A global classification of vegetation based on <scp>NDVI</scp>, rainfall and temperature

Zhang, Xianliang; Wu, Shuang; Yan, Xiaodong; Chen, Zhenju

doi:10.1002/joc.4847

Cited by 48 publications

(37 citation statements)

References 45 publications

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“…In this situation, the distribution of vegetation may not be reflected by our climate zones. Furthermore, the distribution of climate zones also influences vegetation and soil types (Mather and Yoshioka, ; White et al ., ; Azzali and Menenti, ; Box and Fujiwara, ; Zhang et al ., ). As a result, research in fields such as agriculture or forestry is closely connected to the distribution of climate zones.…”

Section: Discussionmentioning

confidence: 99%

Geographic distribution of global climate zones under future scenarios

Zhang

Yan

Chen

2017

Intl Journal of Climatology

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Possible future climate change can be projected by general circulation models (GCMs), and the future geographic distribution of global climate zones can be projected based on these GCM simulations. In this study, the performance of 33 GCMs in simulating the distribution of global climate zones was evaluated using Kappa values. The results show that certain models are more skillful than other models in simulating the present distribution of global climate zones. The ensemble mean of all the GCMs showed worse performance than some individual models in simulating the present distribution of climate zones. The ensemble mean based on the three GCMs that showed superior performance achieved the optimum results in simulating the future distribution of climate zones. Based on the simulations of the ensemble mean of these three GCMs, the distributions of global climate zones in the future were projected for three scenarios (Representative Concentration Pathways, RCP) (RCP2.6, RCP4.5, and RCP8.5). The climate types in the temperate zone were susceptible to climate changes under the three scenarios. The shift in the distribution of climate zones was mainly caused by the joint variations of temperature and precipitation among different climate zones.

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Section: Discussionmentioning

confidence: 99%

Geographic distribution of global climate zones under future scenarios

Zhang

Yan

Chen

2017

Intl Journal of Climatology

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“…For VIT, the high degree of overestimation could probably be due to the high spectral similarity in NDVI between rice and other classes [25]. As the classes were defined on the basis of planting habits and known land use conditions, there are inevitably a few unusual land cover types that are lacking in definition for MCSVC and DTC.…”

Section: Advantages Of Ocsvcmentioning

confidence: 99%

“…These methods represent a quick way to extract the spatial distribution of paddy rice on satellite images, but the extracted planting area may not be accurate [25]. In addition, the thresholds are often determined in a subjective way and could hardly be applicable to other sites or other periods of the growing season [26].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of One-Class Support Vector Classification for Mapping the Paddy Rice Planting Area in Jiangsu Province of China from Landsat 8 OLI Imagery

Jiang

et al. 2018

Remote Sensing

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Identification of paddy fields is essential for monitoring the rice cultivated area and predicting rice productivity. Timely and accurate extraction of rice distribution can bring vital information for national food security, agricultural policy formulation, and regional environmental sustainability. Conventional classification methods usually suffered from low accuracy, multi-class training samples, or demanding imagery requirements. This paper proposes to use one-class support vector classification (OCSVC) to extract rice cultivated area with Landsat Optical Land Imager (OLI) imagery. Instead of sampling and training all land cover types as performed by multi-class classification methods, OCSVC only used the training samples of target class (rice) for rice mapping. The performance of OCSVC was evaluated in terms of the classification accuracy of rice mapping and rice acreage estimation based on high-resolution imagery, field survey data and rice acreage data from government reports for Jiangsu Province, China. At the county-level, OCSVC was also compared with the commonly used multi-class support vector classification (MCSVC), decision tree classification (DTC), and vegetation index-based thresholding (VIT). Our results demonstrated that OCSVC produced a comparable overall accuracy to DTC and outperformed MCSVC and VIT. The computational efficiency of OCSVC increased approximately ten times as compared to MCSVC. The OCSVC produced the best correlation between its classified area and reported area among the four classification methods evaluated. When applied to the provincial level, the classification overall accuracy for OCSVC was 88.54%. The detected rice planting area for Jiangsu Province was 22,602 km 2 , which was consistent with the statistics from the National Bureau of Statistics (22,948 km 2 ). This OCSVC-based mapping strategy provides a practical and efficient way to detect the rice planting extent with Landsat imagery at a large scale.

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“…The normalized difference vegetation index (NDVI) emphasizes variations of band density for environmental analysis with conclusions based on the vegetal cover dynamics 12 . NDVI has been used to classify the distribution 13 and to study the variability of vegetation biophysical parameters such as phytomass production 14 , leaf area index 15 , land use and occupation 16 , vegetation fragmentation 17 and estimating agricultural productivity 18 .…”

Section: Introductionmentioning

confidence: 99%

Geotechnology in the analysis of forest fragments in northern Mato Grosso, Brazil

Barbosa

Spletozer

Roque

et al. 2018

Sci Rep

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Pasture implantation fragments and reduces the Amazonian forest area. The objective was to quantify landscape changes in 1985, 2000 and 2015 in northern Mato Grosso, Brazil. The study was carried out in three scenes obtained by the LANDSAT satellite of a microbasin (2742.33 ha) in the municipality of Alta Floresta. Forest, water bodies, pasture and exposed soil were the thematic classes determined to e mapping the land use evolution. The edge, density and shape indexes of the fragments were measured. Normalized vegetation difference (NDVI) values were high in 1985. Land use and occupation over 15 years (1985–2000) reduced forest cover by 69.8%, but it increased by 1.7% over the next 15 years (2000–2015). The number of exposed soil patches increased between the periods, but the total area and number of the patches of the forest fragments decreased. The high values of NDVI in 1985 showed vegetated areas with high density. Reducing forest cover decreases the size of the fragments, increases the isolation and the number of soil patches exposed. The mapping of land use showed a reduction of the Amazon forest in the microbasin in the north of Mato Grosso, in the years 2000 and 2015 compared to 1985.

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A global classification of vegetation based on NDVI, rainfall and temperature

Cited by 48 publications

References 45 publications

Geographic distribution of global climate zones under future scenarios

Geographic distribution of global climate zones under future scenarios

Evaluation of One-Class Support Vector Classification for Mapping the Paddy Rice Planting Area in Jiangsu Province of China from Landsat 8 OLI Imagery

Geotechnology in the analysis of forest fragments in northern Mato Grosso, Brazil

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