Improved Forest Cover Classification in an Industrialized Mountain Area in Japan

Kachmar, Mark; Sánchez‐Azofeifa, G. Arturo; Rivard, Benoît; Kakubari, Yoshitaka

doi:10.1659/0276-4741(2005)025[0349:ifccia]2.0.co;2

Cited by 7 publications

(7 citation statements)

References 14 publications

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“…For Landsat TM-5 and OLI-8 data, the increases in overall accuracy varied between 3% and 3.97%, and 0.44% and 1.34%, with increases in Kappa coefficient of 2.4% -4.9% and 1.6% -2.9%, respectively [32]. Further, in addition to the topographic correction methods, the performance of forest classification varies between forest cover types [69]. The classification of evergreen forests, bamboo, and non-forest areas was completed with the highest accuracy, but the greatest variation was found in the deciduous forest class.…”

Section: Forest Classification Results and Accuraciesmentioning

confidence: 99%

Topographic Correction of Landsat TM-5 and Landsat OLI-8 Imagery to Improve the Performance of Forest Classification in the Mountainous Terrain of Northeast Thailand

et al. 2017

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Abstract:The accurate mapping and monitoring of forests is essential for the sustainable management of forest ecosystems. Advancements in the Landsat satellite series have been very useful for various forest mapping applications. However, the topographic shadows of irregular mountains are major obstacles to accurate forest classification. In this paper, we test five topographic correction methods: improved cosine correction, Minnaert, C-correction, Statistical Empirical Correction (SEC) and Variable Empirical Coefficient Algorithm (VECA), with multisource digital elevation models (DEM) to reduce the topographic relief effect in mountainous terrain produced by the Landsat Thematic Mapper (TM)-5 and Operational Land Imager (OLI)-8 sensors. The effectiveness of the topographic correction methods are assessed by visual interpretation and the reduction in standard deviation (SD), by means of the coefficient of variation (CV). Results show that the SEC performs best with the Shuttle Radar Topographic Mission (SRTM) 30 m × 30 m DEM. The random forest (RF) classifier is used for forest classification, and the overall accuracy of forest classification is evaluated to compare the performances of the topographic corrections. Our results show that the C-correction, SEC and VECA corrected imagery were able to improve the forest classification accuracy of Landsat TM-5 from 78.41% to 81.50%, 82.38%, and 81.50%, respectively, and OLI-8 from 81.06% to 81.50%, 82.38%, and 81.94%, respectively. The highest accuracy of forest type classification is obtained with the newly available high-resolution SRTM DEM and SEC method.

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Section: Forest Classification Results and Accuraciesmentioning

confidence: 99%

Topographic Correction of Landsat TM-5 and Landsat OLI-8 Imagery to Improve the Performance of Forest Classification in the Mountainous Terrain of Northeast Thailand

et al. 2017

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“…Old birch stands occur as high as 1650 m. Mixed-deciduous forest stands are typically composed of beech, oak (Quercus mongolica), and two bamboo species (Sasa kurilensis and Sasa paniculata). A recent land-cover classification by Kachmar et al (2005) reported that beech forest dominates, covering 41% of the region, followed by mixed-deciduous forest, birch forests, anthropogenic/urban areas, and cedar forests, which cover 30, 19.5, 4.5 and 5% of the region, respectively.…”

Section: Study Plotsmentioning

confidence: 99%

Soil organic carbon stock is closely related to aboveground vegetation properties in cold-temperate mountainous forests

Wang

Endō

et al. 2010

Geoderma

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“…The mapping performance of eight conventional classification methods were tested-including SAM, which achieved the best result. Additionally, Kachmar et al [27] used Landsat 5 TM data, in order to classify dominant forest cover types in the Naeba Mountains of Japan considering the SAM classifier. Silva [28] used a dataset composed by a several Landsat-5 TM and Rapid-Eye images from the Paraíba Valley (Brazil).…”

Section: Introductionmentioning

confidence: 99%

“…SAM treats the spectra as vectors in a space with dimensionality equal to the number of bands used. Using the SAM classifier and spectrally-suitable forest training areas, forest cover types are classified and their accuracies are related to topographic correction methods, applied and localized land use, and land cover change occurring in the study area [27]. Moreover, SAM's advantage over more 'traditional' classifiers is its relative insensitivity to illumination and albedo effects inherent with remotely-sensed imagery [30].…”

Section: Introductionmentioning

confidence: 99%

Mapping Forest Species in the Central Middle Atlas of Morocco (Azrou Forest) through Remote Sensing Techniques

Mohajane

Essahlaoui

Oudija

et al. 2017

IJGI

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Abstract:The studies of forest ecosystems from remotely-sensed data are of great interest to researchers because of ecosystem services provided by this ecosystem, including protection of soils and vegetation, climate stabilization, and regulation of the hydrological cycle. In this context, our study focused on the use of a spectral angle mapper (SAM) classification method for mapping species in the Azrou Forest, Central Middle Atlas, Morocco. A Sentinel-2A image combined with ground reference data were used in this research. Four classes (holm oak, cedar forest, bare soil, and others-unclassified) were selected; they represent, respectively, 27, 11, 24, and 38% of the study area. The overall accuracy of classification was estimated to be around 99.72%. This work explored the potential of the SAM classification combined with Sentinel-2A data for mapping land cover in the Azrou Forest ecosystem.

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Improved Forest Cover Classification in an Industrialized Mountain Area in Japan

Abstract: BioOne Complete (complete.BioOne.org) is a full-text database of 200 subscribed and open-access titles in the biological, ecological, and environmental sciences published by nonprofit societies, associations, museums, institutions, and presses.

Cited by 7 publications

References 14 publications

Topographic Correction of Landsat TM-5 and Landsat OLI-8 Imagery to Improve the Performance of Forest Classification in the Mountainous Terrain of Northeast Thailand

Topographic Correction of Landsat TM-5 and Landsat OLI-8 Imagery to Improve the Performance of Forest Classification in the Mountainous Terrain of Northeast Thailand

Soil organic carbon stock is closely related to aboveground vegetation properties in cold-temperate mountainous forests

Mapping Forest Species in the Central Middle Atlas of Morocco (Azrou Forest) through Remote Sensing Techniques

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