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 Urbanization Impacts on Natural Habitat and Ecosystem Services in the Guangdong-Hong Kong-Macao “Megacity”

2020 ◽  
Vol 12 (16) ◽  
pp. 6675
Author(s):  
Wenjing Wang ◽  
Tong Wu ◽  
Yuanzheng Li ◽  
Shilin Xie ◽  
Baolong Han ◽  
...  
Keyword(s):  
Hong Kong ◽  
Ecosystem Services ◽  
Rural Areas ◽  
Natural Habitat ◽  
Bay Area ◽  
Supply And Distribution ◽  
General Decline ◽  
Urban Rural ◽  
Population Aggregation ◽  
Area Expansion

The population aggregation and built-up area expansion caused by urbanization can have significant impacts on the supply and distribution of crucial ecosystem services. The correlation between urbanization and ecosystem services has been well-studied, but additional research is needed to better understand the spatiotemporal interactions between ecosystem services and urbanization processes in highly urbanized areas as well as surrounding rural areas. In this paper, the relationships of urbanization with natural habitat and three key regulating ecosystem services—water retention, soil conservation, and carbon sequestration, were quantified and mapped for the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), a rapidly developing urban agglomeration of over 70 million people, for the period of 2000–2018. Our results showed that urbanization caused a general decline in ecosystem services, and urbanization and ecosystem services exhibited a negative spatial correlation. However, this relationship varied along urban-rural gradients and weak decoupling was the overall trend during the course of the study period, indicating a greater need for the protection and improvement of ecosystem services. Our results provide instructive insights for new urbanization planning to maintain regional ecosystem services and sustainable development in the GBA and other large, rapidly urbanized agglomerations.

scholarly journals Ecosystem Services Changes on Farmland in Response to Urbanization in the Guangdong–Hong Kong–Macao Greater Bay Area of China

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 501
Author(s):  
Xuege Wang ◽  
Fengqin Yan ◽  
Yinwei Zeng ◽  
Ming Chen ◽  
Bin He ◽  
...  
Keyword(s):  
Land Use ◽  
Hong Kong ◽  
Ecosystem Services ◽  
Ecosystem Service ◽  
Food Production ◽  
Great Loss ◽  
The Past ◽  
Bay Area ◽  
Farmland Loss ◽  
The World

Extensive urbanization around the world has caused a great loss of farmland, which significantly impacts the ecosystem services provided by farmland. This study investigated the farmland loss due to urbanization in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) of China from 1980 to 2018 based on multiperiod datasets from the Land Use and Land Cover of China databases. Then, we calculated ecosystem service values (ESVs) of farmland using valuation methods to estimate the ecosystem service variations caused by urbanization in the study area. The results showed that 3711.3 km2 of farmland disappeared because of urbanization, and paddy fields suffered much higher losses than dry farmland. Most of the farmland was converted to urban residential land from 1980 to 2018. In the past 38 years, the ESV of farmland decreased by 5036.7 million yuan due to urbanization, with the highest loss of 2177.5 million yuan from 2000–2010. The hydrological regulation, food production and gas regulation of farmland decreased the most due to urbanization. The top five cities that had the largest total ESV loss of farmland caused by urbanization were Guangzhou, Dongguan, Foshan, Shenzhen and Huizhou. This study revealed that urbanization has increasingly become the dominant reason for farmland loss in the GBA. Our study suggests that governments should increase the construction of ecological cities and attractive countryside to protect farmland and improve the regional ESV.

scholarly journals Study on the Spatial Pattern of Innovation Output and Influencing Factors in Guangdong-Hong Kong-Macao Greater Bay Area

2019 ◽  
Author(s):  
Dongliang Yang ◽  
Chunfeng Li
Keyword(s):  
Hong Kong ◽  
Spatial Pattern ◽  
Industrial Structure ◽  
Study Region ◽  
Bay Area ◽  
Innovation Activities ◽  
Level Of Economic Development ◽  
Innovation Output ◽  
Countermeasures And Suggestions ◽  
The Impact

The advantageous location, port clusters, strong economic strength, developed financial system, rational and orderly urban division of labor and modern industrial system of Guangdong-Hong Kong-Macao greater bay area provide sustainable driving force for innovation activities in this region. This paper selected the Gini-coefficient, first degree index and concentration index to measure the spatial pattern characteristics of innovation output in Guangdong-Hong Kong-Macao greater bay area. The results show that the innovation output presented a spatial pattern of center-periphery in the study region with Shenzhen and Guangzhou as the dual centers and engines of innovation and Dongguan and Foshan as the main innovative areas. Further empirical analysis of the impact of various factors on innovation output in the study region found that R&D expenditure, the number of R&D personnel, the level of economic development and industrial structure all have significant promoting effects on innovation output. Accordingly, this paper put forward countermeasures and suggestions to promote the innovative development of Guangdong-Hong Kong-Macao greater bay area and build a world-class scientific and technological innovation bay area.

scholarly journals CAN SPOT-6/7 CNN SEMANTIC SEGMENTATION IMPROVE SENTINEL-2 BASED LAND COVER PRODUCTS? SENSOR ASSESSMENT AND FUSION

2020 ◽  
Vol V-2-2020 ◽  
pp. 557-564
Author(s):  
O. Stocker ◽  
A. Le Bris
Keyword(s):  
Deep Learning ◽  
Land Cover ◽  
Data Fusion ◽  
Semantic Segmentation ◽  
Fine Grained ◽  
Satellite Sensors ◽  
Fusion Framework ◽  
Sentinel 2

Abstract. Needs for fine-grained, accurate and up-to-date land cover (LC) data are important to answer both societal and scientific purposes. Several automatic products have already been proposed, but are mostly generated out of satellite sensors like Sentinel-2 (S2) or Landsat. Metric sensors, e.g. SPOT-6/7, have been less considered, while they enable (at least annual) acquisitions at country scale and can now be efficiently processed thanks to deep learning (DL) approaches. This study thus aimed at assessing whether such sensor can improve such land cover products. A custom simple yet effective U-net - Deconv-Net inspired DL architecture is developed and applied to SPOT-6/7 and S2 for different LC nomenclatures, aiming at comparing the relevance of their spatial/spectral configurations and investigating their complementarity. The proposed DL architecture is then extended to data fusion and applied to previous sensors. At the end, the proposed fusion framework is used to enrich an existing S2 based LC product, as it is generic enough to cope with fusion at distinct levels.

Global land use / land cover with Sentinel 2 and deep learning

2021 ◽  
Author(s):  
Krishna Karra ◽  
Caitlin Kontgis ◽  
Zoe Statman-Weil ◽  
Joseph C. Mazzariello ◽  
Mark Mathis ◽  
...  
Keyword(s):  
Land Use ◽  
Deep Learning ◽  
Land Cover ◽  
Land Use Land Cover ◽  
Global Land ◽  
Sentinel 2

scholarly journals Assessing the Effects of Land Cover Land Use Change on Precipitation Dynamics in Guangdong–Hong Kong–Macao Greater Bay Area from 2001 to 2019

2021 ◽  
Vol 13 (6) ◽  
pp. 1135
Author(s):  
Xinghan Wang ◽  
Peitong Cong ◽  
Yuhao Jin ◽  
Xichun Jia ◽  
Junshu Wang ◽  
...  
Keyword(s):  
Land Use ◽  
Spatial Distribution ◽  
Hong Kong ◽  
Land Use Change ◽  
Land Cover ◽  
Temporal Distribution ◽  
Water Security ◽  
Spatial And Temporal Distribution ◽  
Construction Land ◽  
Bay Area

The change of spatial and temporal distribution of precipitation has an important impact on urban water security. The effect of land cover land use change (LCLUC) on the spatial and temporal distribution of precipitation needs to be further studied. In this study, transfer matrix, standard deviation ellipse and spatial autocorrelation analysis techniques were used. Based on the data of land cover land use and precipitation, this paper analyzed the land cover land use change and its influence on the spatial and temporal distribution pattern of precipitation in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). The results showed that from 2001 to 2019, the area of cropland, water, barren, forest/grassland in the GBA decreased by 44.03%, 8.05%, 50.22%, 0.43%, respectively, and the area of construction land increased by 20.05%. The precipitation in the GBA was mainly concentrated in spring and summer, and the precipitation in spring tended to increase gradually, while the precipitation in summer tended to decrease gradually, while the precipitation in autumn and winter has no obvious change. It was found that with the change of land cover land use, the spatial distribution of precipitation also changed. Especially in the areas where the change of construction land was concentrated, the spatial distribution of precipitation changed most obviously.

scholarly journals Deep Learning for Land Cover Change Detection

2020 ◽  
Vol 13 (1) ◽  
pp. 78
Author(s):  
Oliver Sefrin ◽  
Felix M. Riese ◽  
Sina Keller
Keyword(s):  
Deep Learning ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Short Term Memory ◽  
Image Sequences ◽  
Land Cover Changes ◽  
Water Classification ◽  
Land Cover Change Detection ◽  
Sentinel 2

Land cover and its change are crucial for many environmental applications. This study focuses on the land cover classification and change detection with multitemporal and multispectral Sentinel-2 satellite data. To address the challenging land cover change detection task, we rely on two different deep learning architectures and selected pre-processing steps. For example, we define an excluded class and deal with temporal water shoreline changes in the pre-processing. We employ a fully convolutional neural network (FCN), and we combine the FCN with long short-term memory (LSTM) networks. The FCN can only handle monotemporal input data, while the FCN combined with LSTM can use sequential information (multitemporal). Besides, we provided fixed and variable sequences as training sequences for the combined FCN and LSTM approach. The former refers to using six defined satellite images, while the latter consists of image sequences from an extended training pool of ten images. Further, we propose measures for the robustness concerning the selection of Sentinel-2 image data as evaluation metrics. We can distinguish between actual land cover changes and misclassifications of the deep learning approaches with these metrics. According to the provided metrics, both multitemporal LSTM approaches outperform the monotemporal FCN approach, about 3 to 5 percentage points (p.p.). The LSTM approach trained on the variable sequences detects 3 p.p. more land cover changes than the LSTM approach trained on the fixed sequences. Besides, applying our selected pre-processing improves the water classification and avoids reducing the dataset effectively by 17.6%. The presented LSTM approaches can be modified to provide applicability for a variable number of image sequences since we published the code of the deep learning models. The Sentinel-2 data and the ground truth are also freely available.

scholarly journals Matching Ecosystem Services Supply and Demand through Land Use Optimization: A Study of the Guangdong-Hong Kong-Macao Megacity

2021 ◽  
Vol 18 (5) ◽  
pp. 2324
Author(s):  
Wenjing Wang ◽  
Tong Wu ◽  
Yuanzheng Li ◽  
Hua Zheng ◽  
Zhiyun Ouyang
Keyword(s):  
Land Use ◽  
Hong Kong ◽  
Ecosystem Services ◽  
Supply And Demand ◽  
Flood Damage ◽  
Living Environment ◽  
Flood Mitigation ◽  
Bay Area ◽  
Land Use Allocation ◽  
Land Use Optimization

Shortfalls and mismatches between the supply and demand of ecosystem services (ES) can be detrimental to human wellbeing. Studies focused on these problems have increased in recent decades, but few have applied land use optimization to reduce such spatial mismatches. This study developed a methodology to identify ES mismatches and then use these mismatches as objectives for land use optimization. The methodology was applied to the Guangdong-Hong Kong-Macao “Greater Bay Area” (GBA), a megacity of over 70 million people and one of the world’s largest urban agglomerations. Considering the demand for a healthy and secure living environment among city-dwellers, we focused on three ES: heat mitigation, flood mitigation, and recreational services. The results showed large spatial heterogeneity in supply and demand for these three ES. However, compared to current conditions in the GBA, our model showed that optimized land use allocation could better match the supply and demand for heat mitigation (number of beneficiaries increased by 15%), flood mitigation (amount of population exposed to flood damage decreased by 37%), and recreation (number of beneficiaries increased by 14%). By integrating land use allocation and spatial mismatch analysis, this methodology provides a feasible way to align ES supply and demand to advance urban and regional sustainability.

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