Assessment of Aboveground Biomass in a Tropical Dry Deciduous Forest Using PRISMA Data

Verma, Rajani Kant; Sharma, Laxmi Kant; Bhaveshkumar, Kariya Ishita; Rathore, Mahima Kanwar

doi:10.1007/s12524-024-01822-4

Cited by 2 publications

(3 citation statements)

References 35 publications

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“…It uses blue light reflectance measurements to correct for the atmospheric scattering effects that also influence the reflectance of red light [70]. The use of this vegetation index in biomass modeling in different landscapes and various forest stands is proven by other research [71][72][73].…”

Section: Discussionmentioning

confidence: 99%

An Integrating Framework for Biomass and Carbon Stock Spatialization and Dynamics Assessment Using Unmanned Aerial Vehicle LiDAR (LiDAR UAV) Data, Landsat Imagery, and Forest Survey Data in the Mediterranean Cork Oak Forest of Maamora

Fadil,

Sebari,

Ajerame

et al. 2024

Land

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Spatialization of biomass and carbon stocks is essential for a good understanding of the forest stand and its characteristics, especially in degraded Mediterranean cork oak forests. Furthermore, the analysis of biomass and carbon stock changes and dynamics is essential for understanding the carbon cycle, in particular carbon emissions and stocks, in order to make projections, especially in the context of climate change. In this research, we use a multidimensional framework integrating forest survey data, LiDAR UAV data, and extracted vegetation indices from Landsat imagery (NDVI, ARVI, CIG, etc.) to model and spatialize cork oak biomass and carbon stocks on a large scale. For this purpose, we explore the use of univariate and multivariate regression modeling and examine several types of regression, namely, multiple linear regression, stepwise linear regression, random forest regression, simple linear regression, logarithmic regression, and quadratic and cubic regression. The results show that for multivariate regression, stepwise regression gives good results, with R2 equal to 80% and 65% and RMSE equal to 2.59 and 1.52 Mg/ha for biomass and carbon stock, respectively. Random forest regression, chosen as the ML algorithm, gives acceptable results, explaining 80% and 60% of the variation in biomass and carbon stock, respectively, and an RMSE of 2.74 and 1.72 Mg/ha for biomass and carbon stock, respectively. For the univariate regression, the simple linear regression is chosen because it gives satisfactory results, close to those of the quadratic and cubic regressions, but with a simpler equation. The vegetation index chosen is ARVI, which shows good performance indices, close to those of the NDVI and CIG. The assessment of biomass and carbon stock changes in the study area over 35 years (1985–2020) showed a slight increase of less than 10 Mg/ha and a decrease in biomass and carbon stock over a large area.

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Section: Discussionmentioning

confidence: 99%

An Integrating Framework for Biomass and Carbon Stock Spatialization and Dynamics Assessment Using Unmanned Aerial Vehicle LiDAR (LiDAR UAV) Data, Landsat Imagery, and Forest Survey Data in the Mediterranean Cork Oak Forest of Maamora

Fadil,

Sebari,

Ajerame

et al. 2024

Land

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“…This issue comprises 17 articles, all focusing on Indian sites as study regions and encompassing various forest types, including two studies on mangroves. These articles can be broadly categorized as: (a) Forest biomass estimation using optical sensors, with attention to very high resolution and hyperspectral data (Pardeshi et al, 2024;Pasha & Dadhwal, 2024;Singh et al, 2024;Verma et al, 2024), (b) Utilization of SAR sensors, including polarimetric data, for biomass estimation (Ali & Khati, 2024;Bhavsar et al, 2024;Hati et al, 2024;Singhal et al, 2024), (c) Application of LiDAR sensors, both terrestrial and spaceborne, for biomass estimation Rodda et al, 2024aRodda et al, , 2024b, (d) Integration of multi-sensor EO data for biomass estimation (Behera et al, 2024;Prakash et al, 2024;Sainuddin et al, 2024;Sanam et al, 2024), and (e) Biomass product validation, regional studies, and application-focused research (Bhat et al, 2024).…”

Section: Rs-forest Biomass: Special Issuementioning

confidence: 99%

“…Their study estimated carbon sequestration by determining the difference in total carbon content, confirming the role of mangroves as a carbon sink. Verma et al (2024) evaluated the potential of the spaceborne hyperspectral sensor, PRISMA, for estimating AGB in a tropical dry deciduous forest of Gujarat. Their findings underscored the significant impact of vegetation indices and phenological conditions on AGB prediction.…”

Section: Forest Biomass Estimation Using Optical Sensorsmentioning

confidence: 99%

Forest Biomass Assessment Using Multisource Earth Observation Data: Techniques, Data Sets and Applications

Dadhwal,

Nandy

2024

J Indian Soc Remote Sens

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Assessment of Aboveground Biomass in a Tropical Dry Deciduous Forest Using PRISMA Data

Cited by 2 publications

References 35 publications

An Integrating Framework for Biomass and Carbon Stock Spatialization and Dynamics Assessment Using Unmanned Aerial Vehicle LiDAR (LiDAR UAV) Data, Landsat Imagery, and Forest Survey Data in the Mediterranean Cork Oak Forest of Maamora

An Integrating Framework for Biomass and Carbon Stock Spatialization and Dynamics Assessment Using Unmanned Aerial Vehicle LiDAR (LiDAR UAV) Data, Landsat Imagery, and Forest Survey Data in the Mediterranean Cork Oak Forest of Maamora

Forest Biomass Assessment Using Multisource Earth Observation Data: Techniques, Data Sets and Applications

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