Browse
Abstract
Selective logging degrades tropical forests. Logging operations vary in timing, location, and intensity. Evidence of this land use is rapidly obscured by forest regeneration and ongoing deforestation. A detailed study of selective logging operations was conducted near Sinop, State of Mato Grosso, Brazil, one of the key Amazonian logging centers. An 11-yr series of annual Lansdat images (1992–2002) was used to detect and track logged forests across the landscape. A semiautomated method was applied and compared to both visual interpretation and field data. Although visual detection provided precise delineation of some logged areas, it missed many areas. The semiautomated technique provided the best estimates of logging extent that are largely independent of potential user bias. Multitemporal analyses allowed the authors to analyze the annual variations in logging and deforestation, as well as the interaction between them. It is shown that, because of both rapid regrowth and deforestation, evidence of logging activities often disappeared within 1–3 yr. During the 1992–2002 interval, a total of 11 449 km2 of forest was selectively logged. Around 17% of these logged forests had been deforested by 2002. An intra-annual analysis was also conducted using four images spread over a single year. Nearly 3% of logged forests were rapidly deforested during the year in which logging occurred, indicating that even annual monitoring will underestimate logging extent. Great care will need to be taken when inferring logging rates from observations greater than a year apart because of the partial detection of previous years of logging activity.
Abstract
Selective logging degrades tropical forests. Logging operations vary in timing, location, and intensity. Evidence of this land use is rapidly obscured by forest regeneration and ongoing deforestation. A detailed study of selective logging operations was conducted near Sinop, State of Mato Grosso, Brazil, one of the key Amazonian logging centers. An 11-yr series of annual Lansdat images (1992–2002) was used to detect and track logged forests across the landscape. A semiautomated method was applied and compared to both visual interpretation and field data. Although visual detection provided precise delineation of some logged areas, it missed many areas. The semiautomated technique provided the best estimates of logging extent that are largely independent of potential user bias. Multitemporal analyses allowed the authors to analyze the annual variations in logging and deforestation, as well as the interaction between them. It is shown that, because of both rapid regrowth and deforestation, evidence of logging activities often disappeared within 1–3 yr. During the 1992–2002 interval, a total of 11 449 km2 of forest was selectively logged. Around 17% of these logged forests had been deforested by 2002. An intra-annual analysis was also conducted using four images spread over a single year. Nearly 3% of logged forests were rapidly deforested during the year in which logging occurred, indicating that even annual monitoring will underestimate logging extent. Great care will need to be taken when inferring logging rates from observations greater than a year apart because of the partial detection of previous years of logging activity.
Abstract
Selective logging is an extensive land use in the Brazilian Amazon region. The soil–atmosphere fluxes of nitrous oxide (N2O), nitric oxide (NO), methane (CH4), and carbon dioxide (CO2) are studied on two soil types (clay Oxisol and sandy loam Ultisol) over two years (2000–01) in both undisturbed forest and forest recently logged using reduced impact forest management in the Tapajos National Forest, near Santarem, Para, Brazil. In undisturbed forest, annual soil–atmosphere fluxes of N2O (mean ± standard error) were 7.9 ± 0.7 and 7.0 ± 0.6 ng N cm−2 h−1 for the Oxisol and 1.7 ± 0.1 and 1.6 ± 0.3 ng N cm−2 h−1 for the Ultisol for 2000 and 2001, respectively. The annual fluxes of NO from undisturbed forest soil in 2001 were 9.0 ± 2.8 ng N cm−2 h−1 for the Oxisol and 8.8 ± 5.0 ng N cm−2 h−1 for the Ultisol. Consumption of CH4 from the atmosphere dominated over production on undisturbed forest soils. Fluxes averaged −0.3 ± 0.2 and −0.1 ± 0.9 mg CH4 m−2 day−1 on the Oxisol and −1.0 ± 0.2 and −0.9 ± 0.3 mg CH4 m−2 day−1 on the Ultisol for years 2000 and 2001. For CO2 in 2001, the annual fluxes averaged 3.6 ± 0.4 μmol m−2 s−1 on the Oxisol and 4.9 ± 1.1 μmol m−2 s−1 on the Ultisol. We measured fluxes over one year each from two recently logged forests on the Oxisol in 2000 and on the Ultisol in 2001. Sampling in logged areas was stratified from greatest to least ground disturbance covering log decks, skid trails, tree-fall gaps, and forest matrix. Areas of strong soil compaction, especially the skid trails and logging decks, were prone to significantly greater emissions of N2O, NO, and especially CH4. In the case of CH4, estimated annual emissions from decks reached extremely high rates of 531 ± 419 and 98 ± 41 mg CH4 m−2 day−1, for Oxisol and Ultisol sites, respectively, comparable to wetland emissions in the region. We calculated excess fluxes from logged areas by subtraction of a background forest matrix or undisturbed forest flux and adjusted these fluxes for the proportional area of ground disturbance. Our calculations suggest that selective logging increases emissions of N2O and NO from 30% to 350% depending upon conditions. While undisturbed forest was a CH4 sink, logged forest tended to emit methane at moderate rates. Soil–atmosphere CO2 fluxes were only slightly affected by logging. The regional effects of logging cannot be simply extrapolated based upon one site. We studied sites where reduced impact harvest management was used while in typical conventional logging ground damage is twice as great. Even so, our results indicate that for N2O, NO, and CH4, logging disturbance may be as important for regional budgets of these gases as other extensive land-use changes in the Amazon such as the conversion of forest to cattle pasture.
Abstract
Selective logging is an extensive land use in the Brazilian Amazon region. The soil–atmosphere fluxes of nitrous oxide (N2O), nitric oxide (NO), methane (CH4), and carbon dioxide (CO2) are studied on two soil types (clay Oxisol and sandy loam Ultisol) over two years (2000–01) in both undisturbed forest and forest recently logged using reduced impact forest management in the Tapajos National Forest, near Santarem, Para, Brazil. In undisturbed forest, annual soil–atmosphere fluxes of N2O (mean ± standard error) were 7.9 ± 0.7 and 7.0 ± 0.6 ng N cm−2 h−1 for the Oxisol and 1.7 ± 0.1 and 1.6 ± 0.3 ng N cm−2 h−1 for the Ultisol for 2000 and 2001, respectively. The annual fluxes of NO from undisturbed forest soil in 2001 were 9.0 ± 2.8 ng N cm−2 h−1 for the Oxisol and 8.8 ± 5.0 ng N cm−2 h−1 for the Ultisol. Consumption of CH4 from the atmosphere dominated over production on undisturbed forest soils. Fluxes averaged −0.3 ± 0.2 and −0.1 ± 0.9 mg CH4 m−2 day−1 on the Oxisol and −1.0 ± 0.2 and −0.9 ± 0.3 mg CH4 m−2 day−1 on the Ultisol for years 2000 and 2001. For CO2 in 2001, the annual fluxes averaged 3.6 ± 0.4 μmol m−2 s−1 on the Oxisol and 4.9 ± 1.1 μmol m−2 s−1 on the Ultisol. We measured fluxes over one year each from two recently logged forests on the Oxisol in 2000 and on the Ultisol in 2001. Sampling in logged areas was stratified from greatest to least ground disturbance covering log decks, skid trails, tree-fall gaps, and forest matrix. Areas of strong soil compaction, especially the skid trails and logging decks, were prone to significantly greater emissions of N2O, NO, and especially CH4. In the case of CH4, estimated annual emissions from decks reached extremely high rates of 531 ± 419 and 98 ± 41 mg CH4 m−2 day−1, for Oxisol and Ultisol sites, respectively, comparable to wetland emissions in the region. We calculated excess fluxes from logged areas by subtraction of a background forest matrix or undisturbed forest flux and adjusted these fluxes for the proportional area of ground disturbance. Our calculations suggest that selective logging increases emissions of N2O and NO from 30% to 350% depending upon conditions. While undisturbed forest was a CH4 sink, logged forest tended to emit methane at moderate rates. Soil–atmosphere CO2 fluxes were only slightly affected by logging. The regional effects of logging cannot be simply extrapolated based upon one site. We studied sites where reduced impact harvest management was used while in typical conventional logging ground damage is twice as great. Even so, our results indicate that for N2O, NO, and CH4, logging disturbance may be as important for regional budgets of these gases as other extensive land-use changes in the Amazon such as the conversion of forest to cattle pasture.
Abstract
Tropical forests are being subjected to a wide array of disturbances in addition to outright deforestation. Selective logging is one of the most common disturbances ongoing in the Amazon, which results in significant changes in forest structure and canopy integrity. Assessing forest canopy fractional cover (fc) is one way of measuring forest degradation caused by selective logging. In this study we applied a linear mixture model to a vegetation index domain to map canopy fractional cover in tropical forests in the Amazonian state of Mato Grosso, Brazil. The modified soil adjusted vegetation index (MSAVI) was selected as the optimal vegetation index in the model because it is most linearly related to green canopy abundance up to leaf area index = 4.0. In the canopy fc map derived from the Landsat Enhanced Thematic Mapper Plus (ETM+) image, the fc distribution ranged from 0 to 0.4 in clear-cut areas, higher than 0.8 in undisturbed forests, and a wider range of 0.3–1.0 in degraded forests. The fc map was validated with the 1-m panchromatic sharpened IKONOS image. In the logged forests the ETM+ estimated fc values were clustered along the 1:1 line in the scatterplot with the IKONOS estimated fc and had a squared correlation coefficient (R 2) of 0.8.
Abstract
Tropical forests are being subjected to a wide array of disturbances in addition to outright deforestation. Selective logging is one of the most common disturbances ongoing in the Amazon, which results in significant changes in forest structure and canopy integrity. Assessing forest canopy fractional cover (fc) is one way of measuring forest degradation caused by selective logging. In this study we applied a linear mixture model to a vegetation index domain to map canopy fractional cover in tropical forests in the Amazonian state of Mato Grosso, Brazil. The modified soil adjusted vegetation index (MSAVI) was selected as the optimal vegetation index in the model because it is most linearly related to green canopy abundance up to leaf area index = 4.0. In the canopy fc map derived from the Landsat Enhanced Thematic Mapper Plus (ETM+) image, the fc distribution ranged from 0 to 0.4 in clear-cut areas, higher than 0.8 in undisturbed forests, and a wider range of 0.3–1.0 in degraded forests. The fc map was validated with the 1-m panchromatic sharpened IKONOS image. In the logged forests the ETM+ estimated fc values were clustered along the 1:1 line in the scatterplot with the IKONOS estimated fc and had a squared correlation coefficient (R 2) of 0.8.
Abstract
Although large-scale atmospheric reanalyses are now providing physical, realistic fields for many variables, precipitation remains problematic. As shown in recent studies, using a regional model to downscale the global reanalysis only marginally alleviates the precipitation simulation problems. For this reason, later-generation analyses, including the recent National Centers for Environmental Prediction regional reanalysis, are using precipitation assimilation as a methodology to provide superior precipitation fields. This methodology can also be applied to regional model simulations to substantially improve the precipitation fields downscaled from global reanalysis. As an illustration of the regional model precipitation assimilation impact, the authors describe here an extended-range simulation comparison over South America. The numerical experiments cover the beginning of the Large-Scale Biosphere–Atmosphere wet season campaign of January 1999. Evaluations using radiosonde datasets obtained during this campaign are provided as well. As will be shown, rain-rate assimilation not only increases the regional model precipitation simulation skill but also provides improvements in other fields influenced by the precipitation. Because of the potential impact on land surface features, the authors believe they will ultimately be able to show improvements in monthly to seasonal forecasts when precipitation assimilation is used to generate more accurate land surface initial conditions.
Abstract
Although large-scale atmospheric reanalyses are now providing physical, realistic fields for many variables, precipitation remains problematic. As shown in recent studies, using a regional model to downscale the global reanalysis only marginally alleviates the precipitation simulation problems. For this reason, later-generation analyses, including the recent National Centers for Environmental Prediction regional reanalysis, are using precipitation assimilation as a methodology to provide superior precipitation fields. This methodology can also be applied to regional model simulations to substantially improve the precipitation fields downscaled from global reanalysis. As an illustration of the regional model precipitation assimilation impact, the authors describe here an extended-range simulation comparison over South America. The numerical experiments cover the beginning of the Large-Scale Biosphere–Atmosphere wet season campaign of January 1999. Evaluations using radiosonde datasets obtained during this campaign are provided as well. As will be shown, rain-rate assimilation not only increases the regional model precipitation simulation skill but also provides improvements in other fields influenced by the precipitation. Because of the potential impact on land surface features, the authors believe they will ultimately be able to show improvements in monthly to seasonal forecasts when precipitation assimilation is used to generate more accurate land surface initial conditions.
Abstract
In this study, statistical multitemporal analysis was applied to evaluate the capability of reflectance, vegetation indices [normalized difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI)], normalized difference infrared indices (NDII5 and NDII7), and fraction images, derived from spectral mixture analysis (SMA), to distinguish intact forest from four classes of degraded forests: nonmechanized logging, managed logging, conventional logging, and logged and burned. For this purpose, a robust time series dataset of Landsat Thematic Mapper 5/Enhanced Thematic Mapper (TM/ETM+) images was used in conjunction with forest inventory transects and data on disturbance history. The study area is located near two important sawmill centers—Sinop and Claúdia, in Mato Grosso State—in the southern Brazilian Amazon. Most of the remote sensing measures tested to distinguish intact forest from degraded forests showed statistically significant changes. Fraction images, particularly green vegetation (GV) and nonphotosynthetic vegetation (NPV), were the most effective means tested for identifying conventional logging and logged and burned forest in the region. The GV change, detected from intact forest to conventional logging and logged and burned forest classes, persists no more than 1 yr, but the NPV change is still significantly different for up to 2 yr. In the second and third years following a degradation event, a significant regeneration change signal was observed in reflectance and fraction images, which can be useful for identifying these types of forest disturbances in areas where optical satellite images cannot be acquired every year.
Abstract
In this study, statistical multitemporal analysis was applied to evaluate the capability of reflectance, vegetation indices [normalized difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI)], normalized difference infrared indices (NDII5 and NDII7), and fraction images, derived from spectral mixture analysis (SMA), to distinguish intact forest from four classes of degraded forests: nonmechanized logging, managed logging, conventional logging, and logged and burned. For this purpose, a robust time series dataset of Landsat Thematic Mapper 5/Enhanced Thematic Mapper (TM/ETM+) images was used in conjunction with forest inventory transects and data on disturbance history. The study area is located near two important sawmill centers—Sinop and Claúdia, in Mato Grosso State—in the southern Brazilian Amazon. Most of the remote sensing measures tested to distinguish intact forest from degraded forests showed statistically significant changes. Fraction images, particularly green vegetation (GV) and nonphotosynthetic vegetation (NPV), were the most effective means tested for identifying conventional logging and logged and burned forest in the region. The GV change, detected from intact forest to conventional logging and logged and burned forest classes, persists no more than 1 yr, but the NPV change is still significantly different for up to 2 yr. In the second and third years following a degradation event, a significant regeneration change signal was observed in reflectance and fraction images, which can be useful for identifying these types of forest disturbances in areas where optical satellite images cannot be acquired every year.
Abstract
Carbon flux of Amazonian primary forest vegetation has been shown to vary both spatially and temporally. Process-based models are adequate tools to understand the basis of such variation and can also provide projections to future scenarios. The parameterization of such process-based models requires information from the vegetation in question simply because ecosystem-level gas exchange is a direct result of the tightly coupled interaction between local vegetation and regional climate. In this study, data are presented concerning canopy structure [leaf area index (LAI), and the ratio of leaf dry mass to leaf area (LMA)], leaf chemistry [area-based foliar nitrogen content (Narea) and carbon isotope composition (δ 13C)], and photosynthetic gas exchange [maximum carbon assimilation rates (A max), stomatal conductance (gs @A max), maximum carboxylation capacity (Vc max), and respiration rates (Rd )] versus relative height from an extensive survey of primary forest vegetation of the Santarém region (eastern Amazon, Santarém, Federal State of Pará, Brazil). Ground-level LAI values ranged between 4.5 and 5.9. Both A max and Vc max showed large variations within the canopy profile with values ranging between 2.4 and 15.7 μmol m−2 s−1 and between 10.1 and 105.7 μmol m−2 s−1, respectively. Also, N area varied between 0.75 to 4.19 gN m−2, and similar to A max and Vc max, showed higher values at the top of the canopy. Variations were detected among sites in patterns of vertical distribution of N area and LAI, indicating spatial heterogeneity of the forest. Also, no statistically significant evidence of seasonal variations on parameters was observed, indicating that there is limited gas exchange acclimation by the vegetation to wet or dry seasons.
Abstract
Carbon flux of Amazonian primary forest vegetation has been shown to vary both spatially and temporally. Process-based models are adequate tools to understand the basis of such variation and can also provide projections to future scenarios. The parameterization of such process-based models requires information from the vegetation in question simply because ecosystem-level gas exchange is a direct result of the tightly coupled interaction between local vegetation and regional climate. In this study, data are presented concerning canopy structure [leaf area index (LAI), and the ratio of leaf dry mass to leaf area (LMA)], leaf chemistry [area-based foliar nitrogen content (Narea) and carbon isotope composition (δ 13C)], and photosynthetic gas exchange [maximum carbon assimilation rates (A max), stomatal conductance (gs @A max), maximum carboxylation capacity (Vc max), and respiration rates (Rd )] versus relative height from an extensive survey of primary forest vegetation of the Santarém region (eastern Amazon, Santarém, Federal State of Pará, Brazil). Ground-level LAI values ranged between 4.5 and 5.9. Both A max and Vc max showed large variations within the canopy profile with values ranging between 2.4 and 15.7 μmol m−2 s−1 and between 10.1 and 105.7 μmol m−2 s−1, respectively. Also, N area varied between 0.75 to 4.19 gN m−2, and similar to A max and Vc max, showed higher values at the top of the canopy. Variations were detected among sites in patterns of vertical distribution of N area and LAI, indicating spatial heterogeneity of the forest. Also, no statistically significant evidence of seasonal variations on parameters was observed, indicating that there is limited gas exchange acclimation by the vegetation to wet or dry seasons.
Abstract
Mechanized agriculture is rapidly expanding in the state of Mato Grosso, Brazil. In the past five years, land area planted with soybeans, the state’s principal crop, has increased at an average rate of 19.4% yr−1. Drivers of this large-scale land-use conversion are principally economic and sociopolitical, but physical properties of the landscape make some areas more attractive than others for expansion of mechanized agriculture. The goal of this study is to evaluate several physical characteristics of land in Mato Grosso and to quantify their respective weights in determining the likelihood of land-use conversion to crop production. A 2003 land-cover classification at 250-m resolution was compared to maps of five physical landscape characteristics (surface slope, soil type, total November precipitation, distance from paved roads, and previous land-cover type based on a 2001 classification). A land-cover transition matrix was generated to inform analysis of the role of previous land-cover type, and statewide distributions of the other four landscape characteristics were examined across agricultural and nonagricultural land. Finally, logistic regressions were performed to quantify the respective correlations of these various characteristics with the probability of conversion to mechanized agriculture. Areas of new cropland in 2003 (converted since the 2001 classification) were nearly 3 times as likely to have been converted from pasture/cerrado as from all other land-cover types combined, but in terms of class original extent, bare soil was by far the most likely class to be converted to cropland, with 56% of its 2001 land area being converted by 2003. The physical landscape parameter found most highly correlated with conversion to mechanized agriculture between 2001 and 2003 was that of the previous land-cover type, followed by topographic slope and distance from paved roads. Soil type and total November precipitation were poorly correlated with mechanized agriculture. Findings from this study suggest that holistic, spatially explicit models of likelihood of conversion to mechanized agriculture should consider land cover, slope, and proximity to main roads in addition to political and economic parameters to generate realistic scenarios for sustainable land-use planning.
Abstract
Mechanized agriculture is rapidly expanding in the state of Mato Grosso, Brazil. In the past five years, land area planted with soybeans, the state’s principal crop, has increased at an average rate of 19.4% yr−1. Drivers of this large-scale land-use conversion are principally economic and sociopolitical, but physical properties of the landscape make some areas more attractive than others for expansion of mechanized agriculture. The goal of this study is to evaluate several physical characteristics of land in Mato Grosso and to quantify their respective weights in determining the likelihood of land-use conversion to crop production. A 2003 land-cover classification at 250-m resolution was compared to maps of five physical landscape characteristics (surface slope, soil type, total November precipitation, distance from paved roads, and previous land-cover type based on a 2001 classification). A land-cover transition matrix was generated to inform analysis of the role of previous land-cover type, and statewide distributions of the other four landscape characteristics were examined across agricultural and nonagricultural land. Finally, logistic regressions were performed to quantify the respective correlations of these various characteristics with the probability of conversion to mechanized agriculture. Areas of new cropland in 2003 (converted since the 2001 classification) were nearly 3 times as likely to have been converted from pasture/cerrado as from all other land-cover types combined, but in terms of class original extent, bare soil was by far the most likely class to be converted to cropland, with 56% of its 2001 land area being converted by 2003. The physical landscape parameter found most highly correlated with conversion to mechanized agriculture between 2001 and 2003 was that of the previous land-cover type, followed by topographic slope and distance from paved roads. Soil type and total November precipitation were poorly correlated with mechanized agriculture. Findings from this study suggest that holistic, spatially explicit models of likelihood of conversion to mechanized agriculture should consider land cover, slope, and proximity to main roads in addition to political and economic parameters to generate realistic scenarios for sustainable land-use planning.
Abstract
Grass-fueled fires accelerate grassland expansion into dry Hawaiian woodlands by destroying native forests and by producing a disturbance regime that favors grass-dominated plant communities. Knowledge of grassland phenology is a key component of ecosystem assessments and fire management in Hawaii, but diverse topographic relief and poor field-sampling capabilities make ground studies impractical. Remote sensing offers the best approach for large-scale, spatially contiguous measurements of dryland vegetation phenology and fire fuel conditions. A 500-m spatial resolution, 8-day temporal resolution Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite time series of photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and exposed substrate conditions was developed for the island of Hawaii between 2000 and 2004. The results compared favorably with similar measurements of drylands from higher-resolution aircraft data. The satellite time series was compared with available environmental data on precipitation, fire history, and grazing intensity. From these analyses, the temporal patterns of PV and its conversion to NPV and finally to bare substrate were observed. An NPV buildup following fire of 7–8 yr was projected, and more heavily grazed lands were found to exhibit reduced NPV cover, most notably during the summer fire season. These results demonstrate the effects that land use and disturbance history have on fire conditions, and they support the concept that grazed lands managed to reduce litter buildup pose a lower risk of fire across ample geographic scales. Time series of satellite observations with modern analysis techniques can be used with environmental data to support a regional fire-monitoring program throughout Hawaii.
Abstract
Grass-fueled fires accelerate grassland expansion into dry Hawaiian woodlands by destroying native forests and by producing a disturbance regime that favors grass-dominated plant communities. Knowledge of grassland phenology is a key component of ecosystem assessments and fire management in Hawaii, but diverse topographic relief and poor field-sampling capabilities make ground studies impractical. Remote sensing offers the best approach for large-scale, spatially contiguous measurements of dryland vegetation phenology and fire fuel conditions. A 500-m spatial resolution, 8-day temporal resolution Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite time series of photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and exposed substrate conditions was developed for the island of Hawaii between 2000 and 2004. The results compared favorably with similar measurements of drylands from higher-resolution aircraft data. The satellite time series was compared with available environmental data on precipitation, fire history, and grazing intensity. From these analyses, the temporal patterns of PV and its conversion to NPV and finally to bare substrate were observed. An NPV buildup following fire of 7–8 yr was projected, and more heavily grazed lands were found to exhibit reduced NPV cover, most notably during the summer fire season. These results demonstrate the effects that land use and disturbance history have on fire conditions, and they support the concept that grazed lands managed to reduce litter buildup pose a lower risk of fire across ample geographic scales. Time series of satellite observations with modern analysis techniques can be used with environmental data to support a regional fire-monitoring program throughout Hawaii.
Abstract
The all-weather capability, signal independence to the solar illumination angle, and response to 3D vegetation structures are the highlights of active radar systems for natural vegetation mapping and monitoring. However, they may present significant soil background effects. This study addresses a comparative analysis of the performance of L-band synthetic aperture radar (SAR) data and optical vegetation indices (VIs) for discriminating the Brazilian cerrado physiognomies. The study area was the Brasilia National Park, Brazil, one of the test sites of the Large-Scale Biosphere–Atmosphere (LBA) experiment in Amazonia. Seasonal Japanese Earth Resources Satellite-1 (JERS-1) SAR backscatter coefficients (σ°) were compared with two vegetation indices [normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI)] over the five most dominant cerrados’ physiognomies plus gallery forest. In contrast to the VIs, σ° from dry and wet seasons did not change significantly, indicating primary response to vegetation structures. Discriminant analysis and analysis of variance (ANOVA) showed an overall higher performance of radar data. However, when both SAR and VIs are combined, the discrimination capability increased significantly, indicating that the fusion of the optical and radar backscatter observations provides overall improved classifications of the cerrado types. In addition, VIs showed good performance for monitoring the cerrado dynamics.
Abstract
The all-weather capability, signal independence to the solar illumination angle, and response to 3D vegetation structures are the highlights of active radar systems for natural vegetation mapping and monitoring. However, they may present significant soil background effects. This study addresses a comparative analysis of the performance of L-band synthetic aperture radar (SAR) data and optical vegetation indices (VIs) for discriminating the Brazilian cerrado physiognomies. The study area was the Brasilia National Park, Brazil, one of the test sites of the Large-Scale Biosphere–Atmosphere (LBA) experiment in Amazonia. Seasonal Japanese Earth Resources Satellite-1 (JERS-1) SAR backscatter coefficients (σ°) were compared with two vegetation indices [normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI)] over the five most dominant cerrados’ physiognomies plus gallery forest. In contrast to the VIs, σ° from dry and wet seasons did not change significantly, indicating primary response to vegetation structures. Discriminant analysis and analysis of variance (ANOVA) showed an overall higher performance of radar data. However, when both SAR and VIs are combined, the discrimination capability increased significantly, indicating that the fusion of the optical and radar backscatter observations provides overall improved classifications of the cerrado types. In addition, VIs showed good performance for monitoring the cerrado dynamics.
Abstract
Correctly characterizing the frequency and distribution of fire occurrence is essential for understanding the environmental impacts of biomass burning. Satellite fire detection is analyzed from two sensors—the Advanced Very High Resolution Radiometer (AVHRR) on NOAA-12 and the Moderate Resolution Imaging Spectroradiometer (MODIS) on both the Terra and Aqua platforms, for 2001–03—to characterize fire activity in Brazil, giving special emphasis to the Amazon region. In evaluating the daily fire counts, their dependence on variations in satellite viewing geometry, overpass time, atmospheric conditions, and fire characteristics were considered. Fire counts were assessed for major biomes of Brazil, the nine states of the Legal Amazon, and two important road corridors in the Amazon region. All three datasets provide consistent information on the timing of peak fire activity for a given state. Also, ranking by relative fire counts per unit area highlights the importance of fire in smaller biomes such as Complexo do Pantanal. The local analysis of road corridors shows trends for fire detections with the increasing intensity of land use. Although absolute fire counts differ by as much as 1200%, when summarized over space and time, trends in fire counts among the three datasets show clear patterns of fire dynamics. The fire dynamics that are evident in these trend analyses are important foundations for assessing environmental impacts of biomass burning and policy measures to manage fire in Brazil.
Abstract
Correctly characterizing the frequency and distribution of fire occurrence is essential for understanding the environmental impacts of biomass burning. Satellite fire detection is analyzed from two sensors—the Advanced Very High Resolution Radiometer (AVHRR) on NOAA-12 and the Moderate Resolution Imaging Spectroradiometer (MODIS) on both the Terra and Aqua platforms, for 2001–03—to characterize fire activity in Brazil, giving special emphasis to the Amazon region. In evaluating the daily fire counts, their dependence on variations in satellite viewing geometry, overpass time, atmospheric conditions, and fire characteristics were considered. Fire counts were assessed for major biomes of Brazil, the nine states of the Legal Amazon, and two important road corridors in the Amazon region. All three datasets provide consistent information on the timing of peak fire activity for a given state. Also, ranking by relative fire counts per unit area highlights the importance of fire in smaller biomes such as Complexo do Pantanal. The local analysis of road corridors shows trends for fire detections with the increasing intensity of land use. Although absolute fire counts differ by as much as 1200%, when summarized over space and time, trends in fire counts among the three datasets show clear patterns of fire dynamics. The fire dynamics that are evident in these trend analyses are important foundations for assessing environmental impacts of biomass burning and policy measures to manage fire in Brazil.
