Forest canopy height estimation using satellite laser altimetry: a case study in the Western Ghats, India

Ghosh, Sanatan

doi:10.1007/s12518-017-0190-2

Cited by 5 publications

(4 citation statements)

References 26 publications

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“…The microwave pulses transmitted by a SAR system, especially at longer wavelengths such as the L-or P-band, interact with the branches and trunks, providing information about the forest structure, which is highly correlated to AGB [37][38][39]. Airborne LiDAR provides information on the canopy height and canopy cover [40][41][42][43], which are good proxies for woody AGB estimations [44]. The main limitations of LiDAR are that it is still an expensive technology and it is typically not available for large areas [45].…”

Section: Introductionmentioning

confidence: 99%

Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach

et al. 2020

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The tropical savanna in Brazil known as the Cerrado covers circa 23% of the Brazilian territory, but only 3% of this area is protected. High rates of deforestation and degradation in the woodland and forest areas have made the Cerrado the second-largest source of carbon emissions in Brazil. However, data on these emissions are highly uncertain because of the spatial and temporal variability of the aboveground biomass (AGB) in this biome. Remote-sensing data combined with local vegetation inventories provide the means to quantify the AGB at large scales. Here, we quantify the spatial distribution of woody AGB in the Rio Vermelho watershed, located in the centre of the Cerrado, at a high spatial resolution of 30 metres, with a random forest (RF) machine-learning approach. We produced the first high-resolution map of the AGB for a region in the Brazilian Cerrado using a combination of vegetation inventory plots, airborne light detection and ranging (LiDAR) data, and multispectral and radar satellite images (Landsat 8 and ALOS-2/PALSAR-2). A combination of random forest (RF) models and jackknife analyses enabled us to select the best remote-sensing variables to quantify the AGB on a large scale. Overall, the relationship between the ground data from vegetation inventories and remote-sensing variables was strong (R2 = 0.89), with a root-mean-square error (RMSE) of 7.58 Mg ha−1 and a bias of 0.43 Mg ha−1.

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Section: Introductionmentioning

confidence: 99%

Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach

et al. 2020

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“…A few case studies have been carried out in the Western Ghats region focusing on forest health and species diversity preservation (Osuri et al, 2014;Shukla et al, 2015;Jha et al, 2019). Ghosh and Behera (2017) explored the use of lidar data derived from the GeoScience Laser Altimeter System (GLAS) aboard the Ice, Cloud, and Land Elevation satellite (ICESat) to derive canopy height estimates in the tropical forests of the Western Ghats, India. However, PolInSAR based forest height estimation has not been addressed for Shimoga Forest ranges to date.…”

Section: Introductionmentioning

confidence: 99%

Use of TanDEM-X PolInSAR for canopy height retrieval over tropical forests in the Western Ghats, India

Raveendrakumar¹,

Khati²,

Musthafa³

et al. 2022

Front. For. Glob. Change

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Canopy height is a critical parameter in quantifying the vertical structure of forests. Polarimetric SAR Interferometry (PolInSAR) is a radar remote sensing technique that makes use of polarimetric separation of scattering phase centers obtained from interferometry to estimate height. This article discusses the potential of the X-band PolInSAR pair for forest height retrieval over tropical forests in the Western ghats. A total of 19 fully polarimetric datasets with various spatial baselines acquired from November 2015 to February 2016 in bistatic mode are utilized in this study. After compensating for all possible non-volumetric decorrelations in the data-sets, the remaining volume decorrelation is modeled using a Random Volume Over Ground (RVoG) model to invert height from PolInSAR data. A modified three-stage algorithm developed by Cloude and Papathanassiou (2003) is adopted for height inversion. PolInSAR derived heights were cross-validated against reference height data measured during a field survey conducted in March 2019. RMSE values of all TerraSAR-X/TanDEM-X PolInSAR heights with respect to field measured heights range from 3.3 to 13.8 m and the correlation coefficient r2 varies between 0.16 and 0.79. The results suggest that the use of a dataset with optimal wavenumber can improve the tree height estimation process. The best performance was achieved for the dataset acquired on 11 December 2015 with RMSE = 3.4 m and r2 = 0.79. Furthermore, the effects of parameters such as angle of incidence, precipitation, and forest biomass on height inversion accuracy are assessed. A large-scale Shimoga Forest height map was generated using multiple TanDEM-X acquisitions with the best correlation results. To improve the accuracy of the height estimation, a merged height approach is explored. The best height estimates among all PolInSAR estimates for a given field plot are chosen in this regard. The merged height approach gave rise to an improved inversion accuracy with RMSE = 1.9 m and r2 = 0.92. The primary objective of this study was to demonstrate the ability of spaceborne X-band data to estimate height with maximum accuracy over natural forests in India, in which height retrieval research has seldom been done.

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“…Multiple studies have shown that vegetation indices are a good predictor of the canopy height at a regional scale to a global scale [29,31,32]. Earlier attempts were made to estimate the canopy height maps over different regions in India [34,35]. Here a maiden attempt is made to create spatially continuous canopy height maps for the different vegetation types of India using GEDI LiDAR and multiple predictor variables using the RF ML algorithm (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Predicting the Forest Canopy Height from LiDAR and Multi-Sensor Data Using Machine Learning over India

Ghosh¹,

Kumar

Das³

et al. 2022

Remote Sensing

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Forest canopy height estimates, at a regional scale, help understand the forest carbon storage, ecosystem processes, the development of forest management and the restoration policies to mitigate global climate change, etc. The recent availability of the NASA’s Global Ecosystem Dynamics Investigation (GEDI) LiDAR data has opened up new avenues to assess the plant canopy height at a footprint level. Here, we present a novel approach using the random forest (RF) for the wall-to-wall canopy height estimation over India’s forests (i.e., evergreen forest, deciduous forest, mixed forest, plantation, and shrubland) by employing the high-resolution top-of-the-atmosphere (TOA) reflectance and vegetation indices, the synthetic aperture radar (SAR) backscatters, the topography and tree canopy density, as the proxy variables. The variable importance plot indicated that the SAR backscatters, tree canopy density and the topography are the most influential height predictors. 33.15% of India’s forest cover demonstrated the canopy height <10 m, while 44.51% accounted for 10–20 m and 22.34% of forests demonstrated a higher canopy height (>20 m). This study advocates the importance and use of GEDI data for estimating the canopy height, preferably in data-deficit mountainous regions, where most of India’s natural forest vegetation exists.

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Forest canopy height estimation using satellite laser altimetry: a case study in the Western Ghats, India

Cited by 5 publications

References 26 publications

Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach

Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach

Use of TanDEM-X PolInSAR for canopy height retrieval over tropical forests in the Western Ghats, India

Predicting the Forest Canopy Height from LiDAR and Multi-Sensor Data Using Machine Learning over India

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