Journal ArticleDOI
Mapping Forest Height and Aboveground Biomass by Integrating ICESat-2, Sentinel-1 and Sentinel-2 Data Using Random Forest Algorithm in Northwest Himalayan Foothills of India
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This article is published in Geophysical Research Letters.The article was published on 2021-07-28. It has received 55 citations till now. The article focuses on the topics: Foothills.read more
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Spatio-temporal variability of water use efficiency and its drivers in major forest formations in India
TL;DR: In this article , the authors used the Google Earth Engine platform to calculate the water use efficiency (WUE) of major forest formations of India from 2003 to 2018 as the ratio of Moderate Resolution Spectroradiometer (MODIS) Gross Primary Productivity (GPP, MOD17A2H) to evapotranspiration (ET, MOD16A).
Journal ArticleDOI
Spatio-temporal variability of water use efficiency and its drivers in major forest formations in India
TL;DR: In this article, the authors used the Google Earth Engine platform to map the spatial distribution of water use efficiency (WUE) of major forest formations of India and analyzed the inter-annual and monthly variations of WUE.
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Forest Canopy Height Mapping by Synergizing ICESat-2, Sentinel-1, Sentinel-2 and Topographic Information Based on Machine Learning Methods
TL;DR: Wang et al. as discussed by the authors proposed a new method to estimate forest canopy height by combining ICESat-2 data, Synthetic Aperture Radar (SAR) data, multi-spectral images, and topographic data considering forest types.
Journal ArticleDOI
Satellite based integrated approaches to modelling spatial carbon stock and carbon sequestration potential of different land uses of Northeast India
TL;DR: In this article , a combined approach of field inventory and Landsat OLI derived vegetation indices were used in spatial modelling of aboveground biomass and carbon stock in different land uses in Northeast India and relate these estimates with the land use changes.
Journal ArticleDOI
Aboveground biomass mapping by integrating ICESat-2, SENTINEL-1, SENTINEL-2, ALOS2/PALSAR2, and topographic information in Mediterranean forests
Juan Guerra-Hernández,Lana Landra Narine,Adrián Pascual,E. González-Ferreiro,Brigite Botequim,Lonesome Malambo,Amy L. Neuenschwander,Sorin C. Popescu,Sérgio Godinho +8 more
TL;DR: In this paper , the effects of vegetation structure on the performance of canopy height and AGB modeling using ICESat-2 photon-counting light detection and ranging (LiDAR) data in Mediterranean forest areas have not been previously studied in the literature.
References
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Classification and Regression by randomForest
Andy Liaw,Matthew C. Wiener +1 more
TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.
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Red and photographic infrared linear combinations for monitoring vegetation
TL;DR: In this article, the relationship between various linear combinations of red and photographic infrared radiances and vegetation parameters is investigated, showing that red-IR combinations to be more significant than green-red combinations.
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Overview of the radiometric and biophysical performance of the MODIS vegetation indices
TL;DR: In this paper, the authors evaluated the performance and validity of the MODIS vegetation indices (VI), the normalized difference vegetation index (NDVI) and enhanced vegetation index(EVI), produced at 1-km and 500-m resolutions and 16-day compositing periods.