Above ground tree biomass modeling using machine learning algorithms in western Terai Sal Forest of Nepal
The monitoring of forest biomass is a crucial biophysical parameter in forest ecosystems, as it provides valuable information for managing forests sustainably and tracking carbon circulation statistics. To achieve sustainable forest management, it is essential to monitor and study forest resources,...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023086930 |
| _version_ | 1797429804122767360 |
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| author | Bikram Singh Amit Kumar Verma Kasip Tiwari Rajeev Joshi |
| author_facet | Bikram Singh Amit Kumar Verma Kasip Tiwari Rajeev Joshi |
| author_sort | Bikram Singh |
| collection | DOAJ |
| description | The monitoring of forest biomass is a crucial biophysical parameter in forest ecosystems, as it provides valuable information for managing forests sustainably and tracking carbon circulation statistics. To achieve sustainable forest management, it is essential to monitor and study forest resources, particularly biomass. This study aimed to model above ground tree biomass (AGTB) using Machine Learning Algorithms (MLAs) in the western terai Sal forest of Nepal. AGTB was calculated using a systematic inventory sample plot, while spectral and textural variables were processed and masked for the study area using Sentinel-2A satellite imagery. Three MLAs namely support vector machine (SVM), random forest (RF), and stochastic gradient boosting (SGB), were employed for modeling with eight categorized variable datasets. Among the MLAs, the RF algorithm with a combination of gray-level co-occurrence matrix (GLCM) and raw bands (RB) dataset variable demonstrated the best performance, with a low RMSE value of 78.81 t ha−1 in the test data. However, the AGTB range from this model ranged from 118.34 to 425.97 t ha−1. The study found that traditional indices, raw bands, and GLCM texture from near-infrared were important variables for AGTB. Nevertheless, the RF algorithm and the dataset combination of GLCM plus raw bands (RB) exhibited excellent performance in all model runs. Thus, this pioneering study on comparative MLAs-based AGTB assessment with multiple datasets variables can provide valuable insights for new researchers and the development of novel approaches for biomass/carbon estimation techniques in Nepal. |
| first_indexed | 2024-03-09T09:18:25Z |
| format | Article |
| id | doaj.art-0ac4f31dc7ec439c88d18ecb0c542f2b |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-09T09:18:25Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-0ac4f31dc7ec439c88d18ecb0c542f2b2023-12-02T07:02:31ZengElsevierHeliyon2405-84402023-11-01911e21485Above ground tree biomass modeling using machine learning algorithms in western Terai Sal Forest of NepalBikram Singh0Amit Kumar Verma1Kasip Tiwari2Rajeev Joshi3Forest Research Institute (Deemed to be) University, Dehradun-248195, Uttarakhand, India; Corresponding author.Forest Research Institute (Deemed to be) University, Dehradun-248195, Uttarakhand, IndiaTTG Forestry LLC, Atlanta, GA, USACollege of Natural Resource Management, Faculty of Forestry, Agriculture and Forestry University, Katari, 56310, Udayapur, NepalThe monitoring of forest biomass is a crucial biophysical parameter in forest ecosystems, as it provides valuable information for managing forests sustainably and tracking carbon circulation statistics. To achieve sustainable forest management, it is essential to monitor and study forest resources, particularly biomass. This study aimed to model above ground tree biomass (AGTB) using Machine Learning Algorithms (MLAs) in the western terai Sal forest of Nepal. AGTB was calculated using a systematic inventory sample plot, while spectral and textural variables were processed and masked for the study area using Sentinel-2A satellite imagery. Three MLAs namely support vector machine (SVM), random forest (RF), and stochastic gradient boosting (SGB), were employed for modeling with eight categorized variable datasets. Among the MLAs, the RF algorithm with a combination of gray-level co-occurrence matrix (GLCM) and raw bands (RB) dataset variable demonstrated the best performance, with a low RMSE value of 78.81 t ha−1 in the test data. However, the AGTB range from this model ranged from 118.34 to 425.97 t ha−1. The study found that traditional indices, raw bands, and GLCM texture from near-infrared were important variables for AGTB. Nevertheless, the RF algorithm and the dataset combination of GLCM plus raw bands (RB) exhibited excellent performance in all model runs. Thus, this pioneering study on comparative MLAs-based AGTB assessment with multiple datasets variables can provide valuable insights for new researchers and the development of novel approaches for biomass/carbon estimation techniques in Nepal.http://www.sciencedirect.com/science/article/pii/S2405844023086930Machine learning algorithmsAbove ground tree biomassSentinel-2ASustainable |
| spellingShingle | Bikram Singh Amit Kumar Verma Kasip Tiwari Rajeev Joshi Above ground tree biomass modeling using machine learning algorithms in western Terai Sal Forest of Nepal Heliyon Machine learning algorithms Above ground tree biomass Sentinel-2A Sustainable |
| title | Above ground tree biomass modeling using machine learning algorithms in western Terai Sal Forest of Nepal |
| title_full | Above ground tree biomass modeling using machine learning algorithms in western Terai Sal Forest of Nepal |
| title_fullStr | Above ground tree biomass modeling using machine learning algorithms in western Terai Sal Forest of Nepal |
| title_full_unstemmed | Above ground tree biomass modeling using machine learning algorithms in western Terai Sal Forest of Nepal |
| title_short | Above ground tree biomass modeling using machine learning algorithms in western Terai Sal Forest of Nepal |
| title_sort | above ground tree biomass modeling using machine learning algorithms in western terai sal forest of nepal |
| topic | Machine learning algorithms Above ground tree biomass Sentinel-2A Sustainable |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023086930 |
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