Improvement of mapping vegetation cover for arid and semiarid areas using a local nonlinear modelling method and landsat images

2020 ◽  
Vol 42 (3) ◽  
pp. 161
Author(s):  
H. Sun ◽  
Q. Wang ◽  
G. X. Wang ◽  
P. Luo ◽  
F. G. Jiang
Keyword(s):  
Vegetation Cover ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Landsat 8 ◽  
Landsat Images ◽  
Modelling Method ◽  
Semiarid Areas ◽  
Arid And Semiarid Areas ◽  
Land Degradation And Desertification ◽  
Arid And Semiarid

Accurately estimating and mapping vegetation cover for monitoring land degradation and desertification of arid and semiarid areas using remotely sensed images is promising but challenging in remote, sparsely vegetated and large areas. In this study, a novel method – geographically weighted logistic regression (GWLR – integrating geographically weighted regression (GWR) and a logistic model) was proposed to improve vegetation cover mapping of Kangbao County, Hebei of China using Landsat 8 image and field data. Additionally, a new method to determine the bandwidth of GWLR is presented. Using cross-validation, GWLR was compared with a globally linear stepwise regression (LSR), a local linear modelling method GWR and a nonparametric method, k-nearest neighbours (kNN) with varying numbers of nearest plots. Results demonstrated (1) the red and near infrared relevant band ratios and vegetation indices significantly improved mapping; (2) the GWLR, GWR and kNN methods led to more accurate predictions than LSR; (3) GWLR reduced overestimations and underestimations compared with LSR, kNN and GWR, and also eliminated negative and very large estimates caused by GWR and LSR; and (4) The maximum distance of spatial autocorrelation could be used to determine the bandwidth for GWLR. Overall, GWLR proved more promising for mapping vegetation cover of arid and semiarid areas.

scholarly journals Optimizing kNN for Mapping Vegetation Cover of Arid and Semi-Arid Areas Using Landsat images

2018 ◽  
Vol 10 (8) ◽  
pp. 1248 ◽  
Author(s):  
Hua Sun ◽  
Qing Wang ◽  
Guangxing Wang ◽  
Hui Lin ◽  
Peng Luo ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Near Infrared ◽  
Good Alternative ◽  
Landsat 8 ◽  
Arid Areas ◽  
Landsat Images ◽  
Infrared Band ◽  
K Value ◽  
Mean Square Errors ◽  
Semi Arid

Land degradation and desertification in arid and semi-arid areas is of great concern. Accurately mapping percentage vegetation cover (PVC) of the areas is critical but challenging because the areas are often remote, sparsely vegetated, and rarely populated, and it is difficult to collect field observations of PVC. Traditional methods such as regression modeling cannot provide accurate predictions of PVC in the areas. Nonparametric constant k-nearest neighbors (Cons_kNN) has been widely used in estimation of forest parameters and is a good alternative because of its flexibility. However, using a globally constant k value in Cons_kNN limits its ability of increasing prediction accuracy because the spatial variability of PVC in the areas leads to spatially variable k values. In this study, a novel method that spatially optimizes determining the spatially variable k values of Cons_kNN, denoted with Opt_kNN, was proposed to map the PVC in both Duolun and Kangbao County located in Inner Mongolia and Hebei Province of China, respectively, using Landsat 8 images and sample plot data. The Opt_kNN was compared with Cons_kNN, a linear stepwise regression (LSR), a geographically weighted regression (GWR), and random forests (RF) to improve the mapping for the study areas. The results showed that (1) most of the red and near infrared band relevant vegetation indices derived from the Landsat 8 images had significant contributions to improving the mapping accuracy; (2) compared with LSR, GWR, RF and Cons-kNN, Opt_kNN resulted in consistently higher prediction accuracies of PVC and decreased relative root mean square errors by 5%, 11%, 5%, and 3%, respectively, for Duolun, and 12%, 1%, 23%, and 9%, respectively, for Kangbao. The Opt_kNN also led to spatially variable and locally optimal k values, which made it possible to automatically and locally optimize k values; and (3) the RF that has become very popular in recent years did not perform the predictions better than the Opt_kNN for the both areas. Thus, the proposed method is very promising to improve mapping the PVC in the arid and semi-arid areas.

scholarly journals Detecting Forest Fire Area using Landsat Images: A Case Study of Manang District, Nepal

2021 ◽  
Author(s):  
Pawan Thapa
Keyword(s):  
Forest Fires ◽  
Near Infrared ◽  
Human Life ◽  
Vegetation Indices ◽  
Google Earth ◽  
Burn Severity ◽  
Landsat 8 ◽  
Landsat Images ◽  
Severity Rating ◽  
Forest Regions

Abstract Background: Wildfires are on the rise for various reasons, including hunting, the growth of new plants, and the encroachment of forest regions, particularly in developing countries. As a result, it will lose its environment, property, wildlife, and human life. Methods: It generates a burn severity map that can estimate the extent of wildfire damage. The nine bands and vegetation indices are derived using Google Earth Engine (GEE) and the Quantum Geographic Information System (QGIS) platform from Landsat 8 satellite imagery. The Manang district employs wavelengths near-infrared (NIR) and shortwave-infrared (SWIR) to determine burnt patches and burn severity. Results: According to the evaluation, 26 percent of forest fires have moderate, low, high, and higher severity; however, 30 percent of unburned and low-severity fires receive a severity rating of 37 percent. Thus, it shows a considerable rise in wildfires in the Manang area. Conclusion: In general, it has been a novel technique for recognizing wildfire hotspots and mapping their intensity in higher elevations that takes fewer resources and time. Such necessary data assists vital stakeholders, communities, and decision-makers in making well-informed decisions.

scholarly journals Quantitative assessment of the dynamics and attribution of arable land water scarcity for arid and semiarid areas based on water footprint framework: the Inner Mongolia case

2021 ◽  
Author(s):  
Yuanyuan Wang ◽  
Fanhao Meng ◽  
Min Luo
Keyword(s):  
Water Scarcity ◽  
Inner Mongolia ◽  
Water Footprint ◽  
Arable Land ◽  
Well Being ◽  
Water Shortages ◽  
Semiarid Areas ◽  
Arid And Semiarid Areas ◽  
Land Water ◽  
Arid And Semiarid

Abstract Growing water shortages have been a systemic risk around the world, especially in arid and semi-arid areas, with seriously threatening global food security and human well-being. Reasonable and accurate evaluations of the water shortages of cultivated lands provide scientific reference for irrigation strategies. In this study, to better understand the distribution and cause of water scarcity for the arid and semiarid areas, we used the arable land water scarcity index (AWSI), based on water footprint theory to accurately estimate the temporal and spatial patterns of the AWSI of Inner Mongolia in China over 1999–2018, and further reveal the key factors influencing the AWSI distribution. The AWSI distribution pattern of Inner Mongolia was high in southwest and low in northeast, with an average value of 0.63 and suffering from high water stress for a long time. The AWSI presented an increasing trend in 1999–2018, with slow in west (change rate2%) and fast in east (2%). The main factors that significantly affected the AWSI were precipitation, relative humidity, and agricultural planting area. This study can provide scientific reference for the formulation of agricultural water management and sustainable use strategies in arid and semiarid areas.

Assessment of the State of the Territories of Closed Waste Disposal Facilities by Vegetation Indices

2021 ◽  
Vol 25 (9) ◽  
pp. 30-37
Author(s):  
N.N. Sliusar ◽  
A.P. Belousova ◽  
G.M. Batrakova ◽  
R.D. Garifzyanov ◽  
M. Huber-Humer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Waste Disposal ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Cost Effective ◽  
Google Earth ◽  
The State ◽  
Landsat 8 ◽  
The Earth

The possibilities of using remote sensing of the Earth data to assess the formation of phytocenoses at reclaimed dumps and landfills are presented. The objects of study are landfills and dumps in the Perm Territory, which differed from each other in the types and timing of reclamation work. The state of the vegetation cover on the reclaimed and self-overgrowing objects was compared with the reference plots with naturally formed herbage of zonal meadow vegetation. The process of reclamation of the territory of closed landfills was assessed by the presence and homogeneity of the vegetation layer and by the values of the vegetation index NDVI. To identify the dynamics of changes in the vegetation cover, we used multi-temporal satellite images from the open resources of Google Earth and images in the visible and infrared ranges of the Landsat-5/TM and Landsat-8/OLI satellites. It is shown that the data of remote sensing of the Earth, in particular the analysis of vegetation indices, can be used to assess the dynamics of overgrowing of territories of reclaimed waste disposal facilities, as well as an additional and cost-effective method for monitoring the restoration of previously disturbed territories.

Distinct assembly processes and determinants of soil microbial communities between farmland and grassland in arid and semiarid areas

2021 ◽  
Author(s):  
Aiai Xu ◽  
Jie Liu ◽  
Zhiying Guo ◽  
Changkun Wang ◽  
Kai Pan ◽  
...  
Keyword(s):  
Microbial Community ◽  
Variable Selection ◽  
Microbial Communities ◽  
Soil Microbial Community ◽  
Environmental Gradient ◽  
Soil Microbial Communities ◽  
Soil Microbial ◽  
Semiarid Areas ◽  
Arid And Semiarid Areas ◽  
Arid And Semiarid

It is critical to identify the assembly processes and determinants of soil microbial communities to better predict soil microbial responses to environmental change in arid and semiarid areas. Here, soils from 16 grassland-only, 9 paired grassland and farmland, and 16 farmland-only sites were collected across the central Inner Mongolia Plateau covering a steep environmental gradient. Through analyzing the paired samples, we discovered that land uses had strong effects on soil microbial communities, but weak effects on their assembly processes. For all samples, although no environmental variables were significantly correlated with the net relatedness index (NRI), both the nearest taxon index (NTI) and the β-nearest taxon index (βNTI) were most related to mean annual precipitation (MAP). With the increase of MAP, soil microbial taxa at the tips of the phylogenetic tree were more clustered, and the contribution of determinism increased. Determinism (48.6%), especially variable selection (46.3%), and stochasticity (51.4%) were almost equal in farmland, while stochasticity (75.0%) was dominant in grassland. Additionally, Mantel tests and redundancy analyses (RDA) revealed that the main determinants of soil microbial community structure were MAP in grassland, but mean annual temperature (MAT) in farmland. MAP and MAT were also good predictors of the community composition (the top 200 dominant OTUs) in grassland and farmland, respectively. Collectively, in arid and semiarid areas, soil microbial communities were more sensitive to environmental change in farmland than in grassland, and unlike the major impact of MAP on grassland microbial communities, MAT was the primary driver of farmland microbial communities. Importance As one of the most diverse organisms, soil microbes play indispensable roles in many ecological processes in arid and semiarid areas with limited macrofaunal and plant diversity, yet the mechanisms underpinning soil microbial community are not fully understood. In this study, soil microbial communities were investigated along a 500 km transect covering a steep environmental gradient across farmland and grassland in the areas. The results showed that precipitation was the main factor mediating the assembly processes. Determinism was more influential in farmland, and variable selection of farmland was twice that of grassland. Temperature mainly drove farmland microbial communities, while precipitation mainly affected grassland microbial communities. These findings provide new information about the assembly processes and determinants of soil microbial communities in arid and semiarid areas, consequently improving the predictability of the community dynamics, which have implications for sustaining soil microbial diversity and ecosystem functioning, particularly under global climate change conditions.

Grasses for Arid and Semiarid Areas

Forages ◽  
2020 ◽  
pp. 313-330
Author(s):  
Daren D. Redfearn ◽  
Keith R. Harmoney ◽  
Alexander J. Smart
Keyword(s):  
Semiarid Areas ◽  
Arid And Semiarid Areas ◽  
Arid And Semiarid

scholarly journals The role of unsaturated zone in estimating groundwater recharge in arid and semiarid areas as depicted by geochemical tracers

2019 ◽  
Vol 98 ◽  
pp. 12007
Author(s):  
Tianming Huang ◽  
Baoqiang Ma ◽  
Yin Long ◽  
Zhonghe Pang
Keyword(s):  
Groundwater Recharge ◽  
Unsaturated Zone ◽  
Surface Boundary ◽  
Recharge Rate ◽  
Semiarid Area ◽  
Near Surface ◽  
Geochemical Tracers ◽  
Semiarid Areas ◽  
Arid And Semiarid Areas ◽  
Arid And Semiarid

In arid and semiarid area, the recharge rate is relatively limited and the unsaturated zone (UZ) is commonly thick. The moisture in the UZ may represent the water infiltrating from precipitation during the past decades to thousands of years. Therefore, the multiple geochemical tracers in soil moisture, including Cl (chloride mass balance), 3H (tritium peak displacement), NO3, 2H, 18O, can be used to estimate diffuse recharge rate and related recharge characteristics. Based on 45 UZ profiles with maximum depth of 62 m in the Ordos Basin in NW China, a typical arid and semiarid area, we has used multiple geochemical tracers to study the following recharge informations: (1) reconstruction of groundwater recharge history, (2) determination of groundwater recharge mechanism, and (3) assessment of impact of vegetation changes on groundwater recharge. The results show that the soil texture (epically the shallow soil), vegetation and precipitation mainly control the recharge rate. This study also found that shallow groundwater in arid and semiarid areas is often not in equilibrium with near-surface boundary conditions. To estimate present recharge information, the UZ must be considered. The whole recharge process from precipitation to groundwater cannot be well understood unless the UZ have been included in arid and semiarid areas.

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