The monitoring of Kailuan mining area land cover change based on multi-level classification

2010 ◽  
Author(s):  
Li Xingli ◽  
Gao Junhai
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Mining Area ◽  
Multi Level

scholarly journals Land Cover Change of Post-Tin Mining Land Conservation Area and Its Surroundings in Perimping Sub Watershed, Bangka Regency

2018 ◽  
Vol 73 ◽  
pp. 04021
Author(s):  
Meike Erthalia ◽  
Supriatna ◽  
Astrid Damayanti
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Cover Crops ◽  
Mining Area ◽  
Google Earth ◽  
Land Conservation ◽  
Local People ◽  
Degraded Land ◽  
Physical Damage ◽  
Conservation Area

Tin mining is one of the land uses that causes physical damage to the land. Degraded land due to the mining activity requires a conservation. Conservation of post-tin mining land in Perimping Sub Watershed consists of reclamation and revegetation by planting kinds of fast-growing plants and cover crops. As land management, conservation is conducted to establish the diversity of land cover and to recover the land quality to be more productive for the local people. This study aimed to analyze the land cover change in land conservation of post-tin mining area. Moreover, also to identify the condition of post-tin mining land which has been conserved. Land cover map of 2011, 2014, and 2017 were produced from Google Earth imagery. Field validation was conducted to determine the existence of cover types on conservation land and interviews were conducted to find other impact of post-tin mining land conservation for local people. The result shows that land cover change in post-tin mining land conservation area over 6 years dominated by escalation of land cover such as mining, plantations. Monitoring of land cover change in conservation area is important to measure the effectivity of land conservation in post-tin mining area.

scholarly journals Multi-Level Land Cover Change Analysis in the Forest-Savannah Transition Zone of the Kintampo Municipality, Ghana

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Raymond Aabeyir ◽  
◽  
Wilson Agyei Agyare ◽  
Michael J. C. Weir ◽  
Stephen Adu-Bredu ◽  
...  
Keyword(s):  
Land Cover ◽  
Transition Zone ◽  
Land Cover Change ◽  
Change Analysis ◽  
Multi Level ◽  
Land Cover Change Analysis

scholarly journals A Multi-Level Output-Based DBN Model for Fine Classification of Complex Geo-Environments Area Using Ziyuan-3 TMS Imagery

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2089
Author(s):  
Meng Li ◽  
Zhuang Tang ◽  
Wei Tong ◽  
Xianju Li ◽  
Weitao Chen ◽  
...  
Keyword(s):  
Land Cover ◽  
Mining Area ◽  
Open Pit ◽  
Open Pit Mining ◽  
Fine Scale ◽  
Land Cover Types ◽  
Mining Areas ◽  
Sample Data ◽  
Multi Level ◽  
Fine Classification

Fine-scale land use and land cover (LULC) data in a mining area are helpful for the smart supervision of mining activities. However, the complex landscape of open-pit mining areas severely restricts the classification accuracy. Although deep learning (DL) algorithms have the ability to extract informative features, they require large amounts of sample data. As a result, the design of more interpretable DL models with lower sample demand is highly important. In this study, a novel multi-level output-based deep belief network (DBN-ML) model was developed based on Ziyuan-3 imagery, which was applied for fine classification in an open-pit mine area of Wuhan City. First, the last DBN layer was used to output fine-scale land cover types. Then, one of the front DBN layers outputted the first-level land cover types. The coarse classification was easier and fewer DBN layers were sufficient. Finally, these two losses were weighted to optimize the DBN-ML model. As the first-level class provided a larger amount of additional sample data with no extra cost, the multi-level output strategy enhanced the robustness of the DBN-ML model. The proposed model produces an overall accuracy of 95.10% and an F1-score of 95.07%, outperforming some other models.

scholarly journals Integrating Temporal Evolution with Cellular Automata for Simulating Land Cover Change

2019 ◽  
Vol 11 (3) ◽  
pp. 301 ◽  
Author(s):  
Cangjiao Wang ◽  
Shaogang Lei ◽  
Andrew J. Elmore ◽  
Duo Jia ◽  
Shouguo Mu
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Temporal Evolution ◽  
Null Model ◽  
Simulation Models ◽  
Mining Area ◽  
Temporal Dimension ◽  
Evolution Simulation ◽  
Gradual Evolution ◽  
Ca Model

Simultaneously considering the spatial and temporal processes is essential for land cover simulation models. A cellular automaton (CA) usually simulates the spatial conversion of land cover through post-classification comparisons between the beginning and the end of the training period. However, such an approach does not consider the temporal evolution of land cover. As a result, a CA model fails to explain the realistic land cover change. This paper proposes a temporal-dimension-extension CA (TDE-CA) by integrating the temporal evolution of land cover with a CA. In the TDE-CA, the Breaks for Additive Season and Trend (BFAST) monitor algorithm was employed in the temporal evolution simulation module (TESM) to simulate the gradual evolution of land cover, and an optimized random forest CA (optimized RF-CA) was used to simulate the spatial conversion driven by many spatial variables. Subsequently, the Ensemble Kalman Filter (EnKF) was employed to integrate the TESM with the optimized RF-CA. The TDE-CA was then tested in the land cover simulation of Shendong mining area during the period 2005–2015. The TDE-CA was compared with a Null model, with its sub-models, and with the traditional CA models, including the Logistic-CA and the MLP-CA (Multilayer Perceptron CA) models. The results show that the TDE-CA is superior to the Null model. Furthermore, the overall accuracy and the Kappa coefficient of the TDE-CA were 79.84% and 71.61%, respectively; compared with the TESM and the optimized RF-CA, the values showed 17.14% and 4.48% improvements in the overall accuracies and 0.2167 and 0.0512 improvements in the Kappa coefficients, respectively. When compared with the Logistic-CA and the MLP-CA, we measured 8.41% and 8.25% improvements in the overall accuracies and 0.0985 and 0.0964 improvements in the Kappa coefficients. These experiments indicate that the TDE-CA not only provides an effective model for the spatiotemporal dynamical simulation of land cover, but also enhances the development of the existing simulation theory.

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