Land Cover Change Detection in Urban Lake Areas Using Multi-Temporary Very High Spatial Resolution Aerial Images

Zhang, Wenyuan; Tan, Guoxin; Zheng, S.; Sun, Chuanming; Kong, X.; Liu, Zhaobin

doi:10.3390/w10020001

Cited by 59 publications

(3 citation statements)

References 29 publications

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“…With the development of remote sensing and geographic information technology, it becomes easier to characterize landscapes and quantify their structural changes. Remote sensing data has become the most important data source due to its large area coverage, high accuracy, and timeliness [28]. Landscape index analysis is widely used to analyze land use change characteristics that cannot be observed with the naked eye, and measure the specific spatial characteristics of patches, patch types, or the whole landscape from the perspectives of area, shape, aggregation, and diversity [29,30].…”

Section: Introductionmentioning

confidence: 99%

Interaction Analysis of Urban Blue-Green Space and Built-Up Area Based on Coupling Model—A Case Study of Wuhan Central City

Wu¹,

Shen²,

Zhang³

2020

Water

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Urban “blue-green” space is the basic element of urban natural ecological space. The rapid urbanization process has a significant impact on the pattern of urban blue-green space and seriously threatens the urban ecological environment. It is of great significance for urban ecological protection and sustainable development to study the change in characteristics of blue and green space during urban expansion. In this paper, a comprehensive method of remote sensing image, landscape pattern analysis, sector analysis, and coupling analysis is applied to analyze the spatio-temporal evolution characteristics and coordinated development level of the blue-green space and built-up area in central Wuhan from 1987 to 2018. The results showed that the land use and landscape pattern in the central city of Wuhan underwent a drastic change process, and the dominant change was the occupation of blue-green space by urban built-up areas, which led to the increase of landscape fragmentation and the decrease of heterogeneity. Urban green space is gradually marginalized and concentrated in local areas. The blue space has an obvious blocking effect on the expansion of the built-up area, and its spatial pattern tends to be stable gradually. In addition, the coupling degree between urban blue-green space and urban built area presents an inverted U-shape, and the coordinated development level among the three has entered a stage of moderate imbalance. It is urgent to strengthen the protection of blue-green space, curb the disorderly expansion of cities, and improve the quality of urban development.

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Section: Introductionmentioning

confidence: 99%

Interaction Analysis of Urban Blue-Green Space and Built-Up Area Based on Coupling Model—A Case Study of Wuhan Central City

Wu¹,

Shen²,

Zhang³

2020

Water

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“…For example, it has been successfully used for multispectral imagery classification (Abdel-Hamid, Dubovyk, El-Magd & Menz., 2018). CART model representation is a binary tree, in which each node of the decision tree structure makes a binary decision that separates either one or various classes from the remaining ones (Zhang et al, 2018). Creating a CART model involves selecting input variables and splitting points on those variables until a suitable tree is constructed (Zhang et al, 2018).…”

Section: Feature Collectionmentioning

confidence: 99%

Oil Palm Plantation Detection in Indonesia Using Sentinel-2 and Landsat-8 Optical Satellite Imagery (Case Study: Rokan Hulu Regency, Riau Province)

Nurmasari¹,

Wijayanto²

2021

IJReSES

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The objective of this work is to assess the capability of multispectral optical Landsat and Sentinel images to detect oil palm plantations in Rokan Hulu, Riau, one of the largest palm oil producers in Indonesia, by combining multispectral bands and composite indices. In addition to comparing two different sets of satellite images, we also ascertain which gives the best performance among the supervised machine learning classifiers CART Decision Tree, Random Forest, Support Vector Machine, and Naive Bayes. With the use of multispectral bands and derived composite indices, the best classifier achieved an overall accuracy of up to 92%. The findings and contributions of the study include: (1) insight into a set of feature combinations that provides the highest model accuracy, and (2) an extensive evaluation of machine learning-based classifiers on two different optical satellite imageries. Our study could further be beneficial for the government in providing more scalable plantation statistics.

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“…LCCD plays an important role in large-scale land use analysis [7][8][9], environment monitoring evaluation [10,11], natural hazard assessment [12][13][14], and natural resource inventory [15]. However, issues such as "salt-and-pepper" noise in the detection results, especially for VHR remote sensing images [16][17][18], pose a challenge in the practical applications of LCCD with remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Spatial–Spectral Feature Fusion Coupled with Multi-Scale Segmentation Voting Decision for Detecting Land Cover Change with VHR Remote Sensing Images

Zheng

Cao

et al. 2019

Remote Sensing

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In this article, a novel approach for land cover change detection (LCCD) using very high resolution (VHR) remote sensing images based on spatial–spectral feature fusion and multi-scale segmentation voting decision is proposed. Unlike other traditional methods that have used a single feature without post-processing on a raw detection map, the proposed approach uses spatial–spectral features and post-processing strategies to improve detecting accuracies and performance. Our proposed approach involved two stages. First, we explored the spatial features of the VHR remote sensing image to complement the insufficiency of the spectral feature, and then fused the spatial–spectral features with different strategies. Next, the Manhattan distance between the corresponding spatial–spectral feature vectors of the bi-temporal images was employed to measure the change magnitude between the bi-temporal images and generate a change magnitude image (CMI). Second, the use of the Otsu binary threshold algorithm was proposed to divide the CMI into a binary change detection map (BCDM) and a multi-scale segmentation voting decision algorithm to fuse the initial BCDMs as the final change detection map was proposed. Experiments were carried out on three pairs of bi-temporal remote sensing images with VHR remote sensing images. The results were compared with those of the state-of-the-art methods including four popular contextual-based LCCD methods and three post-processing LCCD methods. Experimental comparisons demonstrated that the proposed approach had an advantage over other state-of-the-art techniques in terms of detection accuracies and performance.

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Land Cover Change Detection in Urban Lake Areas Using Multi-Temporary Very High Spatial Resolution Aerial Images

Cited by 59 publications

References 29 publications

Interaction Analysis of Urban Blue-Green Space and Built-Up Area Based on Coupling Model—A Case Study of Wuhan Central City

Interaction Analysis of Urban Blue-Green Space and Built-Up Area Based on Coupling Model—A Case Study of Wuhan Central City

Oil Palm Plantation Detection in Indonesia Using Sentinel-2 and Landsat-8 Optical Satellite Imagery (Case Study: Rokan Hulu Regency, Riau Province)

Spatial–Spectral Feature Fusion Coupled with Multi-Scale Segmentation Voting Decision for Detecting Land Cover Change with VHR Remote Sensing Images

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