Remote sensing plays an important role in characterizing the land surface by extensively concerning its spatial resolution. Most of the time, spectral and temporal resolution becomes a limitation, which now can be overcome via unmanned aerial vehicle (UAV) as a remote sensing platform. The study utilizes the google earth engine cloud-based platform to prepare the classified maps from Sentinel 2 and UAV datasets using the Random Forest algorithm. The canopy cover was estimated using UAV data and divided into 4 classes: very dense forest, moderately dense forest, open forest and scrub forest. The majority (39%) areas were under scrub forest. Furthermore, the land use land cover was prepared using UAV data and showed superior results with 95.5% overall accuracy compared with 86.5% of Sentinel 2. Lastly, the tree count of the area was estimated using high-resolution data. The predicted number of trees was 3052, with an accuracy of 82%. The tree count algorithm works better in plantation and even canopy-size trees. Thus, this methodology ultimately helps to achieve the sustainable use of resources concerning their availability, demand and exploitation in the study area. The estimated results can help policymakers, government officials, and local people halt desertification and better sustainable forest management.


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    Title :

    High-Resolution Mapping of Forest Canopy Cover Using UAV and Sentinel-2


    Additional title:

    Lecture Notes in Civil Engineering



    Conference:

    International Conference on Unmanned Aerial System in Geomatics ; 2021 ; Roorkee, India April 02, 2021 - April 04, 2021



    Publication date :

    2023-03-16


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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