Ecological condition indicators for dry forest: Forest structure variables estimation with NDVI texture metrics and SAR variables
The ecological condition of forest ecosystems is degraded. Limited prior research in vegetation has focused on monitoring ecological condition levels in dry forest at fine scale. We proposed a novel approach to obtain accurate indicators of the ecological condition of the Chaco Serrano forest (Córdo...
| Main Authors: | , , , , |
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| Format: | Artículo |
| Language: | Inglés |
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Elsevier
2025
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| Online Access: | http://hdl.handle.net/20.500.12123/21379 https://www.sciencedirect.com/science/article/abs/pii/S2352938525000382 https://doi.org/10.1016/j.rsase.2025.101485 |
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| author | Alvarez, María Paula Bellis, Laura Marisa Arcamone, Julieta Rocio Silvetti, Luna Emilce Gavier Pizarro, Gregorio Ignacio |
| author_browse | Alvarez, María Paula Arcamone, Julieta Rocio Bellis, Laura Marisa Gavier Pizarro, Gregorio Ignacio Silvetti, Luna Emilce |
| author_facet | Alvarez, María Paula Bellis, Laura Marisa Arcamone, Julieta Rocio Silvetti, Luna Emilce Gavier Pizarro, Gregorio Ignacio |
| author_sort | Alvarez, María Paula |
| collection | INTA Digital |
| description | The ecological condition of forest ecosystems is degraded. Limited prior research in vegetation has focused on monitoring ecological condition levels in dry forest at fine scale. We proposed a novel approach to obtain accurate indicators of the ecological condition of the Chaco Serrano forest (Córdoba, Argentina) by estimating forest structure variables (canopy cover (CC), diameter breast height (DBH_sum), number of woody individuals (NW) and two first axes of a principal component analysis (PC1 and PC2)) as a measure of forest degradation. To achieve this, first the correlation with two complementary groups of remote sensing derived data (texture metrics over Normalised difference vegetation index and SAR-derived data) was explored. Then, General linear models (GLM) were constructed using the most correlated remote sensing derived variables with forest structure variables as predictor variables. The best estimation was obtained to CC (r2=0.58, rmse=14,5%), followed by DBHsum (r2=0.37, rmse=156.6) and NW (r2=0.22, rmse=14.6), with an spatial arrangement consistent with field observations. Moreover, CC estimation was more accurate than those at regional and global scale, and highlights the importance of developing local models in areas that exhibit high ecological, geological, and human heterogeneity. In addition, other forest variables could also be evaluated, like floristic composition or others associated with functioning. Results offer valuable insights for developing management strategies suitable for each condition, and for future studies regarding the relationship of the mentioned condition and associated natural and anthropic factors. |
| format | Artículo |
| id | INTA21379 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | INTA213792025-02-21T10:26:11Z Ecological condition indicators for dry forest: Forest structure variables estimation with NDVI texture metrics and SAR variables Alvarez, María Paula Bellis, Laura Marisa Arcamone, Julieta Rocio Silvetti, Luna Emilce Gavier Pizarro, Gregorio Ignacio Bosques Bosque Seco Ecología Índice Normalizado Diferencial de la Vegetación Teledetección Forests Dry Forests Ecology Normalized Difference Vegetation Index Remote Sensing NDVI The ecological condition of forest ecosystems is degraded. Limited prior research in vegetation has focused on monitoring ecological condition levels in dry forest at fine scale. We proposed a novel approach to obtain accurate indicators of the ecological condition of the Chaco Serrano forest (Córdoba, Argentina) by estimating forest structure variables (canopy cover (CC), diameter breast height (DBH_sum), number of woody individuals (NW) and two first axes of a principal component analysis (PC1 and PC2)) as a measure of forest degradation. To achieve this, first the correlation with two complementary groups of remote sensing derived data (texture metrics over Normalised difference vegetation index and SAR-derived data) was explored. Then, General linear models (GLM) were constructed using the most correlated remote sensing derived variables with forest structure variables as predictor variables. The best estimation was obtained to CC (r2=0.58, rmse=14,5%), followed by DBHsum (r2=0.37, rmse=156.6) and NW (r2=0.22, rmse=14.6), with an spatial arrangement consistent with field observations. Moreover, CC estimation was more accurate than those at regional and global scale, and highlights the importance of developing local models in areas that exhibit high ecological, geological, and human heterogeneity. In addition, other forest variables could also be evaluated, like floristic composition or others associated with functioning. Results offer valuable insights for developing management strategies suitable for each condition, and for future studies regarding the relationship of the mentioned condition and associated natural and anthropic factors. Instituto de Fisiología y Recursos Genéticos Vegetales Fil: Alvarez, María Paula. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Alvarez, María Paula. Universidad Nacional de Córdoba; Argentina Fil: Alvarez, María Paula. Instituto de Altos Estudios Espaciales “Mario Gulich”; Argentina. Fil: Bellis, Laura Marisa. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Bellis, Laura Marisa. Universidad Nacional de Córdoba; Argentina Fil: Bellis, Laura Marisa. Instituto de Altos Estudios Espaciales “Mario Gulich”; Argentina. Fil: Arcamone, Julieta Rocio. Consejo Nacional de Investigaciones Científicas y Tecnológicas; Argentina Fil: Arcamone, Julieta Rocio. Instituto de Altos Estudios Espaciales “Mario Gulich”; Argentina Fil: Silvetti, Luna Emilce. Consejo Nacional de Investigaciones Científicas y Tecnológicas; Argentina Fil: Silvetti, Luna Emilce. Instituto de Altos Estudios Espaciales “Mario Gulich”; Argentina Fil: Gavier Pizarro, Gregorio Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Gavier Pizarro, Gregorio Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Fisiología y Recursos Genéticos Vegetales; Argentina 2025-02-21T10:23:05Z 2025-02-21T10:23:05Z 2025-01 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/21379 https://www.sciencedirect.com/science/article/abs/pii/S2352938525000382 2352-9385 https://doi.org/10.1016/j.rsase.2025.101485 eng info:eu-repo/semantics/restrictedAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf Elsevier Remote Sensing Applications: Society and Environment 37 : 101485. (January 2025) |
| spellingShingle | Bosques Bosque Seco Ecología Índice Normalizado Diferencial de la Vegetación Teledetección Forests Dry Forests Ecology Normalized Difference Vegetation Index Remote Sensing NDVI Alvarez, María Paula Bellis, Laura Marisa Arcamone, Julieta Rocio Silvetti, Luna Emilce Gavier Pizarro, Gregorio Ignacio Ecological condition indicators for dry forest: Forest structure variables estimation with NDVI texture metrics and SAR variables |
| title | Ecological condition indicators for dry forest: Forest structure variables estimation with NDVI texture metrics and SAR variables |
| title_full | Ecological condition indicators for dry forest: Forest structure variables estimation with NDVI texture metrics and SAR variables |
| title_fullStr | Ecological condition indicators for dry forest: Forest structure variables estimation with NDVI texture metrics and SAR variables |
| title_full_unstemmed | Ecological condition indicators for dry forest: Forest structure variables estimation with NDVI texture metrics and SAR variables |
| title_short | Ecological condition indicators for dry forest: Forest structure variables estimation with NDVI texture metrics and SAR variables |
| title_sort | ecological condition indicators for dry forest forest structure variables estimation with ndvi texture metrics and sar variables |
| topic | Bosques Bosque Seco Ecología Índice Normalizado Diferencial de la Vegetación Teledetección Forests Dry Forests Ecology Normalized Difference Vegetation Index Remote Sensing NDVI |
| url | http://hdl.handle.net/20.500.12123/21379 https://www.sciencedirect.com/science/article/abs/pii/S2352938525000382 https://doi.org/10.1016/j.rsase.2025.101485 |
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