Simultaneous use of Landsat-TM and IRS-1C WiFS data in estimating large area tree stem volume and aboveground biomass

Tomppo, Erkki; Nilsson, Mats; Rosengren, M.; Aalto, Paula; Kennedy, Pamela J.

doi:10.1016/s0034-4257(02)00031-7

Cited by 121 publications

(79 citation statements)

References 11 publications

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“…Although the RMSEs of our results at the pixel level were larger than those reported by some previous studies, they were moderate compared with those reported by previous studies, e.g., 44.2% (China [7]), 47.6% (Finland [50]), 58%-80% (Sweden [42]), 59.0% (Sweden [52]), 66.2% (Japan [25]), 66.6% (Sweden [40]), 79.3% (Finland [51]) and 91% (Norway [10]). Accordingly, the stand volume in Japan could be estimated by the k-NN technique with accuracies similar to earlier studies in terms of the rRMSE.…”

Section: Discussioncontrasting

confidence: 85%

“…In addition, although fixed values of t = 0, 1 and 2 are usually used in the k-NN technique, the optimum value of t increases as k increases [49]. However, given its interpretive advantages [21] and the ease of comparing its results with those of previous studies [7,10,25,40,42,[50][51][52], we used a simple k-NN technique with a fixed value of t = 2 in order to investigate the effectiveness of additional feature variables in this study. First, we created prediction models without FTYPE and then created prediction models using FTYPE as a dummy variable.…”

Section: Stand Volume Estimation and Accuracy Assessmentmentioning

confidence: 99%

“…To facilitate a comparison with previous studies (e.g., [7,10,25,40,42,[50][51][52]), we also used relative RMSE (rRMSE, %). The RMSE and rRMSE were calculated using a leave-one-out cross-validation procedure with the following equations:…”

Section: Stand Volume Estimation and Accuracy Assessmentmentioning

confidence: 99%

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Stand Volume Estimation Using the k-NN Technique Combined with Forest Inventory Data, Satellite Image Data and Additional Feature Variables

et al. 2014

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Abstract:The main objective of this study was to evaluate the effectiveness of adding feature variables, such as forest type information and topographic-and climatic-environmental factors to satellite image data, on the accuracy of stand volume estimates made with the k-nearest neighbor (k-NN) technique in southwestern Japan. Data from the Forest Resources Monitoring Survey-a national plot sampling survey in Japan-was used as in situ data in this study. The estimates obtained from three Landsat Enhanced Thematic Mapper Plus (ETM+) datasets acquired in different seasons with various combinations of additional feature variables were compared. The results showed that although the addition of environmental factors to satellite image data did not always help improve estimation OPEN ACCESSRemote Sens. 2015, 7 379 accuracy, the use of summer rainfall (SRF) data had a consistent positive effect on accuracy improvement. Therefore, SRF may be a useful feature variable to consider in stand volume estimation in this study area. Moreover, the use of forest type information is very effective at reducing k-NN estimation errors when using an optimum combination of satellite image data and environmental factors. All of the results indicated that the k-NN technique combined with appropriate feature variables is applicable to nationwide stand volume estimation in Japan.

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Section: Discussioncontrasting

confidence: 85%

Section: Stand Volume Estimation and Accuracy Assessmentmentioning

confidence: 99%

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Stand Volume Estimation Using the k-NN Technique Combined with Forest Inventory Data, Satellite Image Data and Additional Feature Variables

et al. 2014

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“…Nonparametric K-NN Land cover updatealgorithms have been used to produce estimates of various forest parameters over large areas in Sweden, using optical satellite data (Reese et al 2002, Tomppo et al 2002. The K-NN classifier does not require the fitting of a model, it is a memorybased method (Fukunaga 1990).…”

Section: K Nearest Neighbours Classifiermentioning

confidence: 99%

Land cover update by supervised classification of segmented ASTER images

Marçal

Borges

Gomes

et al. 2005

International Journal of Remote Sensing

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The revision of the 1995 land cover dataset for the Vale do Sousa region, in the northwest of Portugal, was carried out by supervised classification of a multispectral image from the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) sensor. The nine reflective bands of ASTER were used, covering the spectral range from 0.52-2.43 mm. The image was initially ortho-rectified and segmented into 51 186 objects, with an average object size of 135 pixels (about 3 ha). A total of 582 of these objects were identified for training nine land cover classes. The image was classified using an algorithm based on a fuzzy classifier, Support Vector Machines (SVM), K Nearest Neighbours (K-NN) and a Logistic Discrimination (LD) classifier. The results from the classification were evaluated using a set of 277 validation sites, independently gathered. The overall accuracy was 44.6% for the fuzzy classifier, 70.5% for the SVM, 60.9% for the K-NN and 72.2% for the LD classifier. The difficulty in discriminating between some of the forest land cover classes was examined by separability analysis and unsupervised classification with hierarchical clustering. The forest classes were found to overlap in the multi-spectral space defined by the nine ASTER bands used.

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“…It has been suggested that coarse (MODIS or MISR) to medium-scale (Landsat) datasets be used to extrapolate fine-scale analyses over larger areas in order to marry the precision associated with fine-scale analysis with the scope of coarser datasets, (Hansen, et al, 2008;Frolking, et al, 2009). Applying finer-scale analyses to broader extents has shown some success in leveraging coarser-scale analyses to pinpoint areas for finescale study (Hansen, et al, 2008;Asner G. , 2009;Tomppo, Nilsson, Rosengren, Aalto, & Kennedy, 2002). This research could similarly stand to benefit from an extrapolation technique that could increase the temporal and spatial extent to which these analyses could be applied.…”

Section: Supplemental Research Suggestionsmentioning

confidence: 99%

Examining Forest Structure in the Mozambican Dry Deciduous Lowland Forest Utilizing In-Situ and Remotely Sensed Measurements and Observations

Heil¹

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Land use change in the form of deforestation and degradation is contributing to the release of greenhouse gases on the order of 1.2 ±0.7 Gt CO 2 e per year. Emissions of this magnitude suggest that the conversion and disturbance of forests play a significant role in forcing global climate change. Although there is growing recognition of this contribution among scientific and political communities, there is an expressed need for more efficient and accurate methods for assessing the role of forests in shifting climate patterns. Recent efforts to measure and monitor forests on large scales have used remotely sensed data from airborne and spaceborne sensors. This study leverages high spatial resolution remotely sensed data with in-situ field measurements to measure forest structure in the Dry Deciduous Lowland Forests ofMozambique. The approach to analysis was motivated by the importance of three-dimensional forest structure in driving forest dynamics. A novel tree crown analysis methodology was employed in order to calculate select structural properties of forests from remotely sensed data.

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Simultaneous use of Landsat-TM and IRS-1C WiFS data in estimating large area tree stem volume and aboveground biomass

Cited by 121 publications

References 11 publications

Stand Volume Estimation Using the k-NN Technique Combined with Forest Inventory Data, Satellite Image Data and Additional Feature Variables

Stand Volume Estimation Using the k-NN Technique Combined with Forest Inventory Data, Satellite Image Data and Additional Feature Variables

Land cover update by supervised classification of segmented ASTER images

Examining Forest Structure in the Mozambican Dry Deciduous Lowland Forest Utilizing In-Situ and Remotely Sensed Measurements and Observations

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