Using Satellite Image Fusion to Evaluate the Impact of Land Use Changes on Ecosystem Services and Their Economic Values

Wang, Shuangao; Padmanaban, Rajchandar; Mbanze, Aires Afonso; Silva, João M. N.; Shamsudeen, Mohamed; Cabral, Pedro; Campos, Felipe S.

doi:10.3390/rs13050851

Cited by 19 publications

(10 citation statements)

References 60 publications

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“…Among them, the selected indices at the type level were as follows: the patch density (PD), average patch area (AREA_MN), maximum patch index (LPI), cohesion index (COHESION), and landscape shape index (LSI). At the landscape level, the selected indices were as follows: the sprawl index (CONTAG), Shannon diversity index (SHDI), and Shannon evenness index (SHEI) (Shuangao, Padmanaban, et al, 2021;Yang, 2021). Then, we used the moving window method to calculate the local-level landscape pattern index (Hagen-Zanker, 2016), and we finally obtained its spatial distribution.…”

Section: Landscape Pattern Index Methodsmentioning

confidence: 99%

The Effects of Urban Landscape Pattern Evolution on Habitat Quality Based on InVEST Model

Guo

Lyu

Feng

2023

IRSPSD International

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With the acceleration of urbanization, urban landscape patterns have been rapidly adjusted and reorganized, which has resulted in drastic changes in the quality of urban habitats. To achieve the objective of a new type of ecoconservative urbanization, it is urgent that we strengthen the research on the relationship between the habitat quality and landscape patterns. Based on the InVEST model and FRAGSTATS landscape pattern index, in this study, we evaluated the habitat quality evolution and landscape pattern change trend of Harbin based on remote sensing images from 2000, 2010, and 2020, and we explored the correlation between them using Pearson's correlation coefficient. The results showed that: (1) From 2000 to 2020, the habitat quality in Harbin decreased from 0.68 to 0.65, with an overall downward trend. The low-quality habitat increased by 1.25%, while the high-quality habitat decreased by 6.43%. The spatial heterogeneity was substantial, and the distribution pattern was high in the middle, middle in the east, and low in the west; (2) The landscape pattern index of Harbin obviously changed, and the overall landscape pattern had a trend of further fragmentation and gradual irregularity, decreases in the diversity and evenness, and spatial heterogeneity; (3) For most of the land types, there were substantial correlations between the habitat quality and landscape pattern indices. The PD, COHESION, and LSI had the most substantial correlations with the habitat quality. The research results provide a theoretical basis for the ecological protection and sustainable development in Harbin.

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Section: Landscape Pattern Index Methodsmentioning

confidence: 99%

The Effects of Urban Landscape Pattern Evolution on Habitat Quality Based on InVEST Model

Guo

Lyu

Feng

2023

IRSPSD International

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“…The landscape index condenses a large amount of landscape pattern information that reflects the characteristics of spatial configuration and structural composition ( Guo et al., 2021 ; Shuangao et al., 2021 ; Yang, 2021 ). In striving to choose elements that are representative, simplified and common ( Mengist et al., 2021 ), and by referring to relevant research results ( Guo et al., 2021 ; Yang, 2021 ) and combining the actual situation of the study area, we choose aggregation index (AI), area-weighted mean fractal dimension index (FRAC_AM), mean patch area (AREA_MN), number of patches (NP), largest patch index (LPI), and percentage of landscape (PLAND).…”

Section: Methodsmentioning

confidence: 99%

The effects of landscape change on habitat quality in arid desert areas based on future scenarios: Tarim River Basin as a case study

Zhang

Chen

2022

Front. Plant Sci.

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Human activities have caused spatiotemporal patterns of land use and land cover (LULC) change. The LULC change has directly affected habitat quality (HQ) and ecosystem functions. Assessing, simulating, and predicting spatiotemporal changes and future trends under different scenarios of LULC-influenced HQ is beneficial to land use planners and decision-makers, helping them to formulate plans in a sustainable and responsible way. This study assesses and simulates the HQ of the Tarim River Basin (TRB) using the future land use simulation model (FLUS), the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, and partial least squares regression (PLSR). Since 2000, the TRB has experienced a declining trend in HQ from 0.449 to 0.444, especially in the lower elevations (740-2000m) and on sloped land (<10°). The decline will continue unless effective and sustainable plans are implemented to halt it. Agricultural and settlement areas have a lower HQ and a higher degree of habitat degradation than native habitats. This shows that the expansion of oasis agriculture (with an annual growth rate of 372.17 km2) and settlements (with an annual growth rate of 23.50 km2) has caused a decline in native habitat and subsequent habitat fragmentation. In other words, changes in LULC have caused a decline in the HQ. Moreover, there is a significant negative correlation between HQ and urbanization rate (p<0.01), and the PLSR also indicate that number of patches (NP), area-weighted mean fractal dimension index (FRAC_AM), percentage of landscape (PLAND), and largest patch index (LPI) were also important contributors to worsening the HQ. Therefore, the TRB urgently needs appropriate strategies to preserve its natural habitats into the future, based on the ecological priority scenario (EPS) and harmonious development scenario (HDS), which can help to maintain a high-quality habitat.

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“…The crucial problem is to attain a fused image with enhanced target regions and suppressed backgrounds from the spaceborne and airborne SAR images, in order to increase the target-to-clutter ratio (TCR) and improve the vessel target detection performance. Considering the issue of image fusion, some works have been carried out in the existing literature [10][11][12][13][14][15][16][17][18][19][20][21][22][23]. The most straightforward and computationally efficient fusion methods are arithmetic-based fusion approaches, such as addition fusion and multiplication fusion, but this class of fusion approaches might maintain a high level of background clutter, resulting in false alarms, or excessively suppress relatively weak targets, resulting in missed detections [10].…”

Section: Related Workmentioning

confidence: 99%

Vessel Target Detection in Spaceborne–Airborne Collaborative SAR Images via Proposal and Polarization Fusion

et al. 2021

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This paper focuses on vessel detection through the fusion of synthetic aperture radar (SAR) images acquired from spaceborne–airborne collaborative observations. The vessel target detection task becomes more challenging when it features inshore interferences and structured and shaped targets. We propose a new method, based on target proposal and polarization information exploitation (TPPIE), to fuse the spaceborne–airborne collaborative SAR images for accurate vessel detection. First, a new triple-state proposal matrix (TSPM) is generated by combining the normed gradient-based target proposal and the edge-based morphological candidate map. The TSPM can be used to extract the potential target regions, as well as filtering out the sea clutter and inshore interference regions. Second, we present a new polarization feature, named the absolute polarization ratio (APR), to exploit the intensity information of dual-polarization SAR images. In the APR map, the vessel target regions are further enhanced. Third, the final fused image with enhanced targets and suppressed backgrounds (i.e., improved target-to-clutter ratio; TCR) is attained by taking the Hadamard product of the intersected TSPM from multiple sources and the composite map exploiting the APR feature. Experimental analyses using Gaofen-3 satellite and unmanned aerial vehicle (UAV) SAR imagery indicate that the proposed TPPIE fusion method can yield higher TCRs for fused images and better detection performance for vessel targets, compared to commonly used image fusion approaches.

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Using Satellite Image Fusion to Evaluate the Impact of Land Use Changes on Ecosystem Services and Their Economic Values

Cited by 19 publications

References 60 publications

The Effects of Urban Landscape Pattern Evolution on Habitat Quality Based on InVEST Model

The Effects of Urban Landscape Pattern Evolution on Habitat Quality Based on InVEST Model

The effects of landscape change on habitat quality in arid desert areas based on future scenarios: Tarim River Basin as a case study

Vessel Target Detection in Spaceborne–Airborne Collaborative SAR Images via Proposal and Polarization Fusion

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