Application of Remote Sensing Vegetation Indices for Forest Cover Assessments

Khunrattanasiri, Weeraphart

doi:10.1007/978-981-19-4200-6_8

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…Third, using the identified unchanged forest regions, we applied spatial filtering to all planted and natural forest samples, preserving only the samples that intersected with the unchanged forest areas, thereby obtaining an initial set of field samples suitable for different time intervals. Fourth, we used the Normalized Difference Vegetation Index (NDVI) time series from Landsat to determine if the minimum NDVI value for each sample in the created dataset from 1990 to 2020 was equal to or greater than 0.6 55 , retaining samples that satisfied this condition. By following these procedures, we acquired a dataset containing 124,407 samples of planted and natural forests, with 70% allocated for model training and 30% for validation (Supplementary Fig.…”

Section: Field Samplesmentioning

confidence: 99%

Carbon storage through China’s planted forest expansion

Cheng,

Yang,

Tao

et al. 2024

Nat Commun

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China’s extensive planted forests play a crucial role in carbon storage, vital for climate change mitigation. However, the complex spatiotemporal dynamics of China’s planted forest area and its carbon storage remain uncaptured. Here we reveal such changes in China’s planted forests from 1990 to 2020 using satellite and field data. Results show a doubling of planted forest area, a trend that intensified post-2000. These changes lead to China’s planted forest carbon storage increasing from 675.6 ± 12.5 Tg C in 1990 to 1,873.1 ± 16.2 Tg C in 2020, with an average rate of ~ 40 Tg C yr−1. The area expansion of planted forests contributed ~ 53% (637.2 ± 5.4 Tg C) of the total above increased carbon storage in planted forests compared with planted forest growth. This proactive policy-driven expansion of planted forests has catalyzed a swift increase in carbon storage, aligning with China’s Carbon Neutrality Target for 2060.

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Section: Field Samplesmentioning

confidence: 99%

Carbon storage through China’s planted forest expansion

Cheng,

Yang,

Tao

et al. 2024

Nat Commun

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“…The influence of vegetation cover is quantified through a dimensionless factor obtained from the normalized vegetation index (NDVI) derived from satellite imagery. This index translates vegetation reflectance into a percentage of vegetation cover (Khunrattanasiri, 2023). However, in response to seasonal canopy variations, our study employs an alternative method, replacing the traditional C-factor with the NDVI vegetation index (Macedo et al 2021).…”

Section: Vegetation Cover Factor (C)mentioning

confidence: 99%

Assessing Soil Erosion Dynamics in the Rmel Watershed, Northern Morocco by Using the Rusle Model, Gis, and Remote Sensing Integration

Ezzahraa BOUAYAD,

EL IDRYSY,

OUALLALI

et al. 2023

AgricultForest

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“…Recent scientific literature showcases a strong association between remotely sensed data and forest stand parameters. The utilization of vegetation indices, derived through spectral transformations of a minimum of two optical bands, plays a crucial role in obtaining valuable vegetation-related properties [8]. Geographic Information Systems (GIS) has been making an impact in diverse domains that include geography, environmental sciences, natural resources, forestry, agriculture, food, manufacturing, banking, and health services.…”

Section: Introductionmentioning

confidence: 99%

“…The Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), and Soil-Adjusted Vegetation Index (SAVI) have been extensively referenced in numerous research articles, finding significant utilization in forest studies. These indices have played a crucial role in exploring the interrelationships between various forest parameters, such as Diameter at Breast Height (DBH), Percent Crown Cover, Tree Age Class, Tree Height, Basal Area, Tree Volume, and Aboveground Living Biomass [8]. By implementing the Normalized Difference Vegetation Indices (NDVI and SAVI), the dynamic nature of the local physiognomy can be observed, as a result, an open canopy with evident lushness is formed, justifying the preservation of the conserved area and this holds significant potential in terms of offering environmental and ecosystem services, as well as serving as a potential local climate modulator, particularly crucial in the context of regional and global climate variations [17,18].…”

Section: Introductionmentioning

confidence: 99%

Detection of Vegetation Cover Changes from 1988-2022 in the University of Baghdad Campus by Remote Sensing and GIS Techniques (Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI)

Mahdi,

Jasim

2023

IOP Conf. Ser.: Earth Environ. Sci.

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This study aims to investigate changes in vegetation cover of the University of Baghdad campus by Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) as a tool to determine the changes in vegetation cover on the University landscape were used for the period 1988 and 2022 for two seasons (Winter and Summer). Land cover was divided into four categories: Barren & buildings, Low, Medium, and Dense vegetation. The results showed an increase in NDVI values in winter (-0.2-0.5), and (-0.1-0.9) in 1988, and 2022 respectively, and recorded in summer (-0.3-0.5) and (-0.04-0.4) in 1988, and 2022 respectively. The highest value was recorded in winter of 2022. Barren & buildings recorded the highest value 1470630 Km2 with 43% in summer of 1988 while in 2022 recorded 1411156 Km2 with 40%, for both seasons. The Low vegetation recorded the highest value 130031 Km2 with 377% in winter 1988 and the lowest was recorded at 941671 Km2 with 28% in summer 2022. Medium vegetation was recorded the highest 787580 Km2 with 22% in winter 2022 and lowest 667730 Km2 with 19 %. Dense vegetation was the highest 413614 Km2 with 12 % in summer 2022 while the lowest was recorded 211763 Km2 with 6 % in winter 1988. SAVI values were recorded in winter (-0.3-0.7), (-0.1-0.4) in 1988 and 2022 respectively and in summer (-0.4 - 0.7)(-0.06 - 0.6) in 1988 and 2022 respectively. The results showed slight differences from NDVI values of vegetation cover (Dense). The integration of remote sensing and geographic information systems is an effective and sustainable tool for studying land cover changes in order to obtain reliable and accurate rapid and economical results.

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Application of Remote Sensing Vegetation Indices for Forest Cover Assessments

Cited by 4 publications

References 20 publications

Carbon storage through China’s planted forest expansion

Carbon storage through China’s planted forest expansion

Assessing Soil Erosion Dynamics in the Rmel Watershed, Northern Morocco by Using the Rusle Model, Gis, and Remote Sensing Integration

Detection of Vegetation Cover Changes from 1988-2022 in the University of Baghdad Campus by Remote Sensing and GIS Techniques (Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI)

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