Carmen Hernandez
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Carmen Hernandez has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Basilio Sierra
Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), Donostia-San Sebastián, Spain
1 shared publication - Carlos Roberto Sette Junior
Federal University of Goiás
1 shared publication - Domingos Lopes
Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Department of Forest Sciences and Landscape Architecture, University of Trás-Os-Montes Alto Douro (UTAD), Quinta dos Prados, Apartado 1013, 5000-911 Vila Real, Portugal
1 shared publication - Gabriela Calvario
Instituto Tecnologico y de Estudios Superiores de Occidente
1 shared publication - Helder Viana
Centre for Natural Resources, Agrarian School, Polytechnic University of Viseu, Av. Cor. José Maria Vale de Andrade, 3504-510 Viseu, Portugal
1 shared publication - José Aranha
Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), University of Trás-os-Montes and Alto Douro (UTAD), Quinta de Prados, 5000-801 Vila Real, Portugal
1 shared publication
Latest Publications
- Open Access
- 21 November 2018
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 6, 72952-72961
Selection and Fusion of Spectral Indices to Improve Water Body Discrimination
- 21 November 2018
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 6, 72952-72961
Open AccessSelection and Fusion of Spectral Indices to Improve Water Body Discrimination
- Open Access
- 2 June 2014
- Published by Italian Society of Sivilculture and Forest Ecology (SISEF) in iForest - Biogeosciences and Forestry
- Vol. 7 (3) , 186-192
A simplified methodology for the correction of Leaf Area Index (LAI) measurements obtained by ceptometer with reference to Pinus Portuguese forests
Forest leaf area index (LAI) is an important structural parameter controlling many biological and physiological processes associated with vegetation. A wide array of methods for its estimation has been proposed, including those based on the sunfleck ceptometer, a ground-based easy-to-use device taking non-destructive LAI measures. However, use of ceptometer in pine stands leads to the underestimation of LAI due to foliage clumping of this species. Previous studies have proposed a correction of biased LAI estimates based on the multiplication by a constant factor. In this study, a new method for obtaining a correction factor is proposed by considering the bias (the difference between the ceptometer measure and the reference LAI) as a function of the stand structural variables, namely the basal area. LAI data were collected from 102 sampling plots (age range: 14-74) established in Pinus pinaster forests all across northern Portugal. Data from 82 sampling plots were used for the adjustment of the LAI ceptometer correction model, while the remaining 20 plots were used for the model validation. The observed LAI ranged from 0.34 to 6.4 as expected from the large heterogeneity of the sampled pine stands. Significant differences were detected between LAI values estimated by ceptometers and LAI reference values. Different correction methods have been compared for their accuracy in predicting LAI reference values. Based on the results of the statistical analysis carried out, the new proposed LAI correction outperformed all the other methods proposed so far. The new approach for bias reduction proposed here has the advantage of being easily applied since the basal area is almost always available from forest inventory or can be inferred from remote sensing surveys. However, the bias correction model obtained is site-specific, being dependent on stand species composition, soil fertility, site aspect, etc. and should therefore be applied only in the study area. Nonetheless, the development of a correction methodology based on an allometric approach has proved to greatly improve LAI ceptometer estimations.
- 2 June 2014
- Published by Italian Society of Sivilculture and Forest Ecology (SISEF) in iForest - Biogeosciences and Forestry
- Vol. 7 (3) , 186-192
Open AccessA simplified methodology for the correction of Leaf Area Index (LAI) measurements obtained by ceptometer with reference to Pinus Portuguese forests
Forest leaf area index (LAI) is an important structural parameter controlling many biological and physiological processes associated with vegetation. A wide array of methods for its estimation has been proposed, including those based on the sunfleck ceptometer, a ground-based easy-to-use device taking non-destructive LAI measures. However, use of ceptometer in pine stands leads to the underestimation of LAI due to foliage clumping of this species. Previous studies have proposed a correction of biased LAI estimates based on the multiplication by a constant factor. In this study, a new method for obtaining a correction factor is proposed by considering the bias (the difference between the ceptometer measure and the reference LAI) as a function of the stand structural variables, namely the basal area. LAI data were collected from 102 sampling plots (age range: 14-74) established in Pinus pinaster forests all across northern Portugal. Data from 82 sampling plots were used for the adjustment of the LAI ceptometer correction model, while the remaining 20 plots were used for the model validation. The observed LAI ranged from 0.34 to 6.4 as expected from the large heterogeneity of the sampled pine stands. Significant differences were detected between LAI values estimated by ceptometers and LAI reference values. Different correction methods have been compared for their accuracy in predicting LAI reference values. Based on the results of the statistical analysis carried out, the new proposed LAI correction outperformed all the other methods proposed so far. The new approach for bias reduction proposed here has the advantage of being easily applied since the basal area is almost always available from forest inventory or can be inferred from remote sensing surveys. However, the bias correction model obtained is site-specific, being dependent on stand species composition, soil fertility, site aspect, etc. and should therefore be applied only in the study area. Nonetheless, the development of a correction methodology based on an allometric approach has proved to greatly improve LAI ceptometer estimations.