Susceptibility Assessment of Small, Shallow and Clustered Landslide in Malipo, southwest China

2021 ◽  
Author(s):  
Xuemei Liu ◽  
Yong Li ◽  
Pengcheng Su ◽  
Taiqiang Yang ◽  
Jun Zhang
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Landslide Susceptibility ◽  
Soil Moisture Content ◽  
Southwest China ◽  
Grid Cell ◽  
Information Value ◽  
Susceptibility Assessment ◽  
Mapping Unit ◽  
Slope Unit

<p><strong>Abstract: </strong>Susceptibility assessment of landslides over a large area depends on the basic spatial unit of mapping, each unit is assumed to have unique assessment value, so the division of mapping unit is directly related to the evaluation rate, grid cell or slope unit are usually be used in many researches. Grid cell divide the study region into regular squares of predefined size, each cell is assigned a value of influence factor. Slope unit based on hydrology divides the region by ridge and valley lines, which is more related to geological environment and it is hard to identify the subbasin boundary. Both units are used in this study for the assessment of small shallow and clustered landslides in vegetated slopes in Malipo, southwest China. Google earth map on February 7, 2019 was used to interpret the landslides. ArcGIS 10.2 software was used to produce landslide inventory map and obtained 1435 landslides in the study area; most frequent landslide areas are in the range of 62m<sup>2</sup> to 900m<sup>2</sup>. Field survey was carried out to verify uncertain factors and measure moisture soil content. Soil moisture content (SMC) map was obtained by Kriging Interpolation methods based on the field measured soil moisture content of 48 sample points. Information value (IV) model was used to generate landslide susceptibility assessment map and improved information value (IIV) model was used to determine whether the mapping unit with or without landslide. Seven factors, including slope angle, slope aspect, elevation, normalized difference vegetation Index (NDVI), Soil Moisture Content (SMC), distance to river and road were used as landslide influence factors. The Area under curve (AUC) values of the slope unit IIV, IV and grid cell were 0.814, 0.802 and 0.702 respectively for success rate. For prediction rate, the AUC values of the slope unit and grid cell were 0.803(IIV), 0.790(IV) and 0.699 respectively. Slope unit is more suitable than grid cell for assessing susceptibility of Small, Shallow and Cluster Landslide (Fig.1). Improved information value model can increase the accuracy of susceptibility assessment model for this characteristic landslide.</p><p><strong>Keywords: </strong>Landslide susceptibility assessment; Slope unit; Grid cell; Information value</p><p>                                                <strong> (a)</strong>  <img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gepj.612a5aa6550062062690161/sdaolpUECMynit/12UGE&app=m&a=0&c=e934e3e9858f863f856c55ba7f923603&ct=x&pn=gepj.elif&d=1" alt="" width="289" height="206">  <strong> (b)</strong>   <img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gepj.c0a33eb6550062262690161/sdaolpUECMynit/12UGE&app=m&a=0&c=f9c48114412d0742a895968d55be3fbd&ct=x&pn=gepj.elif&d=1" alt="" width="293" height="212">                           <strong>                                                      </strong></p><p><strong>                                                              Figure 1</strong> Landslide susceptibility maps (a)Slope unit-based and (b)Grid cell-based</p>

scholarly journals Spatial variability and patterns of surface soil moisture in a field plot of karst area in southwest China

2011 ◽  
Vol 57 (No. 9) ◽  
pp. 409-417 ◽  
Author(s):  
J.G. Zhang ◽  
H.S. Chen ◽  
Y.R. Su ◽  
X.L. Kong ◽  
W. Zhang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Rainy Season ◽  
Soil Moisture Content ◽  
Southwest China ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Field Plot ◽  
Rock Outcrops ◽  
Karst Depression

A field plot (100 m × 50 m) was chosen in a karst depression area of Huanjiang County, Guangxi Province of southwest China, with the aim of characterizing the variability and patterns of upper 15 cm soil moisture. Soil moisture content was measured at 5 m intervals by gravimetric method during dry and rainy seasons in 2005. Results indicated that the surface soil moisture presented a strong spatial dependence at the sampling times in the field scale. The variability of soil moisture by CV values and sill decreased with the increasing mean field soil moisture content either in dry or rainy season. In the dry season, mean soil moisture had a little influence on the sill owing to the previous tillage. But, in the rainy season, a heavy rain event could decrease the variability of soil moisture. The anisotropy characteristics were found that the variance was lower in 0° direction than that in 90° direction based on the northeast axis, and the range had opposite trend except for the sampling on March 15, 2005. The mosaic patterns of soil moisture exhibited the variability and its anisotropy visually. The rainfall (mean soil moisture), topography and micro-relief (rock outcrops) had important influence on the variability of soil moisture. To better understand the variability of soil moisture in the karst depression area, more soil samples should be required in the dry season and in a field with more rock outcrops.

scholarly journals Temporal Soil Moisture Variations in Different Vegetation Cover Types in Karst Areas of Southwest China: A Plot Scale Case Study

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1423 ◽  
Author(s):  
Qiuwen Zhou ◽  
Zhiyan Sun ◽  
Xiaolin Liu ◽  
Xiaocha Wei ◽  
Zheng Peng ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Rainy Season ◽  
Soil Moisture Content ◽  
Southwest China ◽  
Dry Season ◽  
Rainfall Amount ◽  
Vegetation Types ◽  
Strong Negative Correlation ◽  
Forest Lands

For different vegetation types, soil moisture content shows varying characteristics in different seasons and under different precipitation conditions. However, these characteristics have not been extensively analyzed in karst regions of southwest China. In this study, the soil moisture content of four plots of bare land, grassland, shrubland, and forestland was monitored, and the soil moisture content and corresponding meteorological data for each plot were analyzed. The results indicate that the average soil moisture content in grassland was the highest with weak temporal variation and that in bare, shrub, and forest lands soil moisture content was low with moderate temporal variation. The average soil moisture content in bare, grass, and forest lands was higher in the rainy season than in the dry season, whereas in shrubland, the soil moisture content was higher in the dry season than in the rainy season. Increase in soil moisture content during each precipitation event correlated with the rainfall amount. With increasing rainfall amount, soil moisture content in forest and shrub lands increased more than in bare and grass lands. The peak soil moisture time in each vegetation type plot varied and the peak soil moisture time was related to soil moisture content before a rainfall event. Temperature showed a strong negative correlation with soil moisture content for all vegetation cover types in both the dry and rainy season. Wind speed also showed a strong negative correlation with soil moisture content for all vegetation types during the dry season. Relative humidity had a strong positive correlation with soil moisture content in bare, shrub, and forest lands during the dry season as well as in the four vegetation types during the rainy season. These results demonstrate the variations in soil water characteristics across different vegetation types in karst regions of southwest China.

scholarly journals Effects of Vegetation Restoration on Regional Soil Moisture Content in the Humid Karst Areas—A Case Study of Southwest China

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 321
Author(s):  
Xiaocha Wei ◽  
Qiuwen Zhou ◽  
Mingyong Cai ◽  
Yujuan Wang
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Southwest China ◽  
Vegetation Restoration ◽  
Data Set ◽  
Karst Areas ◽  
Increasing Trend ◽  
Drying Trend

Soil moisture is one of the restricting factors in the humid karst areas, which feature strong spatial heterogeneity. However, current research about the effects of vegetation restoration on soil moisture content have mainly focused on plot scale and slope scale, while these effects still remain unclear at regional scale in this area. Taking Southwest China as a case study and based on the land parameter data record (LPDR) and enhanced vegetation index (EVI) data set during 2002–2018, this study analyzed the spatiotemporal variation characteristics of vegetation and soil moisture content, and evaluated the effects of vegetation restoration on regional soil moisture content dynamics in paired years with similar precipitation conditions. The results showed that the EVI generally increased at a rate of 0.035/10a during 2002–2018, while the soil moisture was dominated by a drying trend at a variation rate of −0.0006 (cm3/cm3)/10a. The increasing trend of EVI accounted for 90.90% across the study area, whereas the decreasing trend of soil moisture accounted for 51.66%, and the increasing trend of soil moisture accounted for 48.34%. In addition, the decreasing trend of soil moisture coupled with an increasing trend of EVI distributed in most of the study area, especially in the homogenous limestone area. Our results demonstrate that there were remarkable vegetation restoration efforts in a series of ecological restoration projects, which resulted in a drying trend of the regional soil moisture content in the humid karst areas. The results suggest that it is necessary to consider reasonable vegetation planting density and suitable revegetation types to balance the relationship between vegetation water consumption and soil moisture supplementation in vegetation restoration practice in the humid karst areas.

Analysis on Soil Moisture Content in the Middle Reaches of the Yellow River

2011 ◽  
Vol 28 (1) ◽  
pp. 85-91 ◽  
Author(s):  
Run-chun LI ◽  
Xiu-zhi ZHANG ◽  
Li-hua WANG ◽  
Xin-yan LV ◽  
Yuan GAO
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Yellow River ◽  
The Yellow River

Distribution of soil moisture content and its effect on the potential growth of the over-story species in North-East Chalkidiki

2001 ◽  
Vol 66 ◽  
Author(s):  
M. Aslanidou ◽  
P. Smiris
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Soil Depth ◽  
Moisture Distribution ◽  
Taxus Baccata ◽  
Mixed Stands ◽  
Potential Growth ◽  
North East ◽  
Soil Moisture Distribution

This  study deals with the soil moisture distribution and its effect on the  potential growth and    adaptation of the over-story species in north-east Chalkidiki. These  species are: Quercus    dalechampii Ten, Quercus  conferta Kit, Quercus  pubescens Willd, Castanea  sativa Mill, Fagus    moesiaca Maly-Domin and also Taxus baccata L. in mixed stands  with Fagus moesiaca.    Samples of soil, 1-2 kg per 20cm depth, were taken and the moisture content  of each sample    was measured in order to determine soil moisture distribution and its  contribution to the growth    of the forest species. The most important results are: i) available water  is influenced by the soil    depth. During the summer, at a soil depth of 10 cm a significant  restriction was observed. ii) the    large duration of the dry period in the deep soil layers has less adverse  effect on stands growth than in the case of the soil surface layers, due to the fact that the root system mainly spreads out    at a soil depth of 40 cm iii) in the beginning of the growing season, the  soil moisture content is    greater than 30 % at a soil depth of 60 cm, in beech and mixed beech-yew  stands, is 10-15 % in    the Q. pubescens  stands and it's more than 30 % at a soil depth of 60 cm in Q. dalechampii    stands.

scholarly journals Mapping barrier island soil moisture using a radiative transfer model of hyperspectral imagery from an unmanned aerial system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rehman S. Eon ◽  
Charles M. Bachmann
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Moisture Content ◽  
Layer Thickness ◽  
Soil Moisture Content ◽  
Hyperspectral Imagery ◽  
Barrier Island ◽  
Water Layer ◽  
Radiative Transfer Model ◽  
Transfer Model

AbstractThe advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system. Our approach uses a recently developed model known as the multilayer radiative transfer model of soil reflectance (MARMIT). MARMIT partitions contributions due to water and the sediment surface into equivalent but separate layers and describes these layers using an equivalent slab model formalism. The model water layer thickness along with the fraction of wet surface become parameters that must be optimized in a calibration step, with extinction due to water absorption being applied in the model based on equivalent water layer thickness, while transmission and reflection coefficients follow the Fresnel formalism. In this work, we evaluate the model in both field settings, using UAS hyperspectral imagery, and laboratory settings, using hyperspectral spectra obtained with a goniometer. Sediment samples obtained from four different field sites representing disparate environmental settings comprised the laboratory analysis while field validation used hyperspectral UAS imagery and coordinated ground truth obtained on a barrier island shore during field campaigns in 2018 and 2019. Analysis of the most significant wavelengths for retrieval indicate a number of different wavelengths in the short-wave infra-red (SWIR) that provide accurate fits to measured soil moisture content in the laboratory with normalized root mean square error (NRMSE)< 0.145, while independent evaluation from sequestered test data from the hyperspectral UAS imagery obtained during the field campaign obtained an average NRMSE = 0.169 and median NRMSE = 0.152 in a bootstrap analysis.

scholarly journals Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region

2021 ◽  
Vol 13 (8) ◽  
pp. 1562
Author(s):  
Xiangyu Ge ◽  
Jianli Ding ◽  
Xiuliang Jin ◽  
Jingzhe Wang ◽  
Xiangyue Chen ◽  
...  
Keyword(s):  
Data Mining ◽  
Soil Moisture ◽  
Moisture Content ◽  
Hyperspectral Imaging ◽  
Fractional Order ◽  
Arid Regions ◽  
Soil Moisture Content ◽  
Order Derivative ◽  
Imaging Data ◽  
Strategy I

Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing is an important monitoring technology for the soil moisture content (SMC) of agroecological systems in arid regions. This technology develops precision farming and agricultural informatization. However, hyperspectral data are generally used in data mining. In this study, UAV-based hyperspectral imaging data with a resolution o 4 cm and totaling 70 soil samples (0–10 cm) were collected from farmland (2.5 × 104 m2) near Fukang City, Xinjiang Uygur Autonomous Region, China. Four estimation strategies were tested: the original image (strategy I), first- and second-order derivative methods (strategy II), the fractional-order derivative (FOD) technique (strategy III), and the optimal fractional order combined with the optimal multiband indices (strategy IV). These strategies were based on the eXtreme Gradient Boost (XGBoost) algorithm, with the aim of building the best estimation model for agricultural SMC in arid regions. The results demonstrated that FOD technology could effectively mine information (with an absolute maximum correlation coefficient of 0.768). By comparison, strategy IV yielded the best estimates out of the methods tested (R2val = 0.921, RMSEP = 1.943, and RPD = 2.736) for the SMC. The model derived from the order of 0.4 within strategy IV worked relatively well among the different derivative methods (strategy I, II, and III). In conclusion, the combination of FOD technology and the optimal multiband indices generated a highly accurate model within the XGBoost algorithm for SMC estimation. This research provided a promising data mining approach for UAV-based hyperspectral imaging data.

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