scholarly journals KARST ROCKY DESERTIFICATION INFORMATION EXTRACTION BASED ON THE DECISION TREE

Author(s):  
C. J. Su ◽  
T. Yue ◽  
L. Jiang ◽  
X. M. Li ◽  
W. G. Wang

Abstract. Rocky desertification is a common geo-ecological disasters in China are mainly distributed in southwest karst region, and a wide range of further deterioration. Based on the theory of decision tree Guangxi rocky information extraction, selection of experimental data of Guangxi Zhuang Autonomous Region in 2005 TM image. First of remote sensing images after geometric correction image registration and other pretreatment. Secondly based on binary model of pixel, the Guangxi Zhuang Autonomous Region NDVI values and vegetation cover and slope analysis combining the results of Guangxi Zhuang Autonomous Region, the use of decision tree classification of remote sensing images, and finally get different levels of Guangxi Zhuang Autonomous Region rocky area and spatial distribution. The experimental results showed that: 2005 Guangxi rocky area of about 22,000 km2, accounting for 9% of the total land area in Guangxi, accounting for 24.30% of the karst area the overall classification accuracy of 89.03%, Kappa coefficient was 0.8417. From the classification results and the accuracy evaluation shows that the use of the information extracted rocky achieve better results.

Author(s):  
M. Yao ◽  
G. Zhou ◽  
W. Wang ◽  
Z. Wu ◽  
Y. Huang ◽  
...  

Karst area is a pure natural resource base, at the same time, due to the special geological environment; there are droughts and floods alternating with frequent karst collapse, rocky desertification and other resource and environment problems, which seriously restrict the sustainable economic and social development in karst areas. Therefore, this paper identifies and studies the karst, and clarifies the distribution of karst. Provide basic data for the rational development of resources in the karst region and the governance of desertification. Due to the uniqueness of the karst landscape, it can’t be directly recognized and extracted by computer in remote sensing images. Therefore, this paper uses the idea of “RS + DEM” to solve the above problems. this article is based on Landsat-5 TM imagery in 2010 and DEM data, proposes the methods to identify karst information research what is use of slope vector diagram, vegetation distribution map, distribution map of karst rocky desertification and other auxiliary data in combination with the signs for human-computer interaction interpretation, identification and extraction of peak forest, peaks cluster and isolated peaks, and further extraction of karst depression. Experiments show that this method achieves the “RS + DEM” mode through the reasonable combination of remote sensing images and DEM data. It not only effectively extracts karst areas covered with vegetation, but also quickly and accurately locks down the karst area and greatly improves the efficiency and precision of visual interpretation. The accurate interpretation rate of karst information in study area in this paper is 86.73 %.


2012 ◽  
Vol 170-173 ◽  
pp. 2803-2807
Author(s):  
Yan Hua Sun ◽  
Ping Wang

High resolution remote sensing images generally refer to image to the spatial resolution within 10m aerospace、aviation remote sensing images. The emergence of high-resolution images strengthened the ability to recognize the large scale features, especially for the extraction of houses information in mining area. High spatial resolution image has rich delicate texture feature, it is urgent to solution the problem of how to extract the features. The technology is very useful for statistic houses information、village relocation assessment and research of pressure coal status, providing important data basis for village relocation, statistics, assessment. Taking henan as a mining area for example, houses information extraction methods are discussed. This paper mainly research contents as followings: It is combined with the space texture information of high resolution imaging rich, using different methods to extract building information, including followings: First, ordinary image segmentation technology; this method is simple and feasible, but extracted housing information is not accurate. Second, the object-oriented method of feature extraction technology, visualization degree and extracting accuracy of this method is higher; Third, it has conducted the preliminary height extraction of the houses; according to the solar altitude angles and the shadow of the houses to calculate the height of the houses. And considering the influence of undulating terrain, using the terrain DEM data to analyze study area, finally determined the shadow length, and then used solar altitude angles to calculate houses height. Based on the verification, accuracy evaluation results show that houses contour information extraction accuracy is: accuracy of the number and area is over 80%, the total rate of wrong classifications is lower. Houses highly information extraction accuracy is within the 85%. The research methods are effective.


2021 ◽  
Author(s):  
Stan Thorez ◽  
Koen Blanckaert ◽  
Ulrich Lemmin ◽  
David Andrew Barry

<p>Lake and reservoir water quality is impacted greatly by the input of momentum, heat, oxygen, sediment, nutrients and contaminants delivered to them by riverine inflows. When such an inflow is negatively buoyant, it will plunge upon contact with the receiving ambient water and form a gravity-driven current near the bed (density current). If such a current is sediment-laden, its bulk density can be higher than that of the surrounding ambient water, even if its carrying fluid has a density lower than that of the surrounding ambient water. After sufficient sediment particles have settled however, the buoyancy of the current can reverse and lead to the plume rising up from the bed, a process referred to as lofting. In a stratified environment, the river plume may then find its way into a layer of neutral buoyancy to form an intermediate current (interflow). A deeper understanding of the wide range of hydrodynamic processes related to the transitions from open-channel inflow to underflow (plunging) and from underflow to interflow (lofting) is crucial in predicting the fate of all components introduced into the lake or reservoir by the inflow.</p><p>Field measurements of the plunging inflow of the negatively buoyant Rhône River into Lake Geneva (Switzerland/France) are presented. A combination of a vessel-mounted ADCP and remote sensing cameras was used to capture the three-dimensional flow field of the plunging and lofting transition zones over a wide range of spatial and temporal scales.</p><p>In the plunge zone, the ADCP measurements show that the inflowing river water undergoes a lateral (perpendicular to its downstream direction) slumping movement, caused by its density surplus compared to the ambient lake water and the resulting baroclinic vorticity production. This effect is also visible in the remote sensing images in the form of a distinct plume of sediment-rich water with a triangular shape leading away from the river mouth in the downstream direction towards a sharp tip. A wide range of vortical structures, which most likely impact the amount of mixing taking place, is also visible at the surface in the plunging zone.</p><p>In the lofting zone, the ADCP measurements show that the underflow undergoes a lofting movement at its edges. This is most likely caused by a higher sedimentation rate due to the lower velocities at the underflow edges and leads to a part of the underflow peeling off and forming an interflow, while the higher velocity core of the underflow continues following the bed. Here, the baroclinic vorticity production works in the opposite direction as that in the plunge zone. Further downstream, as more particles have settled and the surrounding ambient water has become denser, the remaining underflow also undergoes a lofting motion. The remnants of these lofting processes show in the remote sensing images as intermittent ‘boils’ of sediment rich water reaching the surface and traces of surface layer leakage.</p>


2020 ◽  
Vol 12 (18) ◽  
pp. 2985 ◽  
Author(s):  
Yeneng Lin ◽  
Dongyun Xu ◽  
Nan Wang ◽  
Zhou Shi ◽  
Qiuxiao Chen

Automatic road extraction from very-high-resolution remote sensing images has become a popular topic in a wide range of fields. Convolutional neural networks are often used for this purpose. However, many network models do not achieve satisfactory extraction results because of the elongated nature and varying sizes of roads in images. To improve the accuracy of road extraction, this paper proposes a deep learning model based on the structure of Deeplab v3. It incorporates squeeze-and-excitation (SE) module to apply weights to different feature channels, and performs multi-scale upsampling to preserve and fuse shallow and deep information. To solve the problems associated with unbalanced road samples in images, different loss functions and backbone network modules are tested in the model’s training process. Compared with cross entropy, dice loss can improve the performance of the model during training and prediction. The SE module is superior to ResNext and ResNet in improving the integrity of the extracted roads. Experimental results obtained using the Massachusetts Roads Dataset show that the proposed model (Nested SE-Deeplab) improves F1-Score by 2.4% and Intersection over Union by 2.0% compared with FC-DenseNet. The proposed model also achieves better segmentation accuracy in road extraction compared with other mainstream deep-learning models including Deeplab v3, SegNet, and UNet.


Author(s):  
Jingtan Li ◽  
Maolin Xu ◽  
Hongling Xiu

With the resolution of remote sensing images is getting higher and higher, high-resolution remote sensing images are widely used in many areas. Among them, image information extraction is one of the basic applications of remote sensing images. In the face of massive high-resolution remote sensing image data, the traditional method of target recognition is difficult to cope with. Therefore, this paper proposes a remote sensing image extraction based on U-net network. Firstly, the U-net semantic segmentation network is used to train the training set, and the validation set is used to verify the training set at the same time, and finally the test set is used for testing. The experimental results show that U-net can be applied to the extraction of buildings.


Author(s):  
Hessah Albanwan ◽  
Rongjun Qin

Remote sensing images and techniques are powerful tools to investigate earth’s surface. Data quality is the key to enhance remote sensing applications and obtaining clear and noise-free set of data is very difficult in most situations due to the varying acquisition (e.g., atmosphere and season), sensor and platform (e.g., satellite angles and sensor characteristics) conditions. With the increasing development of satellites, nowadays Terabytes of remote sensing images can be acquired every day. Therefore, information and data fusion can be particularly important in the remote sensing community. The fusion integrates data from various sources acquired asynchronously for information extraction, analysis, and quality improvement. In this chapter, we aim to discuss the theory of spatiotemporal fusion by investigating previous works, in addition to describing the basic concepts and some of its applications by summarizing our prior and ongoing works.


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