The Global Rain Forest Mapping project - A review

Rosenqvist, A.; Shimada, Masanobu; Chapman, B.; Freeman, A.; Grandi, G. De; Saatchi, S.; Rauste, Yrjö

doi:10.1080/014311600210227

Cited by 128 publications

(72 citation statements)

References 10 publications

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“…Figure 1 contains a subset of the 1996 TM image and a threedate RGB composite of the 1996 JERS images from October, August, and July, respectively. These data were obtained through our participation in NASDA's Global Rain Forest Mapping Project (see Rosenqvist et al 2000 for details on GRFM). JERS-1 SAR data were used for our analysis of secondary vegetation because the lower frequency of L-band is more sensitive to above-groun d biomass than C-band data available from ERS or Radarsat, the only other satellite-based SARs.…”

Section: Jers-1 Sar Data Pre-processingmentioning

confidence: 99%

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

Salas¹,

Ducey²,

Rignot³

et al. 2002

International Journal of Remote Sensing

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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.

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Section: Jers-1 Sar Data Pre-processingmentioning

confidence: 99%

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

Salas¹,

Ducey²,

Rignot³

et al. 2002

International Journal of Remote Sensing

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“…Table 1 lists the eight level 2.1 products (threelooks with 12.5 m pixels) used for this study. These data were obtained through our participation in NASDA's Global Rain Forest Mapping Project (see Rosenqvist et al 2000 for details on GRFM). These images were co-registered automatically using image cross-correlation and converted to normalized radar cross-section (s°) values in dB with the following formula:…”

Section: Jers-1 Sar Data Pre-processingmentioning

confidence: 99%

Assessment of JERS-1 SAR for monitoring secondary vegetation in Amazonia: II. Spatial, temporal, and radiometric considerations for operational monitoring

Salas¹,

Ducey²,

Rignot³

et al. 2002

International Journal of Remote Sensing

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Abstract. While the role of Synthetic Aperture Radar (SAR) in operational tropical forest monitoring has yet to be de ned, it is nevertheless a critical technology for improving our understanding of deforestation and secondary vegetation in the tropics. In order to understand the role of this technology in operational monitoring a systematic evaluation, relative to other existing technologies of its performance is required. In this paper we evaluate the spatial, temporal, and noise constraints of JERS SAR data for mapping and monitoring biomass of secondary vegetation in Rondonia, Brazil. Our results indicate that the variability in stand estimates of biomass is high and that the source of the majority of the variability is not from speckle and the intrinsic texture of the secondary vegetation but likely due to diVerences in environmental conditions resulting in diVerential background scattering properties. Multitemporal analysis signi cantly improves biomass estimates to the point where it is possible to map changes in biomass. Slight reductions in the variability in estimates of normalized radar cross-section greatly improve biomass estimation.

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“…When combined with field inventory and optical data, the SRTM InSAR phase data has been utilized to generate national mosaic maps of both biomass and forest height for the United States, which are provided by the National Biomass and Carbon Dataset (NBCD) 2000 [2]. In addition to this data, JAXA's L-band JERS-1 (from 1992 to 1998; [3]) and ALOS/PALSAR (2006 to 2011; [4,5]) have collected a large amount of SAR image data over the past decade. Continental-scale mosaics of SAR backscatter intensity have been demonstrated and related to both spatial and temporal forest characteristics [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

An Automatic Mosaicking Algorithm for the Generation of a Large-Scale Forest Height Map Using Spaceborne Repeat-Pass InSAR Correlation Magnitude

Yang

Siqueira

2015

Remote Sensing

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This paper describes an automatic mosaicking algorithm for creating large-scale mosaic maps of forest height. In contrast to existing mosaicking approaches through using SAR backscatter power and/or InSAR phase, this paper utilizes the forest height estimates that are inverted from spaceborne repeat-pass cross-pol InSAR correlation magnitude. By using repeat-pass InSAR correlation measurements that are dominated by temporal decorrelation, it has been shown that a simplified inversion approach can be utilized to create a height-sensitive measure over the whole interferometric scene, where two scene-wide fitting parameters are able to characterize the mean behavior of the random motion and dielectric changes of the volume scatterers within the scene. In order to combine these single-scene results into a mosaic, a matrix formulation is used with nonlinear least squares and observations in adjacent-scene overlap areas to create a self-consistent estimate of forest height over the larger region. This automated mosaicking method has the benefit of suppressing the global fitting error and, thus, mitigating the "wallpapering" problem in the manual mosaicking process. The algorithm is validated over the U.S. state of Maine by using InSAR correlation magnitude data from ALOS/PALSAR and comparing the inverted forest height with Laser Vegetation Imaging Sensor (LVIS) height and National Biomass and Carbon Dataset (NBCD) basal area weighted (BAW) height. This paper serves as a companion work to previously demonstrated results, the combination of which is meant to Remote Sens. 2015, 7 5640 be an observational prototype for NASA's DESDynI-R (now called NISAR) and JAXA's ALOS-2 satellite missions.

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The Global Rain Forest Mapping project - A review

Cited by 128 publications

References 10 publications

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

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

Assessment of JERS-1 SAR for monitoring secondary vegetation in Amazonia: II. Spatial, temporal, and radiometric considerations for operational monitoring

An Automatic Mosaicking Algorithm for the Generation of a Large-Scale Forest Height Map Using Spaceborne Repeat-Pass InSAR Correlation Magnitude

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