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Spatial Analysis and Land Use Planning for Sustainable Ecosystem

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainability in Geographic Science".

Deadline for manuscript submissions: 30 May 2024 | Viewed by 14262

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Special Issue Editors

Prof. Dr. Xinchang Zhang

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Guest Editor

School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Interests: deep learning of remote sensing; natural resource identification; digital city (smart city) theory and method

Dr. Ying Sun

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Guest Editor

School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
Interests: urban and vegetation remote sensing; artificial intelligence; remote sensing spatio-temporal data fusion and mining

Dr. Yongjian Ruan

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Guest Editor

School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Interests: ecological remote sensing; deep learning for extracting remote sensing information; vegetation phenology and ice phenology

Special Issue Information

Dear Colleagues,

The sustainability of an ecosystem is a key prerequisite for human survival and development. With the development of science and technology, the ability of human beings to transform the ecosystem is becoming stronger than ever, and the relationship between human beings and ecosystems has entered a new epoch. Human beings have conducted various disturbances and transformations on the ecosystem for various purposes, e.g., to obtain the necessary survival resources, develop the economy, develop industrialization and urbanization, improve the living environment, etc. Some behaviors of human beings affected by ecosystems are as follows: emiting a large volume of carbon dioxide and other greenhouse gases into the atmosphere; changing the original land use/cover; felling of trees and large-scale planting of economic forests, triggering changes in the original ecological community; destroying wetlands and grassland, etc. These behaviors profoundly affect the earth's ecosystem and are changing our living environment. The sustainable ecosystem is facing major challenges. We sincerely invite you to participate in this Special Issue on “Spatial Analysis and Land Use Planning for Sustainable Ecosystem”. The involved topics include (but are not be limited to) the following:

Terrestrial ecosystems and the net primary productivity of vegetation;
Land surface/vegatation phenology dynamic monitoring;
The impact of changing human activities on ecological ecosystems;
Land use/cover imformation extracting based on remote sensing;
Machine/deep learning algorithm for remote sensing applications;
Land degradation and rehabilitation based on intelligent geo-computing;
The cumulative impacts of human pressures on the land environment.

Prof. Dr. Xinchang Zhang
Dr. Ying Sun
Dr. Yongjian Ruan
Guest Editors

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Keywords

terrestrial ecosystem
land surface monitoring
land use/cover
machine/deep learning based ecological application
net primary productivity of vegetation
vegetation phenology and vegetation dynamics

Published Papers (8 papers)

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Research

24 pages, 5560 KiB

Open AccessArticle

Integrating Meteorological and Remote Sensing Data to Simulate Cropland Nocturnal Evapotranspiration Using Machine Learning

by Jiaojiao Huang, Sha Zhang, Jiahua Zhang, Xin Zheng, Xianye Meng, Shanshan Yang and Yun Bai

Sustainability 2024, 16(5), 1987; https://0-doi-org.brum.beds.ac.uk/10.3390/su16051987 - 28 Feb 2024

Viewed by 571

Abstract

Evapotranspiration (ET) represents a significant component of the global water flux cycle, yet nocturnal evapotranspiration (ETn) is often neglected, leading to underestimation of global evapotranspiration. As for cropland, accurate modeling of ETn is essential for rational water management and is important for sustainable agriculture development. We used random forest (RF) to simulate ETn at 16 globally distributed cropland eddy covariance flux sites along with remote sensing and meteorological factors. The recursive feature elimination method was used to remove unimportant variables. We also simulated the ETn of C₃ and C₄ crops separately. The trained RF resulted in a determination coefficient (R²) (root mean square error (RMSE)) of 0.82 (7.30 W m⁻²) on the testing dataset. C₃ and C₄ crops on the testing dataset resulted in an R² (RMSE) of 0.86 (5.59 W m⁻²) and 0.55 (4.86 W m⁻²) for the two types of crops. We also showed that net radiation is the dominant factor in regulating ETn, followed by 2 m horizontal wind speed and vapor pressure deficit (VPD), and these three meteorological factors showed a significant positive correlation with ETn. This research demonstrates that RF can simulate ETn from crops economically and accurately, providing a methodological basis for improving global ETn simulations. Full article

(This article belongs to the Special Issue Spatial Analysis and Land Use Planning for Sustainable Ecosystem)

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21 pages, 6240 KiB

Open AccessArticle

Spatio-Temporal Heterogeneity of the Ecological Environment and Its Response to Land Use Change in the Chushandian Reservoir Basin

by Yichen Fang, Lianhai Cao, Xinyu Guo, Tong Liang, Jiyin Wang, Ning Wang and Yue Chao

Sustainability 2024, 16(4), 1385; https://0-doi-org.brum.beds.ac.uk/10.3390/su16041385 - 6 Feb 2024

Viewed by 545

Abstract

Conducting ecological monitoring assessments and revealing the effects of driving factors are crucial for enhancing ecological safety and promoting sustainable development. Taking the Chushandian Reservoir basin as the research object, this paper employed the Remote Sensing Ecological Index (RSEI), constructed based on remote sensing data, to monitor and assess the ecological environment of the study area from 1990 to 2021, and predicted its future development trend through the Hurst index. On this basis, we integrated land use data to elucidate the response of the ecological environment to human activities. The results show that: (1) The mutation test indicates that selecting 1990, 2004, 2008, 2013, and 2021 as the study time nodes can comprehensively reflect the spatio-temporal information regarding changes in ecological quality in the study area. Specifically, both 1990 and 2021 exhibit higher ecological quality ratings, while 2008 has the lowest ecological quality rating. The spatial distribution of ecological quality is strongly clustered, with high–high clustering and low–low clustering dominating. (2) The overall trend of ecological quality in the study area appears in a pattern of initial decline followed by subsequent improvement. From 1990 to 2004, the degraded area constituted the largest proportion, accounting for 87.82%. After 2008, the quality of the ecological environment began to rebound. Between 2008 and 2013, the proportion of regions with improved ecological conditions was 57.91%, and from 2013 to 2021, 46.74% of the regions showed improvement. (3) In the research area, 36.70% of the regions exhibit a trend of sustainable stability into the future, representing the highest proportion. Approximately 34.3% of the areas demonstrate a trend of sustainable improvement, while the regions exhibiting sustainable degradation account for only 5.72%. While the ecological environment is demonstrating a positive overall developmental trend, it is crucial to stay vigilant regarding areas of ongoing degradation and implement appropriate protective measures. (4) Land use change significantly impacts the ecological environment, with the expansion of land for urban build up causing some ecological deterioration, while the later expansion of forest improves ecological quality. The results provide theoretical approaches and a foundation for decision-making in the ecological management of the Chushandian Reservoir basin. Full article

(This article belongs to the Special Issue Spatial Analysis and Land Use Planning for Sustainable Ecosystem)

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26 pages, 11981 KiB

Open AccessArticle

Analysis of Construction Networks and Structural Characteristics of Pearl River Delta and Surrounding Cities Based on Multiple Connections

by Shengdong Nie and Hengkai Li

Sustainability 2023, 15(14), 10917; https://0-doi-org.brum.beds.ac.uk/10.3390/su151410917 - 12 Jul 2023

Viewed by 881

Abstract

The Pearl River Delta (PRD) is one of three world-class city clusters in China, which is important for the strategical deployment of the national “Belt and Road”. Based on nighttime lighting data, Baidu index, and train stopping times, this study analyzed the network of spatial patterns and structural evolution of the PRD and surrounding cities via social network analysis and dynamic network visualization, providing new perspectives and ideas for the study of intercity linkages and urban networks. The results provide decision support to the government for urban cluster planning. From 2014 to 2020, the economic network evolved from a uniaxial structure to an “inverted V” structure. The transportation network evolved from a uniaxial structure to a “△” structure. The information network did not show any obvious structural changes during its development, except for a star-shaped radial structure. The PRD city cluster and its surrounding cities exhibited a spatially non-uniform distribution in terms of spatial connections. The total connections between Guangzhou and Foshan and the surrounding cities in terms of economic, transportation, and information functions account for 30%, 28%, and 10% of the total urban connections, respectively. The graph entropy growth rates of the PRD city cluster and surrounding cities in economic, transportation, and information networks from 2014 to 2020 were 39.9%, 115.4%, and 5.1%, respectively. The network structures of economic and transportation networks stabilized eventually. The information network structures are stable. Full article

(This article belongs to the Special Issue Spatial Analysis and Land Use Planning for Sustainable Ecosystem)

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21 pages, 15558 KiB

Open AccessArticle

A Deep Learning-Based Approach to Predict Large-Scale Dynamics of Normalized Difference Vegetation Index for the Monitoring of Vegetation Activities and Stresses Using Meteorological Data

by Ying Sun, Dazhao Lao, Yongjian Ruan, Chen Huang and Qinchuan Xin

Sustainability 2023, 15(8), 6632; https://0-doi-org.brum.beds.ac.uk/10.3390/su15086632 - 13 Apr 2023

Cited by 4 | Viewed by 2129

Abstract

Vegetation activities and stresses are crucial for vegetation health assessment. Changes in an environment such as drought do not always result in vegetation drought stress as vegetation responses to the climate involve complex processes. Satellite-based vegetation indices such as the Normalized Difference Vegetation Index (NDVI) have been widely used to monitor vegetation activities. As satellites only carry information for understanding past and current vegetation conditions, there is a need to model vegetation dynamics to make future predictions. Although many other factors are related, we attempt to predict the vegetation activities and stresses via simulating NDVI, based on only meteorological data and using a deep learning method (bidirectional long short-term memory model, BiLSTM). The BiLSTM is a sequence processing model that can predict NDVI by establishing the relationship between meteorological variables and vegetation activities. Experimental results show that the predicted NDVI is consistent with the reference data (R² = 0.69 ± 0.28). The best accuracy was achieved in the deciduous forest (R² = 0.87 ± 0.16). The vegetation condition index (VCI) calculated from the BiLSTM-predicted NDVI also agreed with the satellite-based ones (R² = 0.70 ± 0.28). Both the monitored and predicted VCI indicated an upward but insignificant trend of vegetation activity in the past decade and increased vegetation stresses in the early growing season over northern China. Based on meteorological data, the deep learning-based solution shows the potential for not only retrospective analysis, but also future prediction of vegetation activities and stresses under varied climate conditions as compared with remote sensing data. Full article

(This article belongs to the Special Issue Spatial Analysis and Land Use Planning for Sustainable Ecosystem)

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21 pages, 12995 KiB

Open AccessArticle

Detecting the Spatiotemporal Variation of Vegetation Phenology in Northeastern China Based on MODIS NDVI and Solar-Induced Chlorophyll Fluorescence Dataset

by Ruixin Zhang, Yuke Zhou, Tianyang Hu, Wenbin Sun, Shuhui Zhang, Jiapei Wu and Han Wang

Sustainability 2023, 15(7), 6012; https://0-doi-org.brum.beds.ac.uk/10.3390/su15076012 - 30 Mar 2023

Cited by 2 | Viewed by 3649

Abstract

Vegetation phenology is a crucial biological indicator for monitoring changes in terrestrial ecosystems and global climate. Currently, there are limitations in using traditional vegetation indices for phenology monitoring (e.g., greenness saturation in high-density vegetation areas). Solar-induced chlorophyll fluorescence (SIF), a novel remote sensing product, has great potential in depicting seasonal vegetation dynamics across various regions with different vegetation covers and latitudes. In this study, based on the GOSIF and MODIS NDVI data from 2001 to 2020, we extracted vegetation phenological parameters in Northeastern China by using Double Logistic (D-L) fitting function and the dynamic threshold method. Then, we analyzed the discrepancy in phenological period and temporal trend derived from SIF and NDVI data at multiple spatiotemporal scales. Furthermore, we explored the response of vegetation phenology to climate change and the persistence of phenological trends (Hurst exponent) in Northeastern China. Generally, there is a significant difference in trends between SIF and NDVI, but with similar spatial patterns of phenology. However, the dates of key phenological parameters are distinct based on SIF and MODIS NDVI data. Specifically, the start of season (SOS) of SIF started later (about 10 days), and the end of season (EOS) ended earlier (about 36 days on average). In contrast, the fall attenuation of SIF showed a lag process compared to NDVI. This implies that the actual period of photosynthesis, that is, length of season (LOS), was shorter (by 46 days on average) than the greenness index. The position of peak (POP) is almost the same between them. The great difference in results from SIF and NDVI products indicated that the vegetation indexes seem to overestimate the time of vegetation photosynthesis in Northeastern China. The Hurst exponent identified that the future trend of SOS, EOS, and POP is dominated by weak inverse sustainability, indicating that the future trend may be opposite to the past. The future trend of LOS_SIF and LOS_NDVI are opposite; the former is dominated by weak inverse sustainability, and the latter is mainly weak positive sustainability. In addition, we speculate that the difference between SIF and NDVI phenology is closely related to their different responses to climate. The vegetation phenology estimated by SIF is mainly controlled by temperature, while NDVI is mainly controlled by precipitation and relative humidity. Different phenological periods based on SIF and NDVI showed inconsistent responses to pre-season climate. This may be the cause of the difference in the phenology of SIF and NDVI extraction. Our results imply that canopy structure-based vegetation indices overestimate the photosynthetic cycle, and the SIF product can better track the phenological changes. We conclude that the two data products provide a reference for monitoring the phenology of photosynthesis and vegetation greenness, and the results also have a certain significance for the response of plants to climate change. Full article

(This article belongs to the Special Issue Spatial Analysis and Land Use Planning for Sustainable Ecosystem)

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19 pages, 20030 KiB

Open AccessArticle

Toward 30 m Fine-Resolution Land Surface Phenology Mapping at a Large Scale Using Spatiotemporal Fusion of MODIS and Landsat Data

by Yongjian Ruan, Baozhen Ruan, Xinchang Zhang, Zurui Ao, Qinchuan Xin, Ying Sun and Fengrui Jing

Sustainability 2023, 15(4), 3365; https://0-doi-org.brum.beds.ac.uk/10.3390/su15043365 - 12 Feb 2023

Cited by 2 | Viewed by 1420

Abstract

Satellite-retrieved land surface phenology (LSP) is a first-order control on terrestrial ecosystem productivity, which is critical for monitoring the ecological environment and human and social sustainable development. However, mapping large-scale LSP at a 30 m resolution remains challenging due to the lack of dense time series images with a fine resolution and the difficulty in processing large volumes of data. In this paper, we proposed a framework to extract fine-resolution LSP across the conterminous United States using the supercomputer Tianhe-2. The proposed framework comprised two steps: (1) generation of the dense two-band enhanced vegetation index (EVI2) time series with a fine resolution via the spatiotemporal fusion of MODIS and Landsat images using ESTARFM, and (2) extraction of the long-term and fine-resolution LSP using the fused EVI2 dataset. We obtained six methods (i.e., AT, FOD, SOD, RCR, TOD and CCR) of fine-resolution LSP with the proposed framework, and evaluated its performance at both the site and regional scales. Comparing with PhenoCam-observed phenology, the start of season (SOS) derived from the fusion data using six methods of AT, FOD, SOD, RCR, TOD and CCR obtained r values of 0.43, 0.44, 0.41, 0.29, 0.46 and 0.52, respectively, and RMSE values of 30.9, 28.9, 32.2, 37.9, 37.8 and 33.2, respectively. The satellite-retrieved end of season (EOS) using six methods of AT, FOD, SOD, RCR, TOD and CCR obtained r values of 0.68, 0.58, 0.68, 0.73, 0.65 and 0.56, respectively, and RMSE values of 51.1, 53.6, 50.5, 44.9, 51.8 and 54.6, respectively. Comparing with the MCD12Q2 phenology, the satellite-retrieved 30 m fine-resolution LSP of the proposed framework can obtain more information on the land surface, such as rivers, ridges and valleys, which is valuable for phenology-related studies. The proposed framework can yield robust fine-resolution LSP at a large-scale, and the results have great potential for application into studies addressing problems in the ecological environmental at a large scale. Full article

(This article belongs to the Special Issue Spatial Analysis and Land Use Planning for Sustainable Ecosystem)

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16 pages, 2381 KiB

Open AccessArticle

Evaluation Study of Ecological Resilience in Southern Red Soil Mining Areas Considering Rare Earth Mining Process

by Jianying Zhang, Hengkai Li, Duan Huang and Xiuli Wang

Sustainability 2023, 15(3), 2258; https://0-doi-org.brum.beds.ac.uk/10.3390/su15032258 - 26 Jan 2023

Cited by 2 | Viewed by 2954

Abstract

Ion-adsorption rare earth mining areas are located in southern China’s ecologically fragile red soil hills region. For a long time, under the influence of multiple factors such as low mining technology and indiscriminate mining, this area has experienced serious environmental problems. Therefore, it is crucial for the ecological management and restoration of mining areas to accurately conduct a quantitative evaluation of ecological restoration status. We used remote sensing and geographic information data to establish an ecosystem resilience evaluation index system consisting of five criteria (land stress, vegetation conditions, surface conditions, biodiversity, and air pollution) and 17 evaluation factors. The Lingbei rare earth mining area in Dingnan County in the red soil hill region was used as a case study since it is a representative ion adsorption rare earth mining area. The restoration status of the mining area was evaluated from 2000 to 2020. The results showed the following: (1) From 2000 to 2020, the ecological resilience level of the mining area was 0.695, 0.685, 0.664, 0.651, and 0.657, exhibiting a decrease followed by an increase. (2) Spatially, the ecological resilience was low at the mine site and increased with increasing distance, indicating that rare earth mining adversely affected ecological resilience in the mining area. (3) The regional ecological resilience has improved over time due to the implementation of green development policies. However, the rate of improvement is slow and ecological restoration of mining areas will remain an ongoing challenge in the future. This study can provide a scientific basis and practical reference for the ecological protection and restoration of mining areas. Full article

(This article belongs to the Special Issue Spatial Analysis and Land Use Planning for Sustainable Ecosystem)

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15 pages, 3789 KiB

Open AccessArticle

Long-Term Dynamics of Chlorophyll-a Concentration and Its Response to Human and Natural Factors in Lake Taihu Based on MODIS Data

by Zihong Qin, Baozhen Ruan, Jian Yang, Zushuai Wei, Weiwei Song and Qiang Sun

Sustainability 2022, 14(24), 16874; https://0-doi-org.brum.beds.ac.uk/10.3390/su142416874 - 15 Dec 2022

Cited by 3 | Viewed by 1137

Abstract

Chlorophyll-a plays an essential biochemical role in the eutrophication process, and is widely considered an important water quality indicator for assessing human activity’s effects on aquatic ecosystems. Herein, 20 years of moderate resolution imaging spectroradiometer (MODIS) data were applied to investigate the spatiotemporal patterns and trends of chlorophyll-a concentration (Chla) in the eutrophic Lake Taihu, based on a new empirical model. The validated results suggested that our developed model presented appreciable performance in estimating Chla, with a root mean square error (MAPE) of 12.95 μg/L and mean absolute percentage error (RMSE) of 29.98%. Long-term MODIS observations suggested that the Chla of Lake Taihu experienced an overall increasing trend and significant spatiotemporal heterogeneity during 2002–2021. A driving factor analysis indicated that precipitation and air temperature had a significant impact on the monthly dynamics of Chla, while chemical fertilizer consumption, municipal wastewater, industrial sewage, precipitation, and air temperature were important driving factors and together explained more than 81% of the long-term dynamics of Chla. This study provides a 20 year recorded dataset of Chla for inland waters, offering new insights for future precise eutrophication control and efficient water resource management. Full article

(This article belongs to the Special Issue Spatial Analysis and Land Use Planning for Sustainable Ecosystem)

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