Forest biomass estimation through NDVI composites. The role of remotely sensed data to assess Spanish forests as carbon sinks

González-Alonso, F.; Miguel, S. Merino de; Roldán-Zamarrón, A.; García-Gigorro, S.; Cuevas, J. M.

doi:10.1080/01431160600830748

Cited by 55 publications

(29 citation statements)

References 6 publications

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“…As expected, vegetation indexes, such as NDVI, and other indicators of the radiationland cover interaction, such as the Absorbed Shortwave Solar Radiation, also emerged as valuable predictors of biomass due to their potential relationship with biomass. The relationship between remote sensed NDVI and biomass content, which has been the matter of discussion as strongly dependent on the scale of analyses and characteristics of the imagery, has nonetheless been regarded in the literature as one of the most widely used predictors of biomass content (Foody et al 2003, González-Alonso et al 2006. Interestingly, apart from these variables in the pure spectral domain, up to five variables of the spectral-spatial domain (i.e., texture variables Entropy 3×3, Correlation 3×3, Dissimilarity 5×5, Mean 7×7, Stand.…”

Section: Discussionmentioning

confidence: 99%

Estimating biomass of mixed and uneven-aged forests using spectral data and a hybrid model combining regression trees and linear models

López-Serrano

López-Sánchez

Varela

et al. 2016

iForest

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The Sierra Madre Occidental mountain range (Durango, Mexico) is of great ecological interest because of the high degree of environmental heterogeneity in the area. The objective of the present study was to estimate the biomass of mixed and uneven-aged forests in the Sierra Madre Occidental by using Landsat-5 TM spectral data and forest inventory data. We used the ATCOR3 ® atmospheric and topographic correction module to convert remotely sensed imagery digital signals to surface reflectance values. The usual approach of modeling stand variables by using multiple linear regression was compared with a hybrid model developed in two steps: in the first step a regression tree was used to obtain an initial classification of homogeneous biomass groups, and multiple linear regression models were then fitted to each node of the pruned regression tree. Cross-validation of the hybrid model explained 72.96% of the observed stand biomass variation, with a reduction in the RMSE of 25.47% with respect to the estimates yielded by the linear model fitted to the complete database. The most important variables for the binary classification process in the regression tree were the albedo, the corrected readings of the short-wave infrared band of the satellite (2.08-2.35 µm) and the topographic moisture index. We used the model output to construct a map for estimating biomass in the study area, which yielded values of between 51 and 235 Mg ha -1 . The use of regression trees in combination with stepwise regression of corrected satellite imagery proved a reliable method for estimating forest biomass.

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Section: Discussionmentioning

confidence: 99%

Estimating biomass of mixed and uneven-aged forests using spectral data and a hybrid model combining regression trees and linear models

López-Serrano

López-Sánchez

Varela

et al. 2016

iForest

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“…The NDVI represents a dimensionless, radiometric measure shown to be correlated with the relative condition and amount of green vegetation [23,24] by means of the differential response of incident visible red (absorbed by leaf chlorophyll) and near-infrared (reflected by spongy mesophyll and green leaf biomass) reflectance properties of the vegetation canopy. Prior studies have shown time-series NDVI observations are linked to phenological signals [25][26][27] and biophysical vegetation characteristics over different land cover types (e.g., leaf area index and biomass) [28][29][30][31][32]. Positive correlations between NDVI and precipitation [33][34][35][36] have indicated that increasing available moisture for vegetation also increases the NDVI over many different cover types, including grasslands, shrubs, and crops [25,37].…”

Section: Modis Annual Peak Ndvimentioning

confidence: 99%

Mapping Irrigated Lands at 250-m Scale by Merging MODIS Data and National Agricultural Statistics

2010

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Accurate geospatial information on the extent of irrigated land improves our understanding of agricultural water use, local land surface processes, conservation or depletion of water resources, and components of the hydrologic budget. We have developed a method in a geospatial modeling framework that assimilates irrigation statistics with remotely sensed parameters describing vegetation growth conditions in areas with agricultural land cover to spatially identify irrigated lands at 250-m cell size across the conterminous United States for 2002. The geospatial model result, known as the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset (MIrAD-US), identified irrigated lands with reasonable accuracy in California and semiarid Great Plains states with overall accuracies of 92% and 75% and kappa statistics of 0.75 and 0.51, respectively. A quantitative accuracy assessment of MIrAD-US for the eastern region has not yet been conducted, and qualitative assessment shows that model improvements are needed for the humid eastern regions where the distinction in annual peak NDVI between irrigated and non-irrigated crops is minimal and county sizes are relatively small. This modeling approach enables consistent mapping of irrigated lands based upon USDA irrigation statistics and should lead to better understanding of spatial trends in irrigated lands across the conterminous United States. An improved version of the model with revised datasets is planned and will employ 2007 USDA irrigation statistics. OPEN ACCESSRemote Sens. 2010, 2 2389

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“…Biomass and Leaf Area Index (LAI) are probably the physical parameters which have been tested more in a large number of different environments: forests [e.g. González-Alonso et al, 2006;Tan et al, 2007;Madugundu et al, 2008], rice fields [e.g. Casanova et al, 1998;Wang et al, 2007;Gnyp et al, 2014], dry crops [e.g.…”

Section: Introductionmentioning

confidence: 99%

MODIS time series contribution for the estimation of nutritional properties of alpine grassland

Ranghetti

Bassano

Bogliani

et al. 2016

European Journal of Remote Sensing

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Despite the Normalised Difference Vegetation Index (NDVI) has been used to make predictions on forage quality, its relationship with bromatological field data has not been widely tested. This relationship was investigated in alpine grasslands of the Gran Paradiso National Park (Italian Alps). Predictive models were built using remotely sensed derived variables (NDVI and phenological information computed from MODIS) in combination with geo-morphometric data as predictors of measured biomass, crude protein, fibre and fibre digestibility, obtained from 142 grass samples collected within 19 experimental plots every two weeks during the whole 2012 growing season. The models were both crossvalidated and validated on an independent dataset (112 samples collected during 2013). A good predictability ability was found for the estimation of most of the bromatological measures, with a considerable relative importance of remotely sensed derived predictors; instead, a direct use of NDVI values as a proxy of bromatological variables appeared not to be supported.

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Forest biomass estimation through NDVI composites. The role of remotely sensed data to assess Spanish forests as carbon sinks

Cited by 55 publications

References 6 publications

Estimating biomass of mixed and uneven-aged forests using spectral data and a hybrid model combining regression trees and linear models

Estimating biomass of mixed and uneven-aged forests using spectral data and a hybrid model combining regression trees and linear models

Mapping Irrigated Lands at 250-m Scale by Merging MODIS Data and National Agricultural Statistics

MODIS time series contribution for the estimation of nutritional properties of alpine grassland

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