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

2021 ◽  
Author(s):  
Subrata Nandy ◽  
Ritika Srinet ◽  
Hitendra Padalia
Keyword(s):  
Random Forest ◽  
Aboveground Biomass ◽  
Random Forest Algorithm ◽  
Forest Height ◽  
Himalayan Foothills ◽  
Sentinel 2

scholarly journals Mapping the Distribution of Coffee Plantations from Multi-Resolution, Multi-Temporal, and Multi-Sensor Data Using a Random Forest Algorithm

2020 ◽  
Vol 12 (23) ◽  
pp. 3933
Author(s):  
Anggun Tridawati ◽  
Ketut Wikantika ◽  
Tri Muji Susantoro ◽  
Agung Budi Harto ◽  
Soni Darmawan ◽  
...  
Keyword(s):  
Random Forest ◽  
Remote Sensing Data ◽  
Agroforestry System ◽  
Classification Model ◽  
Sensor Data ◽  
Kappa Statistics ◽  
Random Forest Algorithm ◽  
Coffee Plantations ◽  
Multi Temporal ◽  
Sentinel 2

Indonesia is the world’s fourth largest coffee producer. Coffee plantations cover 1.2 million ha of the country with a production of 500 kg/ha. However, information regarding the distribution of coffee plantations in Indonesia is limited. This study aimed to assess the accuracy of classification model and determine its important variables for mapping coffee plantations. The model obtained 29 variables which derived from the integration of multi-resolution, multi-temporal, and multi-sensor remote sensing data, namely, pan-sharpened GeoEye-1, multi-temporal Sentinel 2, and DEMNAS. Applying a random forest algorithm (tree = 1000, mtry = all variables, minimum node size: 6), this model achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy of 79.333%, 0.774, 92.000%, and 90.790%, respectively. In addition, 12 most important variables achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy 79.333%, 0.774, 91.333%, and 84.570%, respectively. Our results indicate that random forest algorithm is efficient in mapping coffee plantations in an agroforestry system.

scholarly journals Feature Comparison and Optimization for 30-M Winter Wheat Mapping Based on Landsat-8 and Sentinel-2 Data Using Random Forest Algorithm

2019 ◽  
Vol 11 (5) ◽  
pp. 535 ◽  
Author(s):  
Yuanhuizi He ◽  
Changlin Wang ◽  
Fang Chen ◽  
Huicong Jia ◽  
Dong Liang ◽  
...  
Keyword(s):  
Random Forest ◽  
Winter Wheat ◽  
High Precision ◽  
Agricultural Land ◽  
Global Climate ◽  
Machine Learning Algorithms ◽  
Landsat 8 ◽  
Spectral Indices ◽  
Random Forest Algorithm ◽  
Sentinel 2

Winter wheat cropland is one of the most important agricultural land-cover types affected by the global climate and human activity. Mapping 30-m winter wheat cropland can provide beneficial reference information that is necessary for understanding food security. To date, machine learning algorithms have become an effective tool for the rapid identification of winter wheat at regional scales. Algorithm implementation is based on constructing and selecting many features, which makes feature set optimization an important issue worthy of discussion. In this study, the accurate mapping of winter wheat at 30-m resolution was realized using Landsat-8 Operational Land Imager (OLI), Sentinel-2 Multispectral Imager (MSI) data, and a random forest algorithm. This paper also discusses the optimal combination of features suitable for cropland extraction. The results revealed that: (1) the random forest algorithm provided robust performance using multi-features (MFs), multi-feature subsets (MFSs), and multi-patterns (MPs) as input parameters. Moreover, the highest accuracy (94%) for winter wheat extraction occurred in three zones, including: pure farmland, urban mixed areas, and forest areas. (2) Spectral reflectance and the crop growth period were the most essential features for crop extraction. The MFSs combined with the three to four feature types enabled the high-precision extraction of 30-m winter wheat plots. (3) The extraction accuracy of winter wheat in three zones with multiple geographical environments was affected by certain dominant features, including spectral bands (B), spectral indices (S), and time-phase characteristics (D). Therefore, we can improve the winter wheat mapping accuracy of the three regional types by improving the spectral resolution, constructing effective spectral indices, and enriching vegetation information. The results of this paper can help effectively construct feature sets using the random forest algorithm, thus simplifying the feature construction workload and ensuring high-precision extraction results in future winter wheat mapping research.

Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm

2019 ◽  
Vol 102 ◽  
pp. 479-487 ◽  
Author(s):  
Na Zeng ◽  
Xiaoli Ren ◽  
Honglin He ◽  
Li Zhang ◽  
Dan Zhao ◽  
...  
Keyword(s):  
Random Forest ◽  
Tibetan Plateau ◽  
Aboveground Biomass ◽  
The Tibetan Plateau ◽  
Random Forest Algorithm

Impact of Backscatter in Pol-InSAR Forest Height Retrieval Based on the Multimodel Random Forest Algorithm

2020 ◽  
Vol 17 (2) ◽  
pp. 267-271
Author(s):  
Lei Wang ◽  
Jie Yang ◽  
Lei Shi ◽  
Pingxiang Li ◽  
Lingli Zhao ◽  
...  
Keyword(s):  
Random Forest ◽  
Random Forest Algorithm ◽  
Forest Height

Mapping plant functional types in Northwest Himalayan foothills of India using random forest algorithm in Google Earth Engine

2020 ◽  
Vol 41 (18) ◽  
pp. 7296-7309 ◽  
Author(s):  
Ritika Srinet ◽  
Subrata Nandy ◽  
Hitendra Padalia ◽  
Surajit Ghosh ◽  
Taibanganba Watham ◽  
...  
Keyword(s):  
Random Forest ◽  
Google Earth ◽  
Plant Functional Types ◽  
Random Forest Algorithm ◽  
Functional Types ◽  
Himalayan Foothills ◽  
Google Earth Engine
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