Estimation of the environment component of the Water Poverty Index via remote sensing in semi-arid zones

López-Álvarez, Briseida; Urbano-Peña, M. A.; Morán-Ramírez, Janete; Ramos-Leal, José Alfredo; Tuxpan, José

doi:10.1080/02626667.2020.1839081

Cited by 7 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Formula References d Difference Vegetation Index (NDVI) 𝑁𝐷𝑉𝐼 𝑁𝐼𝑅 𝑅 𝑁𝐼𝑅 𝑅 [27] Ratio Vegetation Index (RVI) 𝑅𝑉𝐼 𝑁𝐼𝑅 𝑅 [28] zed Difference Infrared Index (NDII) 𝑁𝐷𝐼𝐼 𝑁𝐼𝑅 𝑆𝑊𝐼𝑅1 𝑁𝐼𝑅 𝑆𝑊𝐼𝑅1 [29] ized Difference Green Index (NDGI) 𝑁𝐷𝐺𝐼 𝐺 𝑅 𝐺 𝑅 [30] hanced Vegetation Index (EVI) 𝐸𝑉𝐼 2.5 𝑁𝐼𝑅 𝑅 𝑁𝐼𝑅 6 𝑅 7.5 * 𝐵 1 [31] ference Vegetation Index (DVI) 𝐷𝑉𝐼 𝑁𝐼𝑅 𝑅 [32] djusted Vegetation Index (SAVI) 𝑆𝐴𝑉𝐼 𝑁𝐼𝑅 𝑅 𝑁𝐼𝑅 𝑅 0.5 [33] Chlorophyll Index (CI green) 𝐶𝐼𝑔𝑟𝑒𝑒𝑛 𝑁𝐼𝑅 𝐺 [34] otenoid Reflectance Index (CRI) Furthermore, this study derived various optical remote sensing factors from preprocessed Sentinel-2 satellite images, including both single-band images and those with a diverse array of vegetation indices formulated through combinations of bands (Table 7). Specially, the B10 band, representing a 60 m cirrus band with minimal contribution to the surface information, was excluded during the atmospheric correction process [26].…”

Section: Vegetation Indexmentioning

confidence: 99%

Regional Forest Structure Evaluation Model Based on Remote Sensing and Field Survey Data

Lin,

Wen,

et al. 2024

Forests

View full text Add to dashboard Cite

The assessment of a forest’s structure is pivotal in guiding effective forest management, conservation efforts, and ensuring sustainable development. However, traditional evaluation methods often focus on isolated forest parameters and incur substantial data acquisition costs. To address these limitations, this study introduces a cost-effective and innovative evaluation model that incorporates remote sensing imagery and machine learning algorithms. This model holistically considers the forest composition, the tree age structure, and spatial configuration. Using a comprehensive approach, the forest structure in Longquan City was evaluated at the stand level and categorized into three distinct categories: good, moderate, and poor. The construction of this evaluation model drew upon multiple data sources, namely Sentinel-2 imagery, digital elevation models (DEMs), and forest resource planning and design survey data. The model employed the Recursive Feature Elimination with Cross-Validation (RFECV) method for feature selection, alongside various machine learning algorithms. The key findings from this research are summarized as follows: The application of the RFECV method proved effective in eliminating irrelevant factors, reducing data dimensionality and, subsequently, enhancing the model’s generalizability; among the tested machine learning algorithms, the CatBoost model emerged as the most accurate and stable across all the datasets; specifically, the CatBoost model achieved an impressive overall accuracy of 88.07%, a kappa coefficient of 0.6833, and a recall rate of 76.86%. These results significantly surpass the classification precision of previous methods. The forest structure assessment of Longquan City revealed notable variations in the forest quality distribution. Notably, forests classified as “good” quality comprised 11.18% of the total, while “medium” quality forests constituted the majority at 76.77%. In contrast, “poor” quality forests accounted for a relatively minor proportion of the total, at 12.05%. The distribution findings provide valuable insights for targeted forest management and conservation strategies.

show abstract