scholarly journals Spatiotemporal Characteristics of Freeze-Thawing Erosion in the Source Regions of the Chin-Sha, Ya-Lung and Lantsang Rivers on the Basis of GIS

2021 ◽  
Vol 13 (2) ◽  
pp. 309
Author(s):  
Yuefeng Lu ◽  
Cong Liu ◽  
Yong Ge ◽  
Yulong Hu ◽  
Qiao Wen ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Evaluation Model ◽  
Scientific Evidence ◽  
Research Area ◽  
Tibet Plateau ◽  
Vegetation Coverage ◽  
Freeze Thaw ◽  
The North ◽  
Erosion Area ◽  
Erosion Intensity

Freeze-thawing erosion is mainly distributed in the tundra, which is one of the main factors affecting soil erosion and soil conservation and affects the economic development of relevant countries and regions. The study area was selected to the north of Tanggula Mountain and the south of Bayankera Mountain, to the east of The Qinghai-Tibet Plateau, as the headwaters of the Yangtze River and lancang River. The topography and climate were particularly prone to soil freeze-thawing erosion, and the ecological damage would seriously affect the production and life of people in the whole downstream area. Therefore, based on the analytic hierarchy process (AHP), this paper selects seven evaluation factors to analyze the temporal and spatial characteristics of freeze-thaw erosion in the study area and establishes a comprehensive weight evaluation model for freeze-thaw erosion. The results show that: (1) the evaluation model is effective, and the soil freeze-thawing erosion is strong in the whole research area; (2) the total area of the research area and the freeze-thawing erosion area are 418,843 km2 and 375,514 km2 respectively, the freeze-thawing erosion area accounting for 89.7% of the total research area, and the freeze-thawing erosion intensity ranged from 0.165 to 0.737; (3) the spatial distribution differs significantly, the freeze-thawing erosion intensity is mainly concentrated in high altitude areas, especially in the Tanggula Mountains; (4) slope, poor annual temperature, illumination, altitude and content of sand in soil accelerate soil freeze-thawing erosion, whereas vegetation index does not; wetness index enhanced the influence of vegetation coverage and sand content. (5) this research will provide scientific evidence for protection and restoration of ecological environment in the area.

scholarly journals Improving Spatial Soil Moisture Representation through the Integration of SMAP and PROBA-V Products

2018 ◽  
Vol 10 (10) ◽  
pp. 3459
Author(s):  
Shu-Di Fan ◽  
Yue-Ming Hu ◽  
Lu Wang ◽  
Zhen-Hua Liu ◽  
Zhou Shi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Factor Model ◽  
Original Method ◽  
Vegetation Coverage ◽  
Estimation Accuracy ◽  
Modified Model ◽  
The North ◽  
Moderate Resolution Imaging Spectroradiometer

To increase the spatial resolution of Soil Moisture Active Passive (SMAP), this study modifies the downscaling factor model based on the Temperature Vegetation Drought Index (TVDI) using data from the Project for On-Board Autonomy (PROBA-V). In the modified model, TVDI parameters were derived from the temperature-vegetation space and the Enhanced Vegetation Index (EVI). This study was conducted in the north China region using SMAP, PROBA-V, and Moderate Resolution Imaging Spectroradiometer satellite images. The 9-km spatial resolution SMAP data was downscaled to 0.3-km spatial resolution soil moisture using a modified downscaling method. Downscaling accuracies from the original and modified downscaling factor models were compared based on field observations. The results show that both methods generated similar spatial distributions in which soil moisture estimates increased as vegetation coverage increased from built-up areas to forest. However, based on the root mean square error between observations and estimations, the modified model demonstrated an increased estimation accuracy of 4.2% for soil moisture compared to the original method. This study also implies that downscaled soil moisture shows promise as a data source for subsequent watershed scale studies.

scholarly journals Drainage Density and Its Controlling Factors on the Eastern Margin of the Qinghai–Tibet Plateau

2022 ◽  
Vol 9 ◽  
Author(s):  
Hongshan Gao ◽  
Fenliang Liu ◽  
Tianqi Yan ◽  
Lin Qin ◽  
Zongmeng Li
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Arid Environment ◽  
Drainage Density ◽  
Tibet Plateau ◽  
Vegetation Coverage ◽  
Mean Annual Precipitation ◽  
Eastern Margin ◽  
Qinghai Tibet Plateau ◽  
Planation Surfaces

The drainage density (Dd) is an important index to show fluvial geomorphology. The study on Dd is helpful to understand the evolution of the whole hydrological and geomorphic process. Based on the Shuttle Radar Topography Mission 90-m digital elevation model, the drainage network of basins along the eastern margin of the Qinghai–Tibet Plateau is extracted using a terrain morphology-based method in ArcGIS 10.3, and Dd is calculated. The spatial characteristics of Dd are analyzed, and the relationship between Dd and its influencing factors, e.g., the topography, precipitation, and vegetation coverage, is explored. Our results show that terrains with a plan curvature ≥3 can represent the channels in the study area. Dd ranges from 2.5 to 0.1 km/km2, increases first, and then decreases from north to south on the eastern margin of the Qinghai–Tibet Plateau. Dd decreases with increasing average slope and average local relief. On the low-relief planation surfaces, Dd increases with increasing altitude, while on the rugged mountainous above planation surfaces, Dd decreases rapidly with increasing altitude. Dd first increased and then decreased with increasing mean annual precipitation (MAP) and normalized difference vegetation index (NDVI), and Dd reaches a maximum in the West Qinling Mountains with a semi-arid environment, indicating that Dd in different climatic regions of the eastern margin of the Qinghai–Tibet Plateau was mainly controlled by precipitation and vegetation.

scholarly journals SPATIO-TEMPORAL CHANGE OF VEGETATION COVERAGE AND ITS DRIVING FORCES BASED ON LANDSAT IMAGES: A CASE STUDY OF CHANGCHUN CITY

2018 ◽  
Vol XLII-3 ◽  
pp. 295-298 ◽  
Author(s):  
L. Dong ◽  
H. Jiang ◽  
L. Yang
Keyword(s):  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Driving Forces ◽  
Land Reclamation ◽  
Research Area ◽  
Vegetation Coverage ◽  
Landsat Images ◽  
Temperature And Precipitation ◽  
Spatio Temporal

Based on the Landsat images in 2006, 2011 and 2015, and the method of dimidiate pixel model, the Normalized Difference Vegetation Index (NDVI) and the vegetation coverage, this paper analyzes the spatio-temporal variation of vegetation coverage in Changchun, China from 2006 to 2015, and investigates the response of vegetation coverage change to natural and artificial factors. The research results show that in nearly 10 years, the vegetation coverage in Changchun dropped remarkably, and reached the minimum in 2011. Moreover, the decrease of maximum NDVI was significant, with a decrease of about 27.43 %, from 2006 to 2015. The vegetation coverage change in different regions of the research area was significantly different. Among them, the vegetation change in Changchun showed a little drop, and it decreased firstly and then increased slowly in Yushu, Nong’an and Dehui. In addition, the temperature and precipitation change, land reclamation all affect the vegetation coverage. In short, the study of vegetation coverage change contributes scientific and technical support to government and environmental protection department, so as to promote the coordinated development of ecology and economy.

Pemetaan Pulau Kecil Gelasa Kabupaten Bangka Tengah

2019 ◽  
Vol 2 (1) ◽  
pp. 11-14
Author(s):  
Wahyu Adi
Keyword(s):  
Coral Reefs ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Image Interpretation ◽  
Vegetation Coverage ◽  
Small Island ◽  
Shallow Waters ◽  
Small Islands ◽  
Seagrass Beds ◽  
Cover Density

Pulau Kecil Gelasa merupakan daerah yang belum banyak diteliti. Pemetaan ekosistem di pulau kecil dilakukan dengan bantuan citra Advanced Land Observing Satellite (ALOS). Penelitian terdahulu diketahui bahwa ALOS memiliki kemampuan memetakan terumbu karang dan padang lamun di perairan dangkal serta mampu memetakan kerapatan penutupan vegetasi. Metode interpretasi citra menggunakan alogaritma indeks vegetasi pada citra ALOS yaitu NDVI (Normalized Difference Vegetation Index), serta pendekatan Lyzengga untuk mengkoreksi kolom perairan. Hasil penelitian didapatkan luasan Padang Lamun di perairan dangkal 41,99 Ha, luasan Terumbu Karang 125,57 Ha. Hasil NDVI di daratan/ pulau kecil Gelasa untuk Vegetasi Rapat seluas 47,62 Ha; luasan penutupan Vegetasi Sedang 105,86 Ha; dan penutupan Vegetasi Jarang adalah 34,24 Ha.   Small Island Gelasa rarely studied. Mapping ecosystems on small islands with the image of Advanced Land Observing Satellite (ALOS). Previous research has found that ALOS has the ability to map coral reefs and seagrass beds in shallow water, and is able to map vegetation cover density. The method of image interpretation uses the vegetation index algorithm in the ALOS image, NDVI (Normalized Difference Vegetation Index), and the Lyzengga approach to correct the water column. The results of the study were obtained in the area of Seagrass Padang in the shallow waters of 41.99 ha, the area of coral reefs was 125.57 ha. NDVI results on land / small islands Gelasa for dense vegetation of 47.62 ha; area of Medium Vegetation coverage 105.86 Ha; and the coverage of Rare Vegetation is 34.24 Ha.

scholarly journals Optimization of Rocky Desertification Classification Model Based on Vegetation Type and Seasonal Characteristic

2021 ◽  
Vol 13 (15) ◽  
pp. 2935
Author(s):  
Chunhua Qian ◽  
Hequn Qiang ◽  
Feng Wang ◽  
Mingyang Li
Keyword(s):  
Learning Algorithm ◽  
Vegetation Type ◽  
Research Area ◽  
Classification Model ◽  
Guizhou Province ◽  
Model Accuracy ◽  
Spatiotemporal Evolution ◽  
Rocky Desertification ◽  
The North ◽  
Svm Model

Building a high-precision, stable, and universal automatic extraction model of the rocky desertification information is the premise for exploring the spatiotemporal evolution of rocky desertification. Taking Guizhou province as the research area and based on MODIS and continuous forest inventory data in China, we used a machine learning algorithm to build a rocky desertification model with bedrock exposure rate, temperature difference, humidity, and other characteristic factors and considered improving the model accuracy from the spatial and temporal dimensions. The results showed the following: (1) The supervised classification method was used to build a rocky desertification model, and the logical model, RF model, and SVM model were constructed separately. The accuracies of the models were 73.8%, 78.2%, and 80.6%, respectively, and the kappa coefficients were 0.61, 0.672, and 0.707, respectively. SVM performed the best. (2) Vegetation types and vegetation seasonal phases are closely related to rocky desertification. After combining them, the model accuracy and kappa coefficient improved to 91.1% and 0.861. (3) The spatial distribution characteristics of rocky desertification in Guizhou are obvious, showing a pattern of being heavy in the west, light in the east, heavy in the south, and light in the north. Rocky desertification has continuously increased from 2001 to 2019. In conclusion, combining the vertical spatial structure of vegetation and the differences in seasonal phase is an effective method to improve the modeling accuracy of rocky desertification, and the SVM model has the highest rocky desertification classification accuracy. The research results provide data support for exploring the spatiotemporal evolution pattern of rocky desertification in Guizhou.

scholarly journals Spatial and Temporal Changes in Vegetation in the Ruoergai Region, China

Forests ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 76
Author(s):  
Yahui Guo ◽  
Jing Zeng ◽  
Wenxiang Wu ◽  
Shunqiang Hu ◽  
Guangxu Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Linear Trend ◽  
Spectral Characteristics ◽  
Growth Conditions ◽  
Temporal Changes ◽  
Vegetation Coverage ◽  
Dynamic Changes ◽  
Spatial And Temporal Changes ◽  
Temporal And Spatial

Timely monitoring of the changes in coverage and growth conditions of vegetation (forest, grass) is very important for preserving the regional and global ecological environment. Vegetation information is mainly reflected by its spectral characteristics, namely, differences and changes in green plant leaves and vegetation canopies in remote sensing domains. The normalized difference vegetation index (NDVI) is commonly used to describe the dynamic changes in vegetation, but the NDVI sequence is not long enough to support the exploration of dynamic changes due to many reasons, such as changes in remote sensing sensors. Thus, the NDVI from different sensors should be scientifically combined using logical methods. In this study, the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI from the Advanced Very High Resolution Radiometer (AVHRR) and Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI are combined using the Savitzky–Golay (SG) method and then utilized to investigate the temporal and spatial changes in the vegetation of the Ruoergai wetland area (RWA). The dynamic spatial and temporal changes and trends of the NDVI sequence in the RWA are analyzed to evaluate and monitor the growth conditions of vegetation in this region. In regard to annual changes, the average annual NDVI shows an overall increasing trend in this region during the past three decades, with a linear trend coefficient of 0.013/10a, indicating that the vegetation coverage has been continuously improving. In regard to seasonal changes, the linear trend coefficients of NDVI are 0.020, 0.021, 0.004, and 0.004/10a for spring, summer, autumn, and winter, respectively. The linear regression coefficient between the gross domestic product (GDP) and NDVI is also calculated, and the coefficients are 0.0024, 0.0015, and 0.0020, with coefficients of determination (R2) of 0.453, 0.463, and 0.444 for Aba, Ruoergai, and Hongyuan, respectively. Thus, the positive correlation coefficients between the GDP and the growth of NDVI may indicate that increased societal development promotes vegetation in some respects by resulting in the planting of more trees or the promotion of tree protection activities. Through the analysis of the temporal and spatial NDVI, it can be assessed that the vegetation coverage is relatively large and the growth condition of vegetation in this region is good overall.

scholarly journals Analysis of Drought Characteristics in Xilingol Grassland of Northern China Based on SPEI and Its Impact on Vegetation

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Siqin Tong ◽  
Yuhai Bao ◽  
Rigele Te ◽  
Qiyun Ma ◽  
Si Ha ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Pearson Correlation ◽  
Growing Season ◽  
Vegetation Coverage ◽  
Semiarid Region ◽  
Linear Regression Method ◽  
Upward Trend ◽  
Drought Effect ◽  
Positive Correlation

This research is based on the standardized precipitation evapotranspiration index (SPEI) and normalized difference vegetation index (NDVI) which represent the drought and vegetation condition on land. Take the linear regression method and Pearson correlation analysis to study the spatial and temporal evolution of SPEI and NDVI and the drought effect on vegetation. The results show that (1) during 1961–2015, SPEI values at different time scales showed a downward trend; SPEI-12 has a mutation in 1997 and the SPEI value significantly decreased after this year. (2) During 2000–2015, the annual growing season SPEI has an obvious upward trend in time and the apparent wetting spatially. (3) In the recent 16 years, the growing season NDVI showed an upward trend and more than 80% of the total area’s vegetation increased in Xilingol. (4) Vegetation coverage in Xilingol grew better in humid years and opposite in arid years. SPEI and NDVI had a significant positive correlation; 98% of the region showed positive correlation, indicating that meteorological drought affects vegetation growth more in arid and semiarid region. (5) The effect of drought on vegetation has lag effect, and the responses of different grassland types to different scales of drought were different.

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