Ecological condition indicators for dry forest: Forest structure variables estimation with NDVI texture metrics and SAR variables

Autores
Alvarez, María Paula; Bellis, Laura Marisa; Arcamone, Julieta Rocio; Silvetti, Luna Emilce; Gavier Pizarro, Gregorio Ignacio
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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
Fuente
Remote Sensing Applications: Society and Environment 37 : 101485. (January 2025)
Materia
Bosques
Bosque Seco
Ecología
Índice Normalizado Diferencial de la Vegetación
Teledetección
Forests
Dry Forests
Ecology
Normalized Difference Vegetation Index
Remote Sensing
NDVI
Nivel de accesibilidad
acceso restringido
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/21379
Acceder
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oai_identifier_str oai:localhost:20.500.12123/21379
network_acronym_str INTADig
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network_name_str INTA Digital (INTA)
spelling Ecological condition indicators for dry forest: Forest structure variables estimation with NDVI texture metrics and SAR variablesAlvarez, María PaulaBellis, Laura MarisaArcamone, Julieta RocioSilvetti, Luna EmilceGavier Pizarro, Gregorio IgnacioBosquesBosque SecoEcologíaÍndice Normalizado Diferencial de la VegetaciónTeledetecciónForestsDry ForestsEcologyNormalized Difference Vegetation IndexRemote SensingNDVIThe 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 VegetalesFil: Alvarez, María Paula. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Alvarez, María Paula. Universidad Nacional de Córdoba; ArgentinaFil: Alvarez, María Paula. Instituto de Altos Estudios Espaciales “Mario Gulich”; Argentina.Fil: Bellis, Laura Marisa. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bellis, Laura Marisa. Universidad Nacional de Córdoba; ArgentinaFil: Bellis, Laura Marisa. Instituto de Altos Estudios Espaciales “Mario Gulich”; Argentina.Fil: Arcamone, Julieta Rocio. Consejo Nacional de Investigaciones Científicas y Tecnológicas; ArgentinaFil: Arcamone, Julieta Rocio. Instituto de Altos Estudios Espaciales “Mario Gulich”; ArgentinaFil: Silvetti, Luna Emilce. Consejo Nacional de Investigaciones Científicas y Tecnológicas; ArgentinaFil: Silvetti, Luna Emilce. Instituto de Altos Estudios Espaciales “Mario Gulich”; ArgentinaFil: Gavier Pizarro, Gregorio Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gavier Pizarro, Gregorio Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Fisiología y Recursos Genéticos Vegetales; ArgentinaElsevier2025-02-21T10:23:05Z2025-02-21T10:23:05Z2025-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/21379https://www.sciencedirect.com/science/article/abs/pii/S23529385250003822352-9385https://doi.org/10.1016/j.rsase.2025.101485Remote Sensing Applications: Society and Environment 37 : 101485. (January 2025)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-10-23T11:19:24Zoai:localhost:20.500.12123/21379instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-10-23 11:19:24.57INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv 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
spellingShingle Ecological condition indicators for dry forest: Forest structure variables estimation with NDVI texture metrics and SAR variables
Alvarez, María Paula
Bosques
Bosque Seco
Ecología
Índice Normalizado Diferencial de la Vegetación
Teledetección
Forests
Dry Forests
Ecology
Normalized Difference Vegetation Index
Remote Sensing
NDVI
title_short 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_sort Ecological condition indicators for dry forest: Forest structure variables estimation with NDVI texture metrics and SAR variables
dc.creator.none.fl_str_mv Alvarez, María Paula
Bellis, Laura Marisa
Arcamone, Julieta Rocio
Silvetti, Luna Emilce
Gavier Pizarro, Gregorio Ignacio
author Alvarez, María Paula
author_facet Alvarez, María Paula
Bellis, Laura Marisa
Arcamone, Julieta Rocio
Silvetti, Luna Emilce
Gavier Pizarro, Gregorio Ignacio
author_role author
author2 Bellis, Laura Marisa
Arcamone, Julieta Rocio
Silvetti, Luna Emilce
Gavier Pizarro, Gregorio Ignacio
author2_role author
author
author
author
dc.subject.none.fl_str_mv Bosques
Bosque Seco
Ecología
Índice Normalizado Diferencial de la Vegetación
Teledetección
Forests
Dry Forests
Ecology
Normalized Difference Vegetation Index
Remote Sensing
NDVI
topic Bosques
Bosque Seco
Ecología
Índice Normalizado Diferencial de la Vegetación
Teledetección
Forests
Dry Forests
Ecology
Normalized Difference Vegetation Index
Remote Sensing
NDVI
dc.description.none.fl_txt_mv 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
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.
publishDate 2025
dc.date.none.fl_str_mv 2025-02-21T10:23:05Z
2025-02-21T10:23:05Z
2025-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv 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
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
identifier_str_mv 2352-9385
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv 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)
eu_rights_str_mv restrictedAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Remote Sensing Applications: Society and Environment 37 : 101485. (January 2025)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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