Land Cover Mapping and Its Spatial Pattern Analysis in Nepal

2017 ◽  
pp. 17-39 ◽  
Author(s):  
Guangbin Lei ◽  
Ainong Li ◽  
Xiaomin Cao ◽  
Wei Zhao ◽  
Jinhu Bian ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Pattern ◽  
Pattern Analysis ◽  
Land Cover Mapping ◽  
Spatial Pattern Analysis

scholarly journals r.pi : A grass gis package for semi‐automatic spatial pattern analysis of remotely sensed land cover data

2017 ◽  
Vol 9 (1) ◽  
pp. 191-199 ◽  
Author(s):  
Martin Wegmann ◽  
Benjamin F. Leutner ◽  
Markus Metz ◽  
Markus Neteler ◽  
Stefan Dech ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Pattern ◽  
Pattern Analysis ◽  
Remotely Sensed ◽  
Spatial Pattern Analysis ◽  
Grass Gis ◽  
Land Cover Data

Spatial pattern analysis in Persian oak (Quercus brantii var. persica) forests on B&W aerial photographs

2008 ◽  
Vol 150 (1-4) ◽  
pp. 251-259 ◽  
Author(s):  
Yousef Erfanifard ◽  
Jahangir Feghhi ◽  
Mahmoud Zobeiri ◽  
Manouchehr Namiranian
Keyword(s):  
Spatial Pattern ◽  
Pattern Analysis ◽  
Aerial Photographs ◽  
Spatial Pattern Analysis ◽  
Quercus Brantii

scholarly journals Spatial Pattern Analysis of the Ecosystem Services in the Guangdong-Hong Kong-Macao Greater Bay Area Using Sentinel-1 and Sentinel-2 Imagery Based on Deep Learning Method

2021 ◽  
Vol 13 (13) ◽  
pp. 7044
Author(s):  
Dawei Wen ◽  
Song Ma ◽  
Anlu Zhang ◽  
Xinli Ke
Keyword(s):  
Hong Kong ◽  
Deep Learning ◽  
Ecosystem Services ◽  
Land Cover ◽  
Spatial Pattern ◽  
Sustainable Urban Development ◽  
Spatial Pattern Analysis ◽  
Bay Area ◽  
Urban Rural ◽  
Sentinel 2

Assessment of ecosystem services supply, demand, and budgets can help to achieve sustainable urban development. The Guangdong-Hong Kong-Macao Greater Bay Area, as one of the most developed megacities in China, sets up a goal of high-quality development while fostering ecosystem services. Therefore, assessing the ecosystem services in this study area is very important to guide further development. However, the spatial pattern of ecosystem services, especially at local scales, is not well understood. Using the available 2017 land cover product, Sentinel-1 SAR and Sentinel-2 optical images, a deep learning land cover mapping framework integrating deep change vector analysis and the ResUnet model was proposed. Based on the produced 10 m land cover map for the year 2020, recent spatial patterns of the ecosystem services at different scales (i.e., the GBA, 11 cities, urban–rural gradient, and pixel) were analyzed. The results showed that: (1) Forest was the primary land cover in Guangzhou, Huizhou, Shenzhen, Zhuhai, Jiangmen, Zhaoqing, and Hong Kong, and an impervious surface was the main land cover in the other four cities. (2) Although ecosystem services in the GBA were sufficient to meet their demand, there was undersupply for all the three general services in Macao and for the provision services in Zhongshan, Dongguan, Shenzhen, and Foshan. (3) Along the urban–rural gradient in the GBA, supply and demand capacity showed an increasing and decreasing trend, respectively. As for the city-level analysis, Huizhou and Zhuhai showed a fluctuation pattern while Jiangmen, Zhaoqing, and Hong Kong presented a decreasing pattern along the gradient. (4) Inclusion of neighborhood landscape led to increased demand scores in a small proportion of impervious areas and oversupply for a very large percent of bare land.

scholarly journals Spatio-Temporal Super-Resolution Land Cover Mapping Based on Fuzzy C-Means Clustering

2018 ◽  
Vol 10 (8) ◽  
pp. 1212 ◽  
Author(s):  
Xiaohong Yang ◽  
Zhong Xie ◽  
Feng Ling ◽  
Xiaodong Li ◽  
Yihang Zhang ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Pattern ◽  
Spatial Resolution ◽  
Super Resolution ◽  
Land Cover Mapping ◽  
Fuzzy C Means Clustering ◽  
Land Cover Map ◽  
Spatio Temporal ◽  
Fine Resolution ◽  
Land Cover Maps

Super-resolution land cover mapping (SRM) is a method that aims to generate land cover maps with fine spatial resolutions from the original coarse spatial resolution remotely sensed image. The accuracy of the resultant land cover map produced by existing SRM methods is often limited by the errors of fraction images and the uncertainty of spatial pattern models. To address these limitations in this study, we proposed a fuzzy c-means clustering (FCM)-based spatio-temporal SRM (FCM_STSRM) model that combines the spectral, spatial, and temporal information into a single objective function. The spectral term is constructed with the FCM criterion, the spatial term is constructed with the maximal spatial dependence principle, and the temporal term is characterized by the land cover transition probabilities in the bitemporal land cover maps. The performance of the proposed FCM_STSRM method is assessed using data simulated from the National Land Cover Database dataset and real Landsat images. Results of the two experiments show that the proposed FCM_STSRM method can decrease the influence of fraction errors by directly using the original images as the input and the spatial pattern uncertainty by inheriting land cover information from the existing fine resolution land cover map. Compared with the hard classification and FCM_SRM method applied to mono-temporal images, the proposed FCM_STSRM method produced fine resolution land cover maps with high accuracy, thus showing the efficiency and potential of the novel approach for producing fine spatial resolution maps from coarse resolution remotely sensed images.

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