Estimation of Above Ground Biomass for Central Indian Deciduous Forests Using ALOS PALSAR L-Band Data

Thumaty, Kiran Chand; Fararoda, Rakesh; Middinti, Suresh; Rajashekar, G.; Jha, Chandra Shekhar; Dadhwal, V. K.

doi:10.1007/s12524-015-0462-4

Cited by 36 publications

(14 citation statements)

References 52 publications

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“…However our results, also according to Figure 4, do not clearly show the usual 100-150 Mg/ha saturation limit reported in literature [1,57,58]; and it is difficult to understand if at higher AGB levels there is a scarce saturation effect or increased error in model. These results from a single research might be caused also by environmental characteristics of the study areas (e.g., soil and forest structure).…”

Section: Discussioncontrasting

confidence: 95%

Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates

et al. 2016

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Remote sensing supports carbon estimation, allowing the upscaling of field measurements to large extents. Lidar is considered the premier instrument to estimate above ground biomass, but data are expensive and collected on-demand, with limited spatial and temporal coverage. The previous JERS and ALOS SAR satellites data were extensively employed to model forest biomass, with literature suggesting signal saturation at low-moderate biomass values, and an influence of plot size on estimates accuracy. The ALOS2 continuity mission since May 2014 produces data with improved features with respect to the former ALOS, such as increased spatial resolution and reduced revisit time. We used ALOS2 backscatter data, testing also the integration with additional features (SAR textures and NDVI from Landsat 8 data) together with ground truth, to model and map above ground biomass in two mixed forest sites: Tahoe (California) and Asiago (Alps). While texture was useful to improve the model performance, the best model was obtained using joined SAR and NDVI (R 2 equal to 0.66). In this model, only a slight saturation was observed, at higher levels than what usually reported in literature for SAR; the trend requires further investigation but the model confirmed the complementarity of optical and SAR datatypes. For comparison purposes, we also generated a biomass map for Asiago using lidar data, and considered a previous lidar-based study for Tahoe; in these areas, the observed R 2 were 0.92 for Tahoe and 0.75 for Asiago, respectively. The quantitative comparison of the carbon stocks obtained with the two methods allows discussion of sensor suitability. The range of local variation captured by lidar is higher than those by SAR and NDVI, with the latter showing overestimation. However, this overestimation is very limited for one of the study areas, suggesting that when the purpose is the overall quantification of the stored carbon, especially in areas with high carbon density, satellite data with lower cost and broad coverage can be as effective as lidar.

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Section: Discussioncontrasting

confidence: 95%

Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates

et al. 2016

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“…AGB predicted in this study area was 72.54 Mg/ha. The estimates of AGB given in the current study are higher than the estimates calculated from deciduous forests in India (58 Mg/ha) [54] (Table 6). It also almost doubles compared to the value from Mediterranean forests in Italy (38 Mg/ha) [53] and the AGB value from boreal forests in Alaska (39.5 Mg/ha) [55].…”

Section: Discussioncontrasting

confidence: 83%

“…It also almost doubles compared to the value from Mediterranean forests in Italy (38 Mg/ha) [53] and the AGB value from boreal forests in Alaska (39.5 Mg/ha) [55]. However, the current AGB is lower than the biomass of mangrove forests in Thailand (250.53 Mg/ha) [52], tropical forests in Borneo (382 Mg/ha) [51], and conifer broadleaf forests in China (106.45 Mg/ha) [54]. The AGB of tropical forests is higher than that of private forests because natural forests store a large amount of biomass on terrestrial ecosystems which accumulates over a long period of time.…”

Section: Discussionmentioning

confidence: 96%

Estimating Aboveground Biomass on Private Forest Using Sentinel-2 Imagery

et al. 2018

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Private forests have a crucial role in maintaining the functioning of the Indonesian forest ecosystem especially because of the continuous degradation of natural forests. Private forests are a part of social forestry which becomes a tool for the Indonesian government to reduce carbon dioxide (CO2) emission by 26% by 2030. The United Nations Programme on Reducing Emissions from Deforestation and Forest Degradation has encouraged the Indonesian government to establish a forest monitoring system by estimating forest carbon stock using a combination of forest inventory and remote sensing. This study is aimed at assessing the potential of vegetation indices derived from Sentinel-2 for estimating aboveground biomass (AGB) of private forests. We used 45 sample plots and 7 vegetation indices to evaluate the ability of Sentinel-2 in estimating AGB on private forests. Normalised difference index (NDI) 45 exhibited a strong correlation with AGB compared to other indices (r = 0.89; R2 = 0.79). Stepwise linear regression fitted for establishing the model between field AGB and vegetation indices (R2 = 0.81). We also found that AGB in the study area based on spatial analysis was 72.54 Mg/ha. A root mean square error (RMSE) value from predicted and observed AGB was 27 Mg/ha. The AGB value in the study area is higher than the AGB value from some of forest types, and it indicates that private forests are good for biomass storage. Overall, vegetation indices from Sentinel-2 multispectral imagery can provide a good result in terms of reporting the AGB on private forests.

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“…For instance, Wiseman et al reported that RADARSAT-2 polarimetric C-Band SAR HV can discriminate the level of biomass for canola and alfalfa crops [6]. Furthermore, C-band data, Advanced Land Observing Satellite-Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR) cross-polarized HV backscatter information also showed a better relationship with the above-ground biomass of different forest types than co-polarized HH did [24]. A possible reason for this may be that the cross-polarized data (HV or VH) mainly originate from multiple scattering within the canopy and are less influenced by the canopy surface condition [25].…”

Section: Discussionmentioning

confidence: 99%

Field-Scale Rice Yield Estimation Using Sentinel-1A Synthetic Aperture Radar (SAR) Data in Coastal Saline Region of Jiangsu Province, China

et al. 2019

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In recent years, a large number of salterns have been converted into rice fields in the coastal region of Jiangsu Province, Eastern China. The high spatial heterogeneity of soil salinity has caused large within-field variabilities in grain yield of rice. The identification of low-yield areas within a field is an important initial step for precision farming. While optical satellite remote sensing can provide valuable information on crop growth and yield potential, the availability of cloud-free optical image data is often hampered by unfavorable weather conditions. Synthetic aperture radar (SAR) offers an alternative due to its nearly day-and-night and all-weather capability in data acquisition. Given the free data access of the Sentinels, this study aimed at developing a Sentinel-1A-based SAR index for rice yield estimation. The proposed SAR simple difference (SSD) index uses the change of the Sentinel-1A backscatter in vertical-horizontal (VH) polarization between the end of the tillering stage and the end of grain filling stage (SSDVH). A strong exponential relationship has been identified between the SSDVH and rice yield, producing accurate yield estimation with a root mean square error (RMSE) of 0.74 t ha−1 and a relative error (RE) of 7.93%.

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Estimation of Above Ground Biomass for Central Indian Deciduous Forests Using ALOS PALSAR L-Band Data

Cited by 36 publications

References 52 publications

Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates

Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates

Estimating Aboveground Biomass on Private Forest Using Sentinel-2 Imagery

Field-Scale Rice Yield Estimation Using Sentinel-1A Synthetic Aperture Radar (SAR) Data in Coastal Saline Region of Jiangsu Province, China

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