Multi-temporal NDVI analysis using UAV images of tree crowns in a northern Mexican pine-oak forest

Gallardo-Salazar, José Luis; Rosas-Chavoya, Marcela; Pompa-García, Marín; López-Serrano, Pablito Marcelo; García-Montiel, Emily; Meléndez-Soto, Arnulfo; Jiménez-Jiménez, Sergio Iván

doi:10.1007/s11676-023-01639-w

Cited by 3 publications

(1 citation statement)

References 67 publications

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“…In previous studies, many researchers have focused on the use of the Normalized Different Vegetation Index (NDVI) to detect vegetation health (Stamford et al 2023), tree species (Ozdarici-Ok and Ok 2023; Sheeren et al 2016;Valderrama-Landeros et al 2018), forest monitoring (Gallardo-Salazar et al 2023;Pesaresi et al 2020;Suwanto et al 2021), agriculture (Ahmed 2016;Gandhi et al 2015) and mangrove density (Arfan et al 2024) but there is limited research on using NDVI to identify potential illegal activities such as illegal mining and logging. This study will contribute to filling this gap in the literature by applying NDVI analysis to identify possible illegal activities related to land use changes.…”

Section: Introductionmentioning

confidence: 99%

Integrated Supervised Classification of LULC in Identifying Musang King Durian Illegal Farming Location

Tarmizi,

Rizwan

2024

J. Sylva Lestari

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Musang King Durian (MKD) is in high demand due to its unique taste and aroma; hence, some opportunistic farmers grow it on a large scale. The issue arises when some MKD is planted on encroached land in remote places, making it impossible for local authorities to locate them. This study proposes to examine the changes in land use land cover (LULC) within the Benom Permanent Forest Reserved area that were reported to have land invasion activities caused by illegal MKD plantations between 2019 and 2022 using Sentinel imagery. The objectives were to investigate the location of illicit MKD farming at Mount Benom based on the interpretation of LULC changes and Normalized Different Vegetation Index (NDVI) analysis. ArcGIS and eCognition Developer software were used for data processing and analysis to identify water bodies, bare land, green areas, forests, and potential illegal MKD plantings (PIDP) areas. Between the years 2019 and 2022, it was found that there has been a significant rise in water bodies (36%), green areas (34%), and forests (19%). However, the potential illegal MKD plantation area fell by 59%, while bare land declined by 35%. These downsizing changes might occur due to illegal MKD destroyed operations executed in 2019 by the forestry department in Raub, Pahang. Keywords: illegal farming, image classification, land use land cover, supervised classification

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Section: Introductionmentioning

confidence: 99%

Integrated Supervised Classification of LULC in Identifying Musang King Durian Illegal Farming Location

Tarmizi,

Rizwan

2024

J. Sylva Lestari

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Enhanced Landslide Spatial Prediction Using Hybrid Deep Learning Model and SHAP Analysis: A Case Study of the Tuyen Quang-Ha Giang Expressway, Vietnam

Nguyen,

Nguyen

et al. 2024

J Indian Soc Remote Sens

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Landslide susceptibility assessment using novel hybridized methods based on the support vector regression

Jaafari

2024

Ecological Engineering

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Multi-temporal NDVI analysis using UAV images of tree crowns in a northern Mexican pine-oak forest

Cited by 3 publications

References 67 publications

Integrated Supervised Classification of LULC in Identifying Musang King Durian Illegal Farming Location

Integrated Supervised Classification of LULC in Identifying Musang King Durian Illegal Farming Location

Enhanced Landslide Spatial Prediction Using Hybrid Deep Learning Model and SHAP Analysis: A Case Study of the Tuyen Quang-Ha Giang Expressway, Vietnam

Landslide susceptibility assessment using novel hybridized methods based on the support vector regression

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