Scholarlink

<p>The Tubarão River Basin, a coastal watershed within the Atlantic Forest biome in southern Brazil, is increasingly exposed to human and climatic pressures. This study evaluated spatiotemporal patterns of terrestrial environmental quality from 2014 to 2023 using the Remote Sensing Ecological Index (RSEI), derived from Landsat-8 imagery through Principal Component Analysis (PCA). The index integrated vegetation greenness (NDVI), soil exposure (NDSI), surface moisture (LSM), and land surface temperature (LST), with NDSI and LST inverted to ensure consistent ecological interpretation and permanent water bodies masked. The first principal component (PC1) was normalized to a 0–1 scale and used as RSEI. Annual values were summarized at the municipal level, and relationships with land use were assessed using linear mixed-effects models. RSEI values remained relatively stable over time (0.69–0.80) but showed marked spatial heterogeneity. Higher values occurred in forested central and western municipalities, while lower values were associated with urbanized coastal areas. Forest cover had a significant positive effect on RSEI (<i>p</i> &lt; 0.001), whereas agricultural and urban areas showed significant negative effects (<i>p</i> &lt; 0.05). Despite increasing LST across municipalities, its relationship with RSEI was not significant, indicating that land-use composition outweighed isolated thermal effects. These findings highlight the utility of PCA-based RSEI for integrated environmental monitoring in tropical coastal basins.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Assessing land use and climate-related trends in environmental quality using RSEI in a neotropical coastal basin

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月30日

查看全文
  • Marcelo Henrique Schmitz,
  • José Antonio Domingues Teixeira-Junior,
  • David Valença Dantas,
  • Eduardo Gentil

摘要

The Tubarão River Basin, a coastal watershed within the Atlantic Forest biome in southern Brazil, is increasingly exposed to human and climatic pressures. This study evaluated spatiotemporal patterns of terrestrial environmental quality from 2014 to 2023 using the Remote Sensing Ecological Index (RSEI), derived from Landsat-8 imagery through Principal Component Analysis (PCA). The index integrated vegetation greenness (NDVI), soil exposure (NDSI), surface moisture (LSM), and land surface temperature (LST), with NDSI and LST inverted to ensure consistent ecological interpretation and permanent water bodies masked. The first principal component (PC1) was normalized to a 0–1 scale and used as RSEI. Annual values were summarized at the municipal level, and relationships with land use were assessed using linear mixed-effects models. RSEI values remained relatively stable over time (0.69–0.80) but showed marked spatial heterogeneity. Higher values occurred in forested central and western municipalities, while lower values were associated with urbanized coastal areas. Forest cover had a significant positive effect on RSEI (p < 0.001), whereas agricultural and urban areas showed significant negative effects (p < 0.05). Despite increasing LST across municipalities, its relationship with RSEI was not significant, indicating that land-use composition outweighed isolated thermal effects. These findings highlight the utility of PCA-based RSEI for integrated environmental monitoring in tropical coastal basins.