Inverting Aboveground Biomass–Canopy Texture Relationships in a Landscape of Forest Mosaic in the Western Ghats of India Using Very High Resolution Cartosat Imagery

Abstract

Large scale assessment of aboveground biomass (AGB) in tropical forests is often limited by the saturation of remote sensing signals at high AGB values. Fourier Transform Textural Ordination (FOTO) performs well in quantifying canopy texture from very high-resolution (VHR) imagery, from which stand structure parameters can be retrieved with no saturation effect for AGB values up to 650 Mg·ha −1 . The method is robust when tested on wet evergreen forests but is more demanding when applied across different forest types characterized by varying structures and allometries. The present study focuses on a gradient of forest types ranging from dry deciduous to wet evergreen forests in the Western Ghats (WG) of India, where we applied FOTO to Cartosat-1a images with 2.5 m resolution. Based on 21 1-ha ground control forest plots, we calibrated independent texture-AGB models for the dry and wet zone forests in the area, as delineated from the distribution of NDVI values computed from LISS-4 multispectral images. This stratification largely improved the relationship between texture-derived and field-derived AGB estimates, which exhibited a R 2 of 0.82 for a mean rRMSE of ca. 17%. By inverting the texture-AGB models, we finally mapped AGB predictions at 1.6-ha resolution over a heterogeneous landscape of ca. 1500 km 2 in the WG, with a mean relative per-pixel propagated error <20% for wet zone forests, i.e., below the recommended IPCC criteria for Monitoring, Reporting and Verification (MRV) methods. The method proved to perform well in predicting high-resolution AGB values over heterogeneous tropical landscape encompassing diversified forest types, and thus presents a promising option for affordable regional monitoring systems of greenhouse gas (GhG) emissions related to forest degradation.

Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)

“…This is likely due to the strong correlation coefficients between the forest AGB and SVI, NDVI, and RVI, which are consistent with the results from the scatterplots ( Figure 5). The result was in line with recent study results reported in Shen et al [88] and Pargal et al [89]. Our results suggest that SVI, NDVI, RVI, and PCA1 generated from Sentinel-2A data play an important role in the forest AGB estimation compared to other vegetation indices in the study area.…”

The real potential of current passive satellite data to map aboveground biomass in tropical forests

“…). The performance of such texture‐based metrics to infer forest AGB has been shown to vary strongly among sites and forest types (Pargal et al., 2017; Ploton et al., 2017; Ploton et al., 2013) and to be highly dependent on image acquisition parameters (Barbier et al., 2011).…”

Monitoring tropical forest carbon stocks and emissions using Planet satellite data

“…Two very popular textural measures used as a remotely sensed vegetation structure feature are the grey-level co-occurrence matrix (GLCM) texture 39–42 and Fourier transform textural ordination (FOTO) 20,43–45 . Although textural features have been applied to various sensors such as IKONOS-2 42 , Cartosat-1a 46 , SPOT-5 41 , QuickBird 47,48 , WorldView-2 49 , or RapidEye 21 , it was not tested on how it performs using Planet Dove images for large scale mapping of ACD.…”