Impacts of Topography on the Land Cover Classification in the Qilian Mountains, Northwest China

Wang, Hong; Liu, Chenli; Zang, Fei; Yang, Jianhong; Li, Na; Rong, Zhanlei

doi:10.1080/07038992.2020.1801401

Cited by 22 publications

(11 citation statements)

References 65 publications

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“…On the regional scale, Yang et al developed the Land Cover Dataset for the QLM Area from 1985 to 2019 (V2.0) based on the GEE platform using Landsat 8 data, with an OA of 92.19% [28]. Wang et al used MODIS data, based on the GEE and combined with topographic features, to conduct land cover classification research on the QLM [29]. However, the existing land cover products have inconsistencies and uncertainties in the classification results of the QLM, lacking a performance comparison of the different remote sensing classification algorithms in the QLM.…”

Section: Introductionmentioning

confidence: 99%

Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform

et al. 2021

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The Qilian Mountains (QLM) are an important ecological barrier in western China. High-precision land cover data products are the basic data for accurately detecting and evaluating the ecological service functions of the QLM. In order to study the land cover in the QLM and performance of different remote sensing classification algorithms for land cover mapping based on the Google Earth Engine (GEE) cloud platform, the higher spatial resolution remote sensing images of Sentinel-1 and Sentinel-2; digital elevation data; and three remote sensing classification algorithms, including the support vector machine (SVM), the classification regression tree (CART), and the random forest (RF) algorithms, were used to perform supervised classification of Sentinel-2 images of the QLM. Furthermore, the results obtained from the classification process were compared and analyzed by using different remote sensing classification algorithms and feature-variable combinations. The results indicated that: (1) the accuracy of the classification results acquired by using different remote sensing classification algorithms were different, and the RF had the highest classification accuracy, followed by the CART and the SVM; (2) the different feature variable combinations had different effects on the overall accuracy (OA) of the classification results and the performance of the identification and classification of the different land cover types; and (3) compared with the existing land cover products for the QLM, the land cover maps obtained in this study had a higher spatial resolution and overall accuracy.

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Section: Introductionmentioning

confidence: 99%

Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform

et al. 2021

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“…In Table 3 , we can see that variables such as elevation, MNDWI, slope, NDVI and NDBI have higher importance scores. The spatial distribution of alpine area is greatly affected by topography and altitude, so the introduction of terrain factors (i.e., elevation and slope) as an important part of characteristic parameters can effectively classify the land types in this area [ 46 , 60 ]. MNDWI can maximize the inhibition of vegetation information and highlight water bodies [ 44 ], which can distinguish part of the alpine grassland and meadow wetland.…”

Section: Discussionmentioning

confidence: 99%

“…These indices calculation formulas were summarized in Supplementary Materials Table S1 . Terrain features is an important index for land cover classification in alpine areas [ 46 ]. Slope [ 47 ], aspect [ 48 ] and elevation [ 47 ] are calculated by DEM to improve classification results.…”

Section: Methodsmentioning

confidence: 99%

Monitoring and Landscape Dynamic Analysis of Alpine Wetland Area Based on Multiple Algorithms: A Case Study of Zoige Plateau

Xue

Liu

et al. 2020

Sensors

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As an important part of the wetland ecosystem, alpine wetland is not only one of the most important ecological water conservation areas in the Qinghai–Tibet Plateau region, but is also an effective regulator of the local climate. In this study, using three machine learning algorithms to extract wetland, we employ the landscape ecological index to quantitatively analyze the evolution of landscape patterns and grey correlation to analyze the driving factors of Zoige wetland landscape pattern change from 1995 to 2020. The following results were obtained. (1) The random forest algorithm (RF) performs best when dealing with high-dimensional data, and the accuracy of the decision tree algorithm (DT) is better. The performance of the RF and DT is better than that of the support vector machine algorithm. (2) The alpine wetland in the study area was degraded from 1995 to 2015, whereas wetland area began to increase after 2015. (3) The results of landscape analysis show the decrease in wetland area from 1995 to 2005 was mainly due to the fragmentation of larger patches into many small patches and loss of the original small patches, while the 2005 to 2015 decrease was caused by the loss of many middle patches and the decrease in large patches from the edge to the middle. The 2015 to 2020 increase is due to an increase in the number of smaller patches and recovery of original wetland area. (4) The grey correlation degree further shows that precipitation and evaporation are the main factors leading to the change in the landscape pattern of Zoige alpine wetland. The results are of great significance to the long-term monitoring of the Zoige wetland ecosystem.

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“…It is therefore safe to assume that most of the conflict events targeting the unknown LULC class are happening in mountainous areas, which are either bare rock (at the highest altitudes) or other vegetation. Nonetheless, using other vegetation indices, such as those that have been tested in semi-arid environments [71], or including topographic variables [72] in the classification process can further improve the accuracy of the LULC data.…”

Section: Limitations and Future Researchmentioning

confidence: 99%

On the Geopolitics of Fire, Conflict and Land in the Kurdistan Region of Iraq

Eklund

Abdi

Shahpurwala³

et al. 2021

Remote Sensing

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There is limited understanding of the geopolitics of fire, conflict, and land, for example, how conflict and fire are related and how conflict impacts the biophysical environment. Since 2014, the natural environment in the Kurdistan Region of Iraq has been negatively affected by recurrent conflict that coincided with a sharp increase in the number of reported fires. Against this background, this study explores the spatiotemporal aspects of conflict, fire, and land use and land cover in this region. We combine several satellite-derived products, including land use and land cover, active fire, and precipitation. We apply a partial correlation analysis to understand the relationship between fire, conflict, climate, and land use and land cover. Conflict events and fires have increased since 2014 and have followed a similar temporal pattern, and we show that certain conflicts were particular to certain land use and land cover contexts. For example, the conflict involving the Islamic State was concentrated in southern areas with bare soil/sparse vegetation, and the conflict involving Turkey largely took place in northern mountainous areas characterized by natural vegetation and rugged topography. This dichotomy indicates divergent effects of conflict on the land system. A surprising finding was that fire hotspots had a low positive correlation with the amplitude of distance to conflict while accounting for other variables such as land cover and climate. The high statistical significance of this relationship indicates nonlinearity and implies that a larger range of distances to conflict creates more space for the fires to spread in the surrounding landscape. At the same time, fire hotspots had a weaker but negative correlation to distance from conflict events, which is somewhat expected as areas farther away from conflict locations have lower exposure risk to fires. We discuss the implications of these findings within the geopolitical context of the region and acknowledge the limitations of the study. We conclude with a summary of the main findings and recommendations for future research.

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Impacts of Topography on the Land Cover Classification in the Qilian Mountains, Northwest China

Cited by 22 publications

References 65 publications

Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform

Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform

Monitoring and Landscape Dynamic Analysis of Alpine Wetland Area Based on Multiple Algorithms: A Case Study of Zoige Plateau

On the Geopolitics of Fire, Conflict and Land in the Kurdistan Region of Iraq

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