Estimation of tree canopy cover in evergreen oak woodlands using remote sensing

Carreiras, João M. B.; Pereira, José M. C.; Pereira, J. S.

doi:10.1016/j.foreco.2005.10.056

Cited by 186 publications

(102 citation statements)

References 19 publications

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“…In contrast, plants with high vegetative vigor absorb more EMR in B4 band (Jensen, 2009), which reveals the high photosynthetic activity, since leaf pigments absorb a greater amount of the radiation incident on the leaves, which is essential for their growth. Carreiras et al (2006) found that the increase in canopy coverage, considering the same background class, led to an increase in reflectance in B5 band and a decrease in B4 band, as observed in Landsat data. This is why Jensen (2009) states that, as the canopy develops, pixel reflectance tends to suffer less interference from the soil line.…”

Section: Resultssupporting

confidence: 64%

Spectral characterization of forest plantations with Landsat 8/OLI images for forest planning and management

Alba

Mello

Marchesan

et al. 2017

Pesq. agropec. bras.

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-The objective of this work was to evaluate the use of Landsat 8/OLI images to differentiate the age and estimate the total volume of Pinus elliottii, in order to determine the applicability of these data in the planning and management of forest activity. Fifty-three sampling units were installed, and dendrometric variables of 9-and-10-year-old P. elliottii commercial stands were measured. The digital numbers of the image were converted into surface reflectance and, subsequently, vegetation indices were determined. Red and nearinfrared reflectance values were used to differentiate the ages of the stands. Regression analysis of the spectral variables was used to estimate the total volume. Increase in age caused an addition in reflectance in the near-infrared band and a decrease in the red band. The general equation for estimating the total volume for P. elliottii had an R² adj of 0.67 with a S yx of 31.46 m³ ha -1. Therefore, the spectral data with medium spatial resolution from the Landsat 8/OLI satellite can be used to distinguish the growth stages of the stands and can, thus, be used in the planning and proper management of forest activity on a spatial and temporal scale.Index terms: Pinus elliottii, remote sensing, vegetation indices, volume modeling. Caracterização espectral de plantios florestais com imagens Landsat 8/OLI para planejamento e manejo florestalResumo -O objetivo deste trabalho foi avaliar o uso de imagens Landsat 8/OLI na diferenciação da idade e na estimativa do volume total de Pinus elliottii, para determinar a aplicabilidade desses dados no planejamento e no manejo da atividade florestal. Foram instaladas 53 unidades amostrais e mensuradas as variáveis dendrométricas de povoamentos comerciais de P. elliottii aos 9 e 10 anos. Os números digitais da imagem foram convertidos à reflectância de superfície e, posteriormente, foram elaborados índices de vegetação. A diferenciação da idade dos povoamentos baseou-se nos valores de reflectância da banda do vermelho e do infravermelho próximo. A análise de regressão das variáveis espectrais foi utilizada na estimativa do volume total. O aumento da idade propiciou um acréscimo da reflectância na banda do infravermelho próximo e uma redução na banda do vermelho. A equação geral de estimativa do volume total para P. elliottii apresentou R² aj de 0,67 com S yx de 31,46 m³ ha -1. Dessa forma, os dados espectrais de média resolução espacial provenientes do satélite Landsat 8/OLI podem ser utilizados na distinção das fases de crescimento dos povoamentos sendo, portanto, passíveis de utilização no planejamento e no manejo adequado da atividade florestal em escala espacial e temporal.Termos para indexação: Pinus elliottii, sensoriamento remoto, índices de vegetação, modelagem do volume.

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Section: Resultssupporting

confidence: 64%

Spectral characterization of forest plantations with Landsat 8/OLI images for forest planning and management

Alba

Mello

Marchesan

et al. 2017

Pesq. agropec. bras.

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“…Similar conclusions were reported by Purevdorj et al [57] relative to the better performance of SAVI to estimate the vegetation cover for low crop densities. Differently, in a study with savannah-type open woodlands dominated by evergreen oak species (montados or dehesa), the NDVI performed slightly better than SAVI for the estimation of tree canopy cover [58] but the study areas were characterized by an extremely variable understory including bare soil, dry grass and a few evergreen shrubs that impacted the VIs values as discussed by the authors.…”

Section: Estimation Of the Fraction Of Ground Cover From Vegetation Imentioning

confidence: 83%

Estimation of Actual Crop Coefficients Using Remotely Sensed Vegetation Indices and Soil Water Balance Modelled Data

et al. 2015

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Abstract:A new procedure is proposed for estimating actual basal crop coefficients from vegetation indices (Kcb VI) considering a density coefficient (Kd) and a crop coefficient for bare soil. Kd is computed using the fraction of ground cover by vegetation (fc VI), which is also estimated from vegetation indices derived from remote sensing. A combined approach for estimating actual crop coefficients from vegetation indices (Kc VI) is also proposed by integrating the Kcb VI with the soil evaporation coefficient (Ke) derived from the soil water balance model SIMDualKc. Results for maize, barley and an olive orchard have shown that the approaches for estimating both fc VI and Kcb VI compared well with results obtained using the SIMDualKc model after calibration with ground observation data. For the crops studied, the correlation coefficients relative to comparing the actual Kcb VI and Kc VI with actual Kcb and Kc obtained with SIMDualKc were larger than 0.73 and 0.71, respectively. The corresponding regression coefficients were close to 1.0. The methodology herein presented and discussed allowed for obtaining information for the whole crop season, including periods when vegetation cover is incomplete, as the initial and development stages. Results show that the proposed methods are adequate for supporting irrigation management. OPEN ACCESSRemote Sens. 2015, 7 2374

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“…To highlight spectral difference among stands of different age rubber trees, e.g., mature, middle and young, as well as that among rubber trees and deciduous forest, shrubs and bare soil etc., we employed KTT on the six reflective TM bands to produce soil brightness, vegetation greenness, and soil/vegetation wetness components. The vegetation greenness component is well correlated with tree canopy cover, leaf area index and live biomass above ground [37]; therefore it was expected to be able to capture difference among stands of rubber trees of diverse ages due to differences in canopy densities. The soil brightness component expresses differences in soil properties, such as particle size and organic matter content; and the soil/vegetation wetness component is sensitive to soil and plant moisture [37].…”

Section: Development Of Input Metricsmentioning

confidence: 99%

“…The vegetation greenness component is well correlated with tree canopy cover, leaf area index and live biomass above ground [37]; therefore it was expected to be able to capture difference among stands of rubber trees of diverse ages due to differences in canopy densities. The soil brightness component expresses differences in soil properties, such as particle size and organic matter content; and the soil/vegetation wetness component is sensitive to soil and plant moisture [37]. These two components together were expected to capture difference between young rubber trees (dominated by bare soil or shrubs) and pure bare soil fallow fields.…”

Section: Development Of Input Metricsmentioning

confidence: 99%

Rubber Tree Distribution Mapping in Northeast Thailand

Li¹,

Fox²

2011

IJG

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In many parts of mainland Southeast Asia rubber plantations are expanding rapidly in areas where the crop was not historically found. Monitoring and mapping the distribution of rubber trees in the region is necessary for developing a better understanding of the consequences of land-cover and land-use change on carbon and water cycles. In this study, we conducted rubber tree growth mapping in Northeast Thailand using Landsat 5 TM data. A Mahalanobis typicality method was used to identify different age rubber trees. Landsat 5 TM 30 m non-thermal reflective bands, NDVI and tasseled cap transformation components were selected as the model input metrics. The validation was carried out using provincial level agricultural statistical data on the rubber tree growth area. At regional (Northeast Thailand) and provincial scales, the estimates of mature and middle-age rubber stands produced from 30 m Landsat 5 TM data compared well (high statistical significance) with the provincial rubber tree growth statistical data.

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Estimation of tree canopy cover in evergreen oak woodlands using remote sensing

Cited by 186 publications

References 19 publications

Spectral characterization of forest plantations with Landsat 8/OLI images for forest planning and management

Spectral characterization of forest plantations with Landsat 8/OLI images for forest planning and management

Estimation of Actual Crop Coefficients Using Remotely Sensed Vegetation Indices and Soil Water Balance Modelled Data

Rubber Tree Distribution Mapping in Northeast Thailand

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