Assessment of JERS-1 SAR for monitoring secondary vegetation in Amazonia: I. Spatial and temporal variability in backscatter across a chrono-sequence of secondary vegetation stands in Rondonia

Abstract

Abstract. Quanti cation of the direct impact of land use in the tropics on net biotic carbon ux relies on estimates of rates of deforestation, pre-and postdisturbance biomass, and fate of the cleared land. While existing remote sensing applications are providing estimates of the rates of deforestation and the fate of the cleared land (pasture, croplands, or secondary vegetation), techniques for estimating biomass of natural systems with remote sensing are needed. Synthetic Aperture Radar (SAR) presents a unique opportunity for imaging tropical forests under most cloud conditions and potentially provides information on vegetation biomass. Models for estimating above-ground biomass from SAR data have been developed. In this paper we examine the temporal and spatial variability of mean normalized radar cross-section across a chrono-sequence of secondary vegetation stands and clearings in Rondonia, Brazil. We also assess the impact of the observed temporal and spatial variability in normalized radar cross-section on estimating biomass of secondary vegetation stands. Results indicate that, while quantitative estimates of biomass are not stable due to intrinsic texture, system noise, and environmental eVects, JERS-1 data are still useful for categorizing relative diVerences in development of secondary vegetation stands. Merging Japanese Earth Resources Satellite 1 (JERS-1) SAR data with Landsat Thematic Mapper (TM) derived age information data provide improved characterization of clearings and secondary vegetation in Rondonia.

A remote sensing/GIS-based physical template to understand the biogeochemistry of the Ji-Paraná river basin (Western Amazônia)

“…These projects resulted in high deforestation rates, which has been well documented since the early 1980s through the use of satellite imagery (Brasil, 2001;Skole & Tucker, 1993;Woodwell, Houghton, Stone, & Park, 1986, among others). Several other studies have been developed on different aspects of land-use and land-cover change to understand the evolution of typical deforestation spatial patterns (fish-bone type) associated with the coloni- zation process (Dale, O'Neil, Pedlowski, & Southworth, 1993;Moran, 1992), its consequences for carbon cycling in terrestrial ecosystems (Alves et al, 1997) and the extent of secondary forest regrowth (Rignot, Salas, & Skole, 1997;Salas, Ducey, Rignot, & Skole, 2002). These studies have employed various remote sensing methods (Alves, Pereira, De Sousa, Soares, & Yamaguchi, 1999;Dale, O'Neil, Southworth, & Pedlowski, 1994), including visual interpretation (Brasil, 2001) and digital classification (Pedlowsky, Dale, Matricardi, & da Silva Filho, 1997;Roberts et al, in press) and, in terms of spatial scale, cover from several hundred to close to 100,000 km 2 .…”

Radar image texture as a function of forest stand age

“…Bernier et al (2003) showed the potential of radar image texture to help discriminate wetlands with various densities. Salas et al (2002) and Kuplich et al (2005) that texture information does improve the accuracy of biomass estimates from SAR data acquired over regenerating plots of secondary vegetation in tropical forest. As a conclusion, whatever the type of forest (temperate or boreal managed forest or naturally regenerating tropical forests), tree growth and development undoubtedly influence the forest spatial distribution and consequently the radar image texture.…”

Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests

“…Given that older generation sensors have many known limitations with respect to their suitability for studying complex biophysical characteristics (De Jong et al, 2000;Salas et al, 2002;Sampson et al, 2001;Steininger, 2000), the need to evaluate the new generation of sensors is of critical importance. This is particularly true in the rainforests, where vegetation complexity, spatial variability, diversity, and dynamics are at their highest levels.…”