a positive linear relationship was found between VI and green cover. The annual integrated VI values showed the gallery forests and cerrado woodland as having the highest, and hence most annual productivity, while the more herbaceous shrub and grassland cerrado types were least productive. The cumulative VI profiles of converted cerrado, pasture areas varied distinctly in shape due to their strong dry season inactivity. Furthermore, the annual integrated VI values of the converted pastures differed significantly between the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) MODIS VI products, resulting in large discrepancies in productivity estimates relative to the native cerrado sites. This study shows that the MODIS seasonal–temporal VI profiles are highly useful in monitoring the cerrado biome and conversion-related activities.
Abstract
The “cerrado” biome in central Brazil is rapidly being converted into pasture and agricultural crops with important consequences for local and regional climate change and regional carbon fluxes between the atmosphere and land surface. Satellite remote sensing provides an opportunity to monitor the highly diverse and complex cerrado biome, encompassing grassland, shrubland, woodland and gallery forests, and converted areas. In this study, the potential of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data is analyzed to discriminate among these diverse cerrado physiognomies and converted pastures based on their seasonal dynamics and phenology. Four years (2000–03) of MODIS 16-day composited, 250-m resolution vegetation index (VI) data were extracted over a series of biophysically sampled field study sites representing the major cerrado types. The temporal VI profiles over the cerrado formations exhibited high seasonal contrasts with a pronounced dry season from June to August and a wet growing season from November to March. The converted pasture areas showed the highest seasonal contrasts while the gallery forest formation had the lowest contrast. Seasonal VI variations were negatively correlated with woody canopy crown cover and provided a method to discriminate among converted cerrado areas, gallery forests, and the woody and herbaceous cerrado formations. The grassland and shrub cerrado formations, however, were difficult to separate based on their seasonal VI profiles. Maximum discrimination among the cerrado types occurred during the dry season where a positive linear relationship was found between VI and green cover. The annual integrated VI values showed the gallery forests and cerrado woodland as having the highest, and hence most annual productivity, while the more herbaceous shrub and grassland cerrado types were least productive. The cumulative VI profiles of converted cerrado, pasture areas varied distinctly in shape due to their strong dry season inactivity. Furthermore, the annual integrated VI values of the converted pastures differed significantly between the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) MODIS VI products, resulting in large discrepancies in productivity estimates relative to the native cerrado sites. This study shows that the MODIS seasonal–temporal VI profiles are highly useful in monitoring the cerrado biome and conversion-related activities.
Abstract
In the Brazilian Amazon, selective logging is second only to forest conversion in its extent. Conversion to pasture or agriculture tends to reduce soil nutrients and site productivity over time unless fertilizers are added. Logging removes nutrients in bole wood, enough that repeated logging could deplete essential nutrients over time. After a single logging event, nutrient losses are likely to be too small to observe in the large soil nutrient pools, but disturbances associated with logging also alter soil properties. Selective logging, particularly reduced-impact logging, results in consistent patterns of disturbance that may be associated with particular changes in soil properties. Soil bulk density, pH, carbon (C), nitrogen (N), phosphorus (P), calcium (Ca), magnesium (Mg), potassium (K), iron (Fe), aluminum (Al), δ 13C, δ 15N, and P fractionations were measured on the soils of four different types of logging-related disturbances: roads, decks, skids, and treefall gaps. Litter biomass and percent bare ground were also determined in these areas. To evaluate the importance of fresh foliage inputs from downed tree crowns in treefall gaps, foliar nutrients for mature forest trees were also determined and compared to that of fresh litterfall. The immediate impacts of logging on soil properties and how these might link to the longer-term estimated nutrient losses and the observed changes in soils were studied.
In the most disturbed areas, roads and decks, the authors found litter biomass removed and reduced soil C, N, P, particularly organic P, and δ 13C. Soils were compacted and often experienced reducing conditions in the deck areas, resulting in higher pH, Ca, and Mg. No increases in soil nutrients were observed in the treefall gaps despite the flush of nutrient-rich fresh foliage in the tree crown that is left behind after the bole wood is removed. Observed nutrient losses are most likely caused by displacement of the litter layer. Increases in soil pH, Ca, and Mg occur in areas with reducing conditions (decks and roads) and may result from Fe reduction, freeing exchange sites that can then retain these cations. Calculations suggest that nutrient inputs from crown foliage in treefall gaps are probably too small to detect against the background level of nutrients in the top soils. The logging disturbances with the greatest spatial extent, skids and gaps, have the smallest immediate effect on soil nutrients, while those with the smallest spatial extent, roads and decks, have the largest impact. The changes observed 3–6 months after logging were similar to those measured 16 yr after logging, suggesting some interesting linkages between the mechanisms causing the immediate change and those maintaining these changes over time. The direct impacts on soil properties appear less important than the loss of nutrients in bole wood in determining the sustainability of selective logging. Medium-to-low intensity selective logging with a sufficiently long cutting cycle may be sustainable in these forests.
Abstract
In the Brazilian Amazon, selective logging is second only to forest conversion in its extent. Conversion to pasture or agriculture tends to reduce soil nutrients and site productivity over time unless fertilizers are added. Logging removes nutrients in bole wood, enough that repeated logging could deplete essential nutrients over time. After a single logging event, nutrient losses are likely to be too small to observe in the large soil nutrient pools, but disturbances associated with logging also alter soil properties. Selective logging, particularly reduced-impact logging, results in consistent patterns of disturbance that may be associated with particular changes in soil properties. Soil bulk density, pH, carbon (C), nitrogen (N), phosphorus (P), calcium (Ca), magnesium (Mg), potassium (K), iron (Fe), aluminum (Al), δ 13C, δ 15N, and P fractionations were measured on the soils of four different types of logging-related disturbances: roads, decks, skids, and treefall gaps. Litter biomass and percent bare ground were also determined in these areas. To evaluate the importance of fresh foliage inputs from downed tree crowns in treefall gaps, foliar nutrients for mature forest trees were also determined and compared to that of fresh litterfall. The immediate impacts of logging on soil properties and how these might link to the longer-term estimated nutrient losses and the observed changes in soils were studied.
In the most disturbed areas, roads and decks, the authors found litter biomass removed and reduced soil C, N, P, particularly organic P, and δ 13C. Soils were compacted and often experienced reducing conditions in the deck areas, resulting in higher pH, Ca, and Mg. No increases in soil nutrients were observed in the treefall gaps despite the flush of nutrient-rich fresh foliage in the tree crown that is left behind after the bole wood is removed. Observed nutrient losses are most likely caused by displacement of the litter layer. Increases in soil pH, Ca, and Mg occur in areas with reducing conditions (decks and roads) and may result from Fe reduction, freeing exchange sites that can then retain these cations. Calculations suggest that nutrient inputs from crown foliage in treefall gaps are probably too small to detect against the background level of nutrients in the top soils. The logging disturbances with the greatest spatial extent, skids and gaps, have the smallest immediate effect on soil nutrients, while those with the smallest spatial extent, roads and decks, have the largest impact. The changes observed 3–6 months after logging were similar to those measured 16 yr after logging, suggesting some interesting linkages between the mechanisms causing the immediate change and those maintaining these changes over time. The direct impacts on soil properties appear less important than the loss of nutrients in bole wood in determining the sustainability of selective logging. Medium-to-low intensity selective logging with a sufficiently long cutting cycle may be sustainable in these forests.
