Mapping the vegetation distribution of the permafrost zone on the Qinghai-Tibet Plateau

Wang, Zhiwei; Wang, Qian; Zhao, Lin; Wu, Xiaodong; Yue, Guozhen; Zou, Defu; Zhang, Nan; Liu, Guangyue; Pang, Qiangqiang; Han, Fang; Wu, Tonghua; Shi, Jianzong; Jiao, Keqin; Zhao, Youpeng; Zhang, Lele

doi:10.1007/s11629-015-3485-y

Cited by 105 publications

(74 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, the permafrost area of this map and the permafrost map over the QTP derived from the 1:4,000,000 map of snow, ice, and frozen ground in China (hereafter referred to as the "QTP88_map") are overestimated [36,60,66]. The QTP06_map (the frozen soil map of QTP based on MAGT) derived from 1:4,000,000 Map of the Glaciers, Frozen Ground and Deserts in China also was used benchmark (Wang, Rinke, et al, 2016), which permafrost area was 111.8 × 10 4 km 2 .…”

Section: Discussionmentioning

confidence: 94%

“…The vegetation type of Northwest Plateau is the alpine, grassland and non-vegetation; the center of the QTP is meadow steppe and meadow with a high vegetation cover in this region. Generally, changes in vegetation can cause changes in ground surface conditions, such as evapotranspiration, albedo, and infiltration rate of precipitation, which can indirectly influence the thermal dynamics of permafrost [36], however, NDVI (Normalized Differential Vegetation Index) as the best indicator of vegetation growth state and vegetation coverage is widely used in vegetation remote sensing, it shows NDVI can be used as one of the parameters reflecting the frozen ground. In addition, the basic permafrost types of the QTP are low-latitude and high-altitude plateau permafrost, and the permafrost in the Qilian Mountains, Himalaya Range, Kunlun Mountains, Hengduan Mountains, belongs to a middle-low latitude and high altitude alpine permafrost, which have obvious zonality, especially vertical zonality (e.g., the lower bound of permafrost lowered with the latitude increasing and the thickness of frozen soil increases with the altitude increasing).…”

Section: Study Areamentioning

confidence: 99%

See 1 more Smart Citation

Permafrost Presence/Absence Mapping of the Qinghai-Tibet Plateau Based on Multi-Source Remote Sensing Data

et al. 2018

View full text Add to dashboard Cite

Abstract:The Qinghai-Tibet Plateau (QTP) is known as the Third Polar of the earth and the Water Tower of Asia, with more than 70% of the area on the QTP is covered by permafrost possibly. An accurate permafrost distribution map based on valid and available methods is indispensable for the local environment evaluation and engineering constructions planning. Most of the previous permafrost maps have employed traditional mapping method based on field surveys and borehole investigation data. However their accuracy is limited because it is extremely difficulties in obtaining mass data in the high-altitude and cold regions as the QTP; moreover, the mapping method, which would effectively integrate many factors, is still facing great challenges. With the rapid development of remote sensing technology in permafrost mapping, spatial data derived from the satellite sensors can recognize the permafrost environment features and quantitatively estimate permafrost distribution. Until now there is no map indicated permafrost presence/absence on the QTP that has been generated only by remote sensing data as yet. Therefore, this paper used permafrost-influencing factors and examined distribution features of each factor in permafrost regions and seasonally frozen ground regions. Then, using the Decision Tree method with the environmental factors, the 1 km resolution permafrost map over the QTP was obtained. The result shows higher accuracy compared to the previous published map of permafrost on the QTP and the map of the glaciers, frozen ground and deserts in China, which also demonstrates that making comprehensive use of remote sensing technology in permafrost mapping research is fast, macro and feasible. Furthermore, this result provides a simple and valid method for further permafrost research.

show abstract

Section: Discussionmentioning

confidence: 94%

Section: Study Areamentioning

confidence: 99%

Permafrost Presence/Absence Mapping of the Qinghai-Tibet Plateau Based on Multi-Source Remote Sensing Data

et al. 2018

View full text Add to dashboard Cite

show abstract

“…To ensure that the study area samples included more vegetation types at a large spatial scale, 9 study areas were set up in the high-altitude permafrost regions of the Tibet Plateau based on updated vegetation maps of the permafrost zone of the QTP [14]. Excluding the discontinuous area in the permafrost zone of the QTP, a central point (89.5°E, 35.5°N) was first calculated by averaging the values of longitude and latitude.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, permafrost regions have better water conditions and are more favorable to vegetation growth compared with seasonally frozen soil regions [12,13]. The permafrost region of the QTP has unique vegetation types that mainly include alpine steppe (AS), alpine swamp meadow (ASM), alpine meadow (AM) and alpine desert (AD) [9,12,13,14]. These ecosystems have completely different hydrothermal processes as well as different limiting factors for vegetation growth.…”

Section: Introductionmentioning

confidence: 99%

Vegetation Changes in the Permafrost Regions of the Qinghai-Tibetan Plateau from 1982-2012: Different Responses Related to Geographical Locations and Vegetation Types in High-Altitude Areas

Wang

et al. 2017

PLoS ONE

Self Cite

View full text Add to dashboard Cite

The Qinghai-Tibetan Plateau (QTP) contains the largest permafrost area in a high-altitude region in the world, and the unique hydrothermal environments of the active layers in this region have an important impact on vegetation growth. Geographical locations present different climatic conditions, and in combination with the permafrost environments, these conditions comprehensively affect the local vegetation activity. Therefore, the responses of vegetation to climate change in the permafrost region of the QTP may be varied differently by geographical location and vegetation condition. In this study, using the latest Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) product based on turning points (TPs), which were calculated using a piecewise linear model, 9 areas within the permafrost region of the QTP were selected to investigate the effect of geographical location and vegetation type on vegetation growth from 1982 to 2012. The following 4 vegetation types were observed in the 9 selected study areas: alpine swamp meadow, alpine meadow, alpine steppe and alpine desert. The research results show that, in these study areas, TPs mainly appeared in 2000 and 2001, and almost 55.1% and 35.0% of the TPs were located in 2000 and 2001. The global standardized precipitation evapotranspiration index (SPEI) and 7 meteorological variables were selected to analyze their correlations with NDVI. We found that the main correlative variables to vegetation productivity in study areas from 1982 to 2012 were precipitation, surface downward long-wave radiation and temperature. Furthermore, NDVI changes exhibited by different vegetation types within the same study area followed similar trends. The results show that regional effects rather than vegetation type had a larger impact on changes in vegetation growth in the permafrost regions of the QTP, indicating that climatic factors had a larger impact in the permafrost regions than the environmental factors (including permafrost) related to the underlying surface conditions.

show abstract

“…Major techniques used for the detection, classification, and mapping of vegetation using remote sensing imagery are vegetation indices [20,21], spectral mixture analysis [22], temporal image-fusion [23,24], texture based measures [25], and supervised classification using machine learning classifiers such as maximum likelihood [26], random forests [27,28], decision trees [29], support vector machines [30], fuzzy learning [31], and neural networks [32][33][34]. Nevertheless, performance of existing large-scale land cover maps is limited to the discrimination of vegetation physiognomic types, which is still a challenging field [35].…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach

Sharma

Hara

Tateishi

2017

Land

View full text Add to dashboard Cite

This paper presents an evaluation of the multi-source satellite datasets such as Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) with different spatial and temporal resolutions for nationwide vegetation mapping. The random forests based machine learning and cross-validation approach was applied for evaluating the performance of different datasets. Cross-validation with the rich-feature datasets-with a sample size of 390-showed that the MODIS datasets provided highest classification accuracy (Overall accuracy = 0.80, Kappa coefficient = 0.77) compared with Landsat 8 (Overall accuracy = 0.77, Kappa coefficient = 0.74) and Sentinel-2 (Overall accuracy = 0.66, Kappa coefficient = 0.61) datasets. As a result, temporally rich datasets were found to be crucial for the vegetation physiognomic classification. However, in the case of Landsat 8 or Sentinel-2 datasets, sample size could be increased excessively as around 9800 ground truth points could be prepared within 390 MODIS pixel-sized polygons. The increase in the sample size significantly enhanced the classification using Landsat-8 datasets (Overall accuracy = 0.86, Kappa coefficient = 0.84). However, Sentinel-2 datasets (Overall accuracy = 0.77, Kappa coefficient = 0.74) could not perform as much as the Landsat-8 datasets, possibly because of temporally limited datasets covered by the Sentinel-2 satellites so far. A combination of the Landsat-8 and Sentinel-2 datasets slightly improved the classification (Overall accuracy = 0.89, Kappa coefficient = 0.87) than using the Landsat 8 datasets separately. Regardless of the fact that Landsat 8 and Sentinel-2 datasets have lower temporal resolutions than MODIS datasets, they could enhance the classification of otherwise challenging vegetation physiognomic types due to possibility of training a wider variation of physiognomic types at 30 m resolution. Based on these findings, an up-to-date 30 m resolution vegetation map was generated by using Landsat 8 and Sentinel-2 datasets, which showed better accuracy than the existing map in Japan.

show abstract

Mapping the vegetation distribution of the permafrost zone on the Qinghai-Tibet Plateau

Cited by 105 publications

References 39 publications

Permafrost Presence/Absence Mapping of the Qinghai-Tibet Plateau Based on Multi-Source Remote Sensing Data

Permafrost Presence/Absence Mapping of the Qinghai-Tibet Plateau Based on Multi-Source Remote Sensing Data

Vegetation Changes in the Permafrost Regions of the Qinghai-Tibetan Plateau from 1982-2012: Different Responses Related to Geographical Locations and Vegetation Types in High-Altitude Areas

High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach

Contact Info

Product

Resources

About