William Salas

Benchmark map of forest carbon stocks in tropical regions across three continents

Developing countries are required to produce robust estimates of forest carbon stocks for successful implementation of climate change mitigation policies related to reducing emissions from deforestation and degradation (REDD). Here we present a “benchmark” map of biomass carbon stocks over 2.5 billion ha of forests on three continents, encompassing all tropical forests, for the early 2000s, which will be invaluable for REDD assessments at both project and national scales. We mapped the total carbon stock in live biomass (above- and belowground), using a combination of data from 4,079 in situ inventory plots and satellite light detection and ranging (Lidar) samples of forest structure to estimate carbon storage, plus optical and microwave imagery (1-km resolution) to extrapolate over the landscape. The total biomass carbon stock of forests in the study region is estimated to be 247 Gt C, with 193 Gt C stored aboveground and 54 Gt C stored belowground in roots. Forests in Latin America, sub-Saharan Africa, and Southeast Asia accounted for 49%, 25%, and 26% of the total stock, respectively. By analyzing the errors propagated through the estimation process, uncertainty at the pixel level (100 ha) ranged from ±6% to ±53%, but was constrained at the typical project (10,000 ha) and national (>1,000,000 ha) scales at ca. ±5% and ca. ±1%, respectively. The benchmark map illustrates regional patterns and provides methodologically comparable estimates of carbon stocks for 75 developing countries where previous assessments were either poor or incomplete.

Baseline map of carbon emissions from deforestation in tropical regions.

Tropical Carbon Loss Accurate and precise measures of tropical deforestation and the resulting carbon emissions are needed in order to formulate climate policy. Harris et al. (p. 1573; see the Perspective by Zarin) used satellite observations of deforestation within the tropics of three continents to estimate that gross annual carbon emissions were approximately 0.8 Pg Cyr−2 (Pg = 1015 g) for the years 2000 to 2005, from the loss of 43 million hectares of forest. This result, which is about one-third of some previous estimates, should serve as a baseline for future assessments of changes in the rate of loss of tropical forests. Tropical deforestation and degradation across three continents led to ~0.8 petagrams of yearly carbon emissions from 2000 to 2005. Policies to reduce emissions from deforestation would benefit from clearly derived, spatially explicit, statistically bounded estimates of carbon emissions. Existing efforts derive carbon impacts of land-use change using broad assumptions, unreliable data, or both. We improve on this approach using satellite observations of gross forest cover loss and a map of forest carbon stocks to estimate gross carbon emissions across tropical regions between 2000 and 2005 as 0.81 petagram of carbon per year, with a 90% prediction interval of 0.57 to 1.22 petagrams of carbon per year. This estimate is 25 to 50% of recently published estimates. By systematically matching areas of forest loss with their carbon stocks before clearing, these results serve as a more accurate benchmark for monitoring global progress on reducing emissions from deforestation.

Physical and human dimensions of deforestation in Amazonia

In the Brazilian Amazon, regional trends are influenced by large scale external forces but mediated by local conditions. Tropical deforestation has a large influence on global hydrology, climate and biogeochemical cycles, but understanding is inadequate because of a lack of accurate measurements of rate, geographic extent and spatial patterns and lack of insight into its causes including interrelated social, economic and environmental factors. This article proposes an interdisciplinary approach for analyzing tropical deforestation in the Brazilian Amazon. The first part shows how deforestation can be measured from satellite remote sensing and sociodemographic and economic data. The second part proposes an explanatory model, considering the relationship among deforestation and large scale social, economic, and institutional factors. 43 refs., 8 figs.

Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data

A unique physical feature of paddy rice fields is that rice is grown on flooded soil. During the period of flooding and rice transplanting, there is a large proportion of surface water in a land surface consisting of water, vegetation and soils. The VEGETATION (VGT) sensor has four spectral bands that are equivalent to spectral bands of Landsat TM, and its mid-infrared spectral band is very sensitive to soil moisture and plant canopy water content. In this study we evaluated a VGT-derived normalized difference water index (NDWI VGT =(B3-MIR)/ (B3+MIR)) for describing temporal and spatial dynamics of surface moisture. Twenty-seven 10-day composites (VGT- S10) from 1 March to 30 November 1999 were acquired and analysed for a study area (175 km by 165 km) in eastern Jiangsu Province, China, where a winter wheat and paddy rice double cropping system dominates the landscape. We compared the temporal dynamics and spatial patterns of normalized difference vegetation index (NDVI VGT ) and NDWI VGT . The NDWI VGT temporal dynamics were sensitive enough to capture the substantial increases of surface water due to flooding and rice transplanting at paddy rice fields. A land use thematic map for the timing and location of flooding and rice transplanting was generated for the study area. Our results indicate that NDWI and NDVI temporal anomalies may provide a simple and effective tool for detection of flooding and rice transplanting across the landscape.

Fourier analysis of multi-temporal AVHRR data applied to a land cover classification

Abstract A signal processing technique is presented and applied to annual patterns of the Global Vegetation Index (GVI) derived from the Advanced Very High Resolution Radiometer (AVHRR) to examine the frequency distribution of the multi-temporal signal. It is shown that frequencies of the signal are linked to integrated GVI, seasonal variability and subseasonal variability of the land cover type. These characteristics are used to derive a land cover classification.

Secondary Forest Age and Tropical Forest Biomass Estimation Using Thematic Mapper Imagery

he Brazilian Legal Amazon encompasses an area of approximately 5,030,000 km 2 , stretching from the states of Maranhao and Tocantins in the east to Amazonas and Acre in the west, and from Roraima and Amapa in the north to Mato Grosso in the south. Approximately 4,090,000 km 2 of this area is forested, 850,000 km 2 is cerrado (wooded grassland), and 90,000 km 2 is water (Skole and Tucker 1993, Fearnside 1996). As of 1988, 230,324 km 2 , or 5.6%, of the approximately 4,090,000 km 2 forested Brazilian Legal Amazon had been deforested (Skole and Tucker 1993, Skole et al. 1994) since theconstruction of the Belem-Brasilia Highway in 1958 (Moran et al. 1994). Each year, approximately 15,000‐20,000 km 2 of additional primary tropical forest in this region are cut and cleared (Skole et al. 1994), although estimates of clearing rates have varied greatly among studies and over the decades (approximately 8000‐10,000 km 2 /yr in the 1970s, Mahar 1988; approximately 35,000 km 2 /yr in the 1980s, Fearnside 1989; approximately 15,200 km 2 /yr from 1978‐1988, Skole and Tucker 1993).

Landscape-scale characterization of cropland in China using Vegetation and Landsat TM images

In this landscape-scale study we explored the potential for multitemporal 10-day composite data from the Vegetation sensor to characterize land cover types, in combination with Landsat TM image and agricultural census data. The study area (175 km by 165 km) is located in eastern Jiangsu Province, China. The Normalized Difference Vegetation Index (NDVI ) and the Normalized Difference Water Index (NDWI ) were calculated for seven 10-day composite (VGT-S10) data from 11 March to 20 May 1999. Multi-temporal NDVI and NDWI were visually examined and used for unsupervised classification. The resultant VGT classification map at 1 km resolution was compared to the TM classification map derived from unsupervised classification of a Landsat 5 TM image acquired on 26 April 1996 at 30 m resolution to quantify percent fraction of cropland within a 1 km VGT pixel; resulting in a mean of 60% for pixels classified as cropland, and 47% for pixels classified as cropland/natural vegetation mosaic. The estimates of cropland area from VGT data and TM image were also aggregated to county-level, using an administrative county map, and then compared to the 1995 county-level agricultural census data. This landscape-scale analysis incorporated image classification (e.g. coarse-resolution VGT data, fineresolution TM data), statistical census data (e.g. county-level agricultural census data) and a geographical information system (e.g. an administrative county map), and demonstrated the potential of multi-temporal VGT data for mapping of croplands across various spatial scales from landscape to region. This analysis also illustrated some of the limitations of per-pixel classification at the 1 km resolution for a heterogeneous landscape.

Potential biomass accumulation in Amazonian regrowth forests.

Biomass accumulation in the secondary forests of abandoned pastures and slash-and-burn agricultural fallows is an important but poorly constrained component of the regional carbon budget for the Brazilian Amazon. Using empirical relationships derived from a global analysis, we predicted potential aboveground biomass accumulation (ABA) for the regions regrowth forests based on soil texture and climate data. For regrowth forests on nonsandy soils, the globally derived relationship provided a nearly unbiased linear predictor of Amazonian validation data consisting of 66 stands at seven sites; there was no significant difference between stands that regrew following use as pasture land and those that regrew following slash-and-burn agriculture. For regrowth forests on nonsandy soil, the 1 sigma error range of our ABA model was 58%–171% for the Amazonian validation data. For regrowth forests on sandy soils, the validation data were limited to 19 stands at one site, and the globally derived relationship was substantially biased multiplicatively and nonlinearly. Hence we developed a regional refinement by adding to our validation data ABA values from the two Amazonian sites with sandy soil that had previously been included in the global analysis. Based on a conservative jackknife goodness-of-fit assessment (leaving out one site at a time), we calculated a 1 sigma error range of 42%–158% for our sandy soil Amazonian regrowth forest ABA model. We present our predictions of potential regrowth forest ABA as a set of 0.5° resolution maps for the region at 5, 10, and 20 years following abandonment.

Mapping paddy rice with multitemporal ALOS/PALSAR imagery in southeast China

Mapping rice cropping areas with optical remote sensing is often a challenge in tropical and subtropical regions because of frequent cloud cover and rainfall during the rice growing season. Synthetic aperture radar (SAR) is a potential alternative for rice mapping because of its all-weather imaging capabilities. The recent Phased Array-type L-band SAR (PALSAR) sensor onboard the Advanced Land Observing Satellite (ALOS) acquires multipolarization and multitemporal images that are highly suitable for rice mapping. In this pilot study, we demonstrate the feasibility of this sensor in mapping the rice planting area in Zhejiang Province, southeast China. High-resolution ALOS/PALSAR images were acquired at three rice growing stages (transplanting, tillering and heading) and were applied in a support vector machine (SVM) classifier to map rice and other land use surfaces. The results show that, based on the 1:10 000 land use/land cover (LULC) survey map, the rice fields can be mapped with a conditional Kappa value of 0.87 and at users and producers accuracies of 90% and 76%, respectively. The large commission error primarily came from confusion between rice and dryland crops or orchards because of their similar backscatter amplitudes in the rice growing season. The relatively high rice mapping accuracy in this study indicates that the new ALOS/PALSAR data could provide useful information in rice cropping management in subtropical regions such as southeast China.

Mapping secondary tropical forest and forest age from SPOT HRV data

Accurate mapping of secondary forest and the age of these forests is critical to assess the carbon budget in tropical regions accurately. Using SPOT HRV (High Resolution Visible) data, techniques were developed and tested to discriminate primary forest, secondary forest and deforested areas on a study site in Rondonia, Brazil. Six co-registered SPOT HRV images (1986, 1988, 1989, 1991, 1992 and 1994) were used to create a time series of classified images of land cover (primary forest, secondary forest and deforested). These trajectories were used to identify secondary forest age classes relative to the most recent (1994) image. The resultant 1994 map of primary forest, secondary forest age classes and deforested areas served as ground reference data to establish training and testing sites. Several band 2 and 3 texture measurements were calculated using a 3 x 3 window to quantify canopy homogeneity. Neural networks and linear analysis techniques were tested for discriminating between primary forest, seconda...