## 6 Pan-Tropical AGB Mapping
### Country-wide High-Resolution Tropical Biomass Mapping  
Apart from low-resolution pan-tropical estimates of tropical biomass, numerous studies have been conducted to estimate biomass on national or limited geographic scales by using the high-resolution passive or active remote sensing data. Free availability of medium-to-high-resolution images (such as Landsat and Sentinel-2 imagery at 30 m and 10 m spatial resolutions), rapid progress in cost-effective airborne systems and high-performance computing systems have enabled the possibility of mapping the tropical carbon stock monitoring at a higher resolution. Recently, [PERSON] et al. [171] mapped AGB in West African tropical dry forests by applying a Random Forest machine learning algorithm on integrated SAR images of Sentinel-1 and multispectral images of Sentinel-2. They found that multispectral images produced better results than the SAR images. However, the best results were achieved by combining the two. A random forest regression model was effectively used to estimate the high-resolution AGB map of Peru by combing the textural and reflectance features derived from high-resolution 'Planet Dove' satellite images (4 m spatial resolution), and canopy height information from LiDAR datasets [114]. Earlier, [PERSON] [33] presented an integrated approach of combining airborne LiDAR data with the Landsat-based forest attributes (such as canopy cover, deforestation and degradation), which were obtained through the Carnegie Landsat Analysis System (CLAS) developed for the Brazilian tropical forest monitoring and assessment using a variety of satellite imagery [35]. This analysis is based on the allometric equations derived from extensive in situ measurements of tree diameters, height and wood density [38; 172]. [PERSON] et al. [173] explored the potential of Rapid Eye imager (5 m spatial resolution) for national-level carbon stock assessment of Tanzania by first segmenting the image into image object of 1 ha, and then applying a random forest regression between the image objects feature (textural, spectral indices and reflectance) and field-based estimates of AGB. In another study, an object-based method was developed by integrating very-high-resolution Quickbird-2 imagery with LiDAR data to estimate AGB in the tropical forest of Cambodia [174]. In Central Africa, very-high-resolution images acquired by the GeoEye-1 and Quickbird-2 satellites were used to produce the high-resolution biomass map (100 m), by developing the piecewise regression model from the 26 inventory plots and canopy texture metrics--Fourier Textural Ordination--derived from the optical satellite images [175]. [PERSON] et al. [176] developed a framework of modelling the AGB of the tropical forest in Southeast Asia, by applying Random Forest and a Support Vector Machine (SVM) on textural indices--Grey Level Co-Occurrence Matrix, Fourier-Based Textural Ordination (FOTO) and Gabor Wavelet-Based Texture Measures--derived from Google Earth images at a 50 cm resolution, and very-high-resolution multispectral aerial images at an 8 cm resolution. Similarly, a combination of FOTO and Lacunarity textural feature and bioclimatic proxy variables was used to develop a generalized prediction model of AGB. This study was based on 279 samples of 1 ha plots distributed in three continents (India, Africa and French Guiana). The model was tested using the textural features derived from high-resolution (0.7 to 1 m) Pleiades images and another set of validation data comprising 49 plots of 1 ha in Eastern Cameroon [177]. Texture-based modelling of the spatial gradient in tropical AGB has gained popularity among scientists over the past 10 years, and these studies have opened new possibilities of using high-resolution low-cost images for estimating AGB biomass in tropical forests at the landscape level. However, the scope of biomass estimation using the very-high-resolution commercial satellite imagery is limited to smaller geographic areas due to its availability and cost.