Evaluation of multiscale canopy height maps in Patagonian native forests
- Autores
- Lencinas, José Daniel; Letourneau, Federico Jorge; Salvaré, Fernando; Loguercio, Gabriel; Walentowski, Helge
- Año de publicación
- 2026
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Canopy height is a critical metric for assessing forest biomass, productivity, and ecological condition. Consequently, its rapid and accurate estimation remains essential for evaluating terrestrial ecosystem processes, dynamics, and services. In recent years, efforts to predict canopy height by integrating remote sensing technologies and artificial intelligence (AI) from regional to global scales have proliferated. This study evaluates the vertical accuracy of multiscale canopy height models (CHMs) in Andean Patagonia, assessing the performance of global (the High−Resolution Canopy Height Model of the Earth, HRCH; and the Global Map of Tree Canopy Height, GMTCH) and regional (the Nationwide Scale Native Forest Structure Maps, NNFS; and the Canopy Height Mapper, CH−GEE) canopy height products at spatial resolutions ranging from 1 m to 30 m. Model−derived vertical accuracy was quantified by benchmarking estimates against in situ dominant height references, which were calculated from forest inventory plots spanning representative native stands along altitudinal gradients. We assessed model performance using three criteria: observed−versus−predicted regression analysis, mixed−effects modelling of prediction errors, and canopy height profiling. Overall, mean absolute error (MAE) values ranged from 5.6 m (HRCH) to 7.9 m (GMTCH), while root mean square error (RMSE) values spanned from 6.6 m (HRCH) to 9.9 m (GMTCH). Compared with dominant tree−height field measurements, HRCH, CH−GEE, and NNFS tended, on average, to overestimate canopy height, whereas GMTCH exhibited marked underestimation. Our results indicate that HRCH, followed by CH−GEE, achieves the best performance and calibration, exhibiting moderate agreement with field data. In contrast, canopy height predictions from GMTCH and NNFS failed calibration and displayed poor predictive power. Prediction errors were strongly CHM dependent and systematically influenced by elevation, with all CHMs exhibiting increasing overestimation along the altitudinal gradient. GMTCH showed the greatest sensitivity to elevation, indicating reduced predictive accuracy in complex mountainous terrain. In contrast, HRCH was comparatively insensitive to topographic variability, whereas CH−GEE and NNFS displayed intermediate but more heterogeneous responses. Forest type was also a significant source of systematic prediction error, yielding differences from substantial overestimation in low−stature Nothofagus antarctica shrublands to consistent underestimation in tall, structurally complex Nothofagus dombeyi forests. HRCH, NNFS, and CH−GEE tended to overestimate canopy height at dominant heights up to approximately 20 m, after which errors shifted toward underestimation. Conversely, GMTCH systematically underestimated canopy height across the full range of dominant heights. None of the evaluated CHMs achieved the ±2 m accuracy threshold commonly considered suitable for operational forest height assessment. Nevertheless, the performance of HRCH and CH−GEE suggests that these products can provide valuable regional−scale information where high−resolution local CHMs or extensive airborne LiDAR coverage are unavailable. Overall, our results provide realistic expectations of the capabilities and limitations of current−generation CHMs and support their application in forest monitoring and ecological assessments across data−limited temperate mountain forests.
EEA Bariloche
Fil: Lencinas, José Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigación y Extensión Forestal Andino Patagónico (CIEFAP); Argentina
Fil: Letourneau, Federico Jorge. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche (IFAB); Argentina
Fil: Letourneau, Federico Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB); Argentina
Fil: Salvaré, Fernando. Universidad Nacional de Río Negro. Escuela de Producción, Tecnología y Medio Ambiente; Argentina
Fil: Loguercio, Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigación y Extensión Forestal Andino Patagónico (CIEFAP); Argentina
Fil: Loguercio, Gabriel. Universidad Nacional de la Patagonia San Juan Bosco. Facultad de Ingeniería. Departamento de Forestales; Argentina
Fil: Walentowski, Helge. University of Applied Sciences and Arts. Faculty of Resource Management; Alemania - Fuente
- Sylwan 170 (7-8) : 445-468. (July−August 2026)
- Materia
-
Cubierta de Copas
Teledetección
Altura del Arbol
Bosques
Bosque Primario
Canopy
Remote Sensing
Tree Height
Forests
Nothofagus
Primary Forests
LiDAR
Bosques Nativos
Región Patagónica - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
.jpg)
- Institución
- Instituto Nacional de Tecnología Agropecuaria
- OAI Identificador
- oai:localhost:20.500.12123/27871
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Evaluation of multiscale canopy height maps in Patagonian native forestsOcena wieloskalowych map wysokości koron drzew w rodzimych lasach PatagoniiLencinas, José DanielLetourneau, Federico JorgeSalvaré, FernandoLoguercio, GabrielWalentowski, HelgeCubierta de CopasTeledetecciónAltura del ArbolBosquesBosque PrimarioCanopyRemote SensingTree HeightForestsNothofagusPrimary ForestsLiDARBosques NativosRegión PatagónicaCanopy height is a critical metric for assessing forest biomass, productivity, and ecological condition. Consequently, its rapid and accurate estimation remains essential for evaluating terrestrial ecosystem processes, dynamics, and services. In recent years, efforts to predict canopy height by integrating remote sensing technologies and artificial intelligence (AI) from regional to global scales have proliferated. This study evaluates the vertical accuracy of multiscale canopy height models (CHMs) in Andean Patagonia, assessing the performance of global (the High−Resolution Canopy Height Model of the Earth, HRCH; and the Global Map of Tree Canopy Height, GMTCH) and regional (the Nationwide Scale Native Forest Structure Maps, NNFS; and the Canopy Height Mapper, CH−GEE) canopy height products at spatial resolutions ranging from 1 m to 30 m. Model−derived vertical accuracy was quantified by benchmarking estimates against in situ dominant height references, which were calculated from forest inventory plots spanning representative native stands along altitudinal gradients. We assessed model performance using three criteria: observed−versus−predicted regression analysis, mixed−effects modelling of prediction errors, and canopy height profiling. Overall, mean absolute error (MAE) values ranged from 5.6 m (HRCH) to 7.9 m (GMTCH), while root mean square error (RMSE) values spanned from 6.6 m (HRCH) to 9.9 m (GMTCH). Compared with dominant tree−height field measurements, HRCH, CH−GEE, and NNFS tended, on average, to overestimate canopy height, whereas GMTCH exhibited marked underestimation. Our results indicate that HRCH, followed by CH−GEE, achieves the best performance and calibration, exhibiting moderate agreement with field data. In contrast, canopy height predictions from GMTCH and NNFS failed calibration and displayed poor predictive power. Prediction errors were strongly CHM dependent and systematically influenced by elevation, with all CHMs exhibiting increasing overestimation along the altitudinal gradient. GMTCH showed the greatest sensitivity to elevation, indicating reduced predictive accuracy in complex mountainous terrain. In contrast, HRCH was comparatively insensitive to topographic variability, whereas CH−GEE and NNFS displayed intermediate but more heterogeneous responses. Forest type was also a significant source of systematic prediction error, yielding differences from substantial overestimation in low−stature Nothofagus antarctica shrublands to consistent underestimation in tall, structurally complex Nothofagus dombeyi forests. HRCH, NNFS, and CH−GEE tended to overestimate canopy height at dominant heights up to approximately 20 m, after which errors shifted toward underestimation. Conversely, GMTCH systematically underestimated canopy height across the full range of dominant heights. None of the evaluated CHMs achieved the ±2 m accuracy threshold commonly considered suitable for operational forest height assessment. Nevertheless, the performance of HRCH and CH−GEE suggests that these products can provide valuable regional−scale information where high−resolution local CHMs or extensive airborne LiDAR coverage are unavailable. Overall, our results provide realistic expectations of the capabilities and limitations of current−generation CHMs and support their application in forest monitoring and ecological assessments across data−limited temperate mountain forests.EEA BarilocheFil: Lencinas, José Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigación y Extensión Forestal Andino Patagónico (CIEFAP); ArgentinaFil: Letourneau, Federico Jorge. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche (IFAB); ArgentinaFil: Letourneau, Federico Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB); ArgentinaFil: Salvaré, Fernando. Universidad Nacional de Río Negro. Escuela de Producción, Tecnología y Medio Ambiente; ArgentinaFil: Loguercio, Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigación y Extensión Forestal Andino Patagónico (CIEFAP); ArgentinaFil: Loguercio, Gabriel. Universidad Nacional de la Patagonia San Juan Bosco. Facultad de Ingeniería. Departamento de Forestales; ArgentinaFil: Walentowski, Helge. University of Applied Sciences and Arts. Faculty of Resource Management; AlemaniaPolish Forest Society2026-09-21T14:08:08Z2026-09-21T14:08:08Z2026-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/27871https://sylwan-journal.pl/apex/f?p=sylwan:10:::NO::P10_NAZWA_PLIKU,P10_ARTYKUL,P10_ZESZYT_NEW:41710500817512431/2026_78_445au.pdf,2026008,2026_70039-7660https://doi.org/10.26202/sylwan.2026008Sylwan 170 (7-8) : 445-468. (July−August 2026)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2026-09-24T11:43:52Zoai:localhost:20.500.12123/27871instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2026-09-24 11:43:52.506INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse |
| dc.title.none.fl_str_mv |
Evaluation of multiscale canopy height maps in Patagonian native forests Ocena wieloskalowych map wysokości koron drzew w rodzimych lasach Patagonii |
| title |
Evaluation of multiscale canopy height maps in Patagonian native forests |
| spellingShingle |
Evaluation of multiscale canopy height maps in Patagonian native forests Lencinas, José Daniel Cubierta de Copas Teledetección Altura del Arbol Bosques Bosque Primario Canopy Remote Sensing Tree Height Forests Nothofagus Primary Forests LiDAR Bosques Nativos Región Patagónica |
| title_short |
Evaluation of multiscale canopy height maps in Patagonian native forests |
| title_full |
Evaluation of multiscale canopy height maps in Patagonian native forests |
| title_fullStr |
Evaluation of multiscale canopy height maps in Patagonian native forests |
| title_full_unstemmed |
Evaluation of multiscale canopy height maps in Patagonian native forests |
| title_sort |
Evaluation of multiscale canopy height maps in Patagonian native forests |
| dc.creator.none.fl_str_mv |
Lencinas, José Daniel Letourneau, Federico Jorge Salvaré, Fernando Loguercio, Gabriel Walentowski, Helge |
| author |
Lencinas, José Daniel |
| author_facet |
Lencinas, José Daniel Letourneau, Federico Jorge Salvaré, Fernando Loguercio, Gabriel Walentowski, Helge |
| author_role |
author |
| author2 |
Letourneau, Federico Jorge Salvaré, Fernando Loguercio, Gabriel Walentowski, Helge |
| author2_role |
author author author author |
| dc.subject.none.fl_str_mv |
Cubierta de Copas Teledetección Altura del Arbol Bosques Bosque Primario Canopy Remote Sensing Tree Height Forests Nothofagus Primary Forests LiDAR Bosques Nativos Región Patagónica |
| topic |
Cubierta de Copas Teledetección Altura del Arbol Bosques Bosque Primario Canopy Remote Sensing Tree Height Forests Nothofagus Primary Forests LiDAR Bosques Nativos Región Patagónica |
| dc.description.none.fl_txt_mv |
Canopy height is a critical metric for assessing forest biomass, productivity, and ecological condition. Consequently, its rapid and accurate estimation remains essential for evaluating terrestrial ecosystem processes, dynamics, and services. In recent years, efforts to predict canopy height by integrating remote sensing technologies and artificial intelligence (AI) from regional to global scales have proliferated. This study evaluates the vertical accuracy of multiscale canopy height models (CHMs) in Andean Patagonia, assessing the performance of global (the High−Resolution Canopy Height Model of the Earth, HRCH; and the Global Map of Tree Canopy Height, GMTCH) and regional (the Nationwide Scale Native Forest Structure Maps, NNFS; and the Canopy Height Mapper, CH−GEE) canopy height products at spatial resolutions ranging from 1 m to 30 m. Model−derived vertical accuracy was quantified by benchmarking estimates against in situ dominant height references, which were calculated from forest inventory plots spanning representative native stands along altitudinal gradients. We assessed model performance using three criteria: observed−versus−predicted regression analysis, mixed−effects modelling of prediction errors, and canopy height profiling. Overall, mean absolute error (MAE) values ranged from 5.6 m (HRCH) to 7.9 m (GMTCH), while root mean square error (RMSE) values spanned from 6.6 m (HRCH) to 9.9 m (GMTCH). Compared with dominant tree−height field measurements, HRCH, CH−GEE, and NNFS tended, on average, to overestimate canopy height, whereas GMTCH exhibited marked underestimation. Our results indicate that HRCH, followed by CH−GEE, achieves the best performance and calibration, exhibiting moderate agreement with field data. In contrast, canopy height predictions from GMTCH and NNFS failed calibration and displayed poor predictive power. Prediction errors were strongly CHM dependent and systematically influenced by elevation, with all CHMs exhibiting increasing overestimation along the altitudinal gradient. GMTCH showed the greatest sensitivity to elevation, indicating reduced predictive accuracy in complex mountainous terrain. In contrast, HRCH was comparatively insensitive to topographic variability, whereas CH−GEE and NNFS displayed intermediate but more heterogeneous responses. Forest type was also a significant source of systematic prediction error, yielding differences from substantial overestimation in low−stature Nothofagus antarctica shrublands to consistent underestimation in tall, structurally complex Nothofagus dombeyi forests. HRCH, NNFS, and CH−GEE tended to overestimate canopy height at dominant heights up to approximately 20 m, after which errors shifted toward underestimation. Conversely, GMTCH systematically underestimated canopy height across the full range of dominant heights. None of the evaluated CHMs achieved the ±2 m accuracy threshold commonly considered suitable for operational forest height assessment. Nevertheless, the performance of HRCH and CH−GEE suggests that these products can provide valuable regional−scale information where high−resolution local CHMs or extensive airborne LiDAR coverage are unavailable. Overall, our results provide realistic expectations of the capabilities and limitations of current−generation CHMs and support their application in forest monitoring and ecological assessments across data−limited temperate mountain forests. EEA Bariloche Fil: Lencinas, José Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigación y Extensión Forestal Andino Patagónico (CIEFAP); Argentina Fil: Letourneau, Federico Jorge. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche (IFAB); Argentina Fil: Letourneau, Federico Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB); Argentina Fil: Salvaré, Fernando. Universidad Nacional de Río Negro. Escuela de Producción, Tecnología y Medio Ambiente; Argentina Fil: Loguercio, Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigación y Extensión Forestal Andino Patagónico (CIEFAP); Argentina Fil: Loguercio, Gabriel. Universidad Nacional de la Patagonia San Juan Bosco. Facultad de Ingeniería. Departamento de Forestales; Argentina Fil: Walentowski, Helge. University of Applied Sciences and Arts. Faculty of Resource Management; Alemania |
| description |
Canopy height is a critical metric for assessing forest biomass, productivity, and ecological condition. Consequently, its rapid and accurate estimation remains essential for evaluating terrestrial ecosystem processes, dynamics, and services. In recent years, efforts to predict canopy height by integrating remote sensing technologies and artificial intelligence (AI) from regional to global scales have proliferated. This study evaluates the vertical accuracy of multiscale canopy height models (CHMs) in Andean Patagonia, assessing the performance of global (the High−Resolution Canopy Height Model of the Earth, HRCH; and the Global Map of Tree Canopy Height, GMTCH) and regional (the Nationwide Scale Native Forest Structure Maps, NNFS; and the Canopy Height Mapper, CH−GEE) canopy height products at spatial resolutions ranging from 1 m to 30 m. Model−derived vertical accuracy was quantified by benchmarking estimates against in situ dominant height references, which were calculated from forest inventory plots spanning representative native stands along altitudinal gradients. We assessed model performance using three criteria: observed−versus−predicted regression analysis, mixed−effects modelling of prediction errors, and canopy height profiling. Overall, mean absolute error (MAE) values ranged from 5.6 m (HRCH) to 7.9 m (GMTCH), while root mean square error (RMSE) values spanned from 6.6 m (HRCH) to 9.9 m (GMTCH). Compared with dominant tree−height field measurements, HRCH, CH−GEE, and NNFS tended, on average, to overestimate canopy height, whereas GMTCH exhibited marked underestimation. Our results indicate that HRCH, followed by CH−GEE, achieves the best performance and calibration, exhibiting moderate agreement with field data. In contrast, canopy height predictions from GMTCH and NNFS failed calibration and displayed poor predictive power. Prediction errors were strongly CHM dependent and systematically influenced by elevation, with all CHMs exhibiting increasing overestimation along the altitudinal gradient. GMTCH showed the greatest sensitivity to elevation, indicating reduced predictive accuracy in complex mountainous terrain. In contrast, HRCH was comparatively insensitive to topographic variability, whereas CH−GEE and NNFS displayed intermediate but more heterogeneous responses. Forest type was also a significant source of systematic prediction error, yielding differences from substantial overestimation in low−stature Nothofagus antarctica shrublands to consistent underestimation in tall, structurally complex Nothofagus dombeyi forests. HRCH, NNFS, and CH−GEE tended to overestimate canopy height at dominant heights up to approximately 20 m, after which errors shifted toward underestimation. Conversely, GMTCH systematically underestimated canopy height across the full range of dominant heights. None of the evaluated CHMs achieved the ±2 m accuracy threshold commonly considered suitable for operational forest height assessment. Nevertheless, the performance of HRCH and CH−GEE suggests that these products can provide valuable regional−scale information where high−resolution local CHMs or extensive airborne LiDAR coverage are unavailable. Overall, our results provide realistic expectations of the capabilities and limitations of current−generation CHMs and support their application in forest monitoring and ecological assessments across data−limited temperate mountain forests. |
| publishDate |
2026 |
| dc.date.none.fl_str_mv |
2026-09-21T14:08:08Z 2026-09-21T14:08:08Z 2026-09 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
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article |
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http://hdl.handle.net/20.500.12123/27871 https://sylwan-journal.pl/apex/f?p=sylwan:10:::NO::P10_NAZWA_PLIKU,P10_ARTYKUL,P10_ZESZYT_NEW:41710500817512431/2026_78_445au.pdf,2026008,2026_7 0039-7660 https://doi.org/10.26202/sylwan.2026008 |
| url |
http://hdl.handle.net/20.500.12123/27871 https://sylwan-journal.pl/apex/f?p=sylwan:10:::NO::P10_NAZWA_PLIKU,P10_ARTYKUL,P10_ZESZYT_NEW:41710500817512431/2026_78_445au.pdf,2026008,2026_7 https://doi.org/10.26202/sylwan.2026008 |
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eng |
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eng |
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info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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application/pdf |
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Polish Forest Society |
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Polish Forest Society |
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Sylwan 170 (7-8) : 445-468. (July−August 2026) reponame:INTA Digital (INTA) instname:Instituto Nacional de Tecnología Agropecuaria |
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