Understanding the Correlation between Landscape Pattern and Vertical Urban Volume by Time-Series Remote Sensing Data: A Case Study of Melbourne

Ge, Mengyu; Fang, Shenghui; Gong, Yi; Tao, Pengjie; Guang, Yang; Gong, Wenbing

doi:10.3390/ijgi10010014

Cited by 9 publications

(8 citation statements)

References 75 publications

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“…On the one hand, it can provide insights into how different factors affect landscape patterns, such as landscape composition or spatial arrangement. It can also help researchers determine which environmental factors have the most impact on the formation of landscape patterns, which contributes to effective landscape ecological management and protection work [51]. Correlation analyses between landscape indices and environmental factors were carried out at the grid scale and island scale (Table 3).…”

Section: Grid Scale and Island Scalementioning

confidence: 99%

“…Three-dimensional landscape information and landscape indices provide a more comprehensive understanding of the spatial structure and organization of urban areas, which can better analyze the impact of urban form and buildings on the overall landscape. Furthermore, ecosystem assessments based on the characteristics of three-dimensional landscape elements were carried out [47,51]. Three-dimensional landscape ecological assessment can not only evaluate the urban heat island effect and sunlight distribution but also the visual aesthetics and residents' and tourists' perceptions of urban landscapes.…”

Section: Introductionmentioning

confidence: 99%

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Measuring the Multi-Scale Landscape Pattern of China’s Largest Archipelago from a Dual-3D Perspective Based on Remote Sensing

Qu,

Chi,

Gao

et al. 2023

Remote Sensing

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Measuring the landscape pattern from a three-dimensional perspective is of great significance for comprehensively revealing the complex spatial characteristics of island ecosystems. However, the archipelago composed of rocky islands has received little attention as its three-dimensional landscape characteristics are difficult to quantify. This study took the largest archipelago in China, the Zhoushan Archipelago, as the study area and constructed an island landscape pattern evaluation model from a dual-three-dimensional (dual-3D) perspective. The model divided the island into upper and lower layers, namely the surface landscape based on topography and the landscape elements above the surface (i.e., vegetation and buildings), and then evaluated their landscape patterns from a three-dimensional perspective, respectively. The landscape pattern model based on a dual-3D perspective and multiple scales achieved excellent results in the archipelago. First, the island landscape pattern was evaluated from three-dimensional perspectives, including human interference, landscape fragmentation, vegetation space, and building space. Second, landscape indices such as the human interference three-dimensional index (HITI), the landscape fragmentation three-dimensional index (LFTI), the vegetation three-dimensional index (VTI), and the building three-dimensional index (BTI) established at multiple spatial scales revealed spatial heterogeneity within and between islands. Environmental factors such as elevation, slope, and island area exhibited significant correlations with them. There were significant differences in landscape pattern indices between the two-dimensional (2D) and the three-dimensional (3D) perspectives, and high values were mainly distributed in areas with significant topographic changes and larger islands. In addition, as the evaluation unit increased, the landscape indices increased, and HITI became more responsive to the transitions from 2D to 3D, while LFTI was the opposite. Therefore, the multiscale landscape pattern measurement of China’s largest archipelago based on high-resolution remote sensing was carried out from three-dimensional perspectives to accurately reveal the spatial heterogeneity.

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Section: Grid Scale and Island Scalementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measuring the Multi-Scale Landscape Pattern of China’s Largest Archipelago from a Dual-3D Perspective Based on Remote Sensing

Qu,

Chi,

Gao

et al. 2023

Remote Sensing

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“…Therefore, we believe the proposed variable is expected to be indicative and usable in practice. (2) In this study, the DMSP/OLS night light images, MOD11A2 daytime surface temperature products and MOD13A3 vegetation products were employed to build the fused variable. These data and products are open, and the calculation of NUBAI is relatively simple.…”

Section: Major Findings From the Experimentsmentioning

confidence: 99%

“…China is now in a period of rapid urbanization [1][2][3], which has an impact on the gravity of different human dynamics [4]. Accurate acquisition of urban built-up areas is an important guidance for China's urban construction, management and research.…”

Section: Introductionmentioning

confidence: 99%

A Fused Variable by Night Light Images and MODIS Products for Improving Urban Built-Up Area Extraction

Guang

2021

Technologies

Self Cite

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The boundary of urban built-up areas is the baseline data of a city. Rapid and accurate monitoring of urban built-up areas is the prerequisite for the boundary control and the layout of urban spaces. In recent years, the night light satellite sensors have been employed in urban built-up area extraction. However, the existing extraction methods have not fully considered the properties that directly reflect the urban built-up areas, like the land surface temperature. This research first converted multi-source data into a uniform projection, geographic coordinate system and resampling size. Then, a fused variable that integrated the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) night light images, the Moderate-resolution Imaging Spectroradiometer (MODIS) surface temperature product and the normalized difference vegetation index (NDVI) product was designed to extract the built-up areas. The fusion results showed that the values of the proposed index presented a sharper gradient within a smaller spatial range, compared with the only night light images. The extraction results were tested in both the area sizes and the spatial locations. The proposed index performed better in both accuracies (average error rate 1.10%) and visual perspective. We further discussed the regularity of the optimal thresholds in the final boundary determination. The optimal thresholds of the proposed index were more stable in different cases on the premise of higher accuracies.

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“…To explore the impact of urban vegetation on the regional carbon cycle, it is necessary to accurately estimate the carbon storage of urban vegetation [5,6]. Due to the complexity of landscape types, including buildings, roads, trees, parking lots, water bodies, forests and other green areas, it is undoubtedly challenging to estimate the carbon storage of urban vegetation [7][8][9][10]. Traditionally, plot surveys, biomass conversion factors, process models, the vorticity correlation and covariance method, and remote sensing information models have widely been used for forest carbon storage monitoring [11,12].…”

Section: Introductionmentioning

confidence: 99%

A Novel Spatial Simulation Method for Mapping the Urban Forest Carbon Density in Southern China by the Google Earth Engine

Jiang

Chen

Li³

et al. 2021

Remote Sensing

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Urban forest is an important component of terrestrial ecosystems and is highly related to global climate change. However, because of complex city landscapes, deriving the spatial distribution of urban forest carbon density and conducting accuracy assessments are difficult. This study proposes a novel spatial simulation method, optimized geographically weighted logarithm regression (OGWLR), using Landsat 8 data acquired by the Google Earth Engine (GEE) and field survey data to map the forest carbon density of Shenzhen city in southern China. To verify the effectiveness of the novel method, multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF) and geographically weighted regression (GWR) models were established for comparison. The results showed that OGWLR achieved the highest coefficient of determination (R2 = 0.54) and the lowest root mean square error (RMSE = 13.28 Mg/ha) among all estimation models. In addition, OGWLR achieved a more consistent spatial distribution of carbon density with the actual situation. The carbon density of the forests in the study area was large in the central and western regions and coastal areas and small in the building and road areas. Therefore, this method can provide a new reference for urban forest carbon density estimation and mapping.

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Understanding the Correlation between Landscape Pattern and Vertical Urban Volume by Time-Series Remote Sensing Data: A Case Study of Melbourne

Cited by 9 publications

References 75 publications

Measuring the Multi-Scale Landscape Pattern of China’s Largest Archipelago from a Dual-3D Perspective Based on Remote Sensing

Measuring the Multi-Scale Landscape Pattern of China’s Largest Archipelago from a Dual-3D Perspective Based on Remote Sensing

A Fused Variable by Night Light Images and MODIS Products for Improving Urban Built-Up Area Extraction

A Novel Spatial Simulation Method for Mapping the Urban Forest Carbon Density in Southern China by the Google Earth Engine

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