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Urban Green and Blue Infrastructure Monitoring Using Remote Sensing: Current Progress and Future Vision

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: 15 May 2024 | Viewed by 24130

Special Issue Editors


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Guest Editor
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 10085, China
Interests: urban ecology; remote sensing; urban greenspace; spatial pattern; urban heat island; landscape ecology
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
College of Applied Arts and Sciences, Beijing Union University, Beijing 100191, China
Interests: Integrated risk assessment; ecosystem service; resilience; vulnerability; urban agglomeration

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Guest Editor
Institute of Urban Studies, School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China
Interests: urban ecology; remote sensing; urban sustainability; GIS modelling

Special Issue Information

Dear Colleagues,

Urban green and blue infrastructures provide myriad ecosystem services (ESs) that are fundamental to human well-being and urban sustainability. Remote sensing has long been used to quantify the spatial and temporal patterns of urban green and blue infrastructures, and their linkage to ecological function and services. With the improvement of temporal, spatial and spectral resolution, remote sensing data has been increasingly becoming the main data sources for describing and monitoring urban landscapes. Particularly, the wide availability of high-resolution imagery, hyperspectral imagery, LiDAR data, and microwave remote sensing data offers new opportunities to better understand the structure and function of urban green and blue infrastructure.

The Special Issue aims to enhance our understanding on the applications of remote sensing, especially high-resolution imagery, hyperspectral imagery, LiDAR data, and microwave remote sensing data in urban green and blue infrastructure monitoring. We particularly welcome new approaches, new algorithms, and new data that can be applied to improve the accuracy and efficiency of quantifying the spatiotemporal patterns of urban green and blue infrastructures. But topics may cover anything from classical urban land cover classification and estimation of urban greenspace and water variables, to spatial pattern analysis, landscape dynamics, urban ecological and environmental problems, urban planning and management, and more comprehensive aims. We welcome studies conducted at the local scale, or city scale, or regional and global scales.

The topics of this Special Issue include but are not limited to:

  • Urban land cover classification;
  • Urban greenspace variables inventory;
  • Urban water variables inventory;
  • Spatial pattern analysis;
  • Landscape dynamics;
  • Urban planning and management;
  • Remote sensing data fusion;
  • Remote sensing monitoring;
  • Urban biodiversity;
  • Urban water pollution;
  • Urban heat island.

Dr. Weiqi Zhou
Dr. Xiaoqian Liu
Dr. Zhonghao Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Urban greenspace
  • Urban ecology
  • Urban water
  • Urban wetland
  • Spatial pattern
  • Urban landscape
  • Urban dynamics
  • Urban biodiversity
  • Urban resilience
  • Remote sensing data fusion
  • Remote sensing monitoring

Published Papers (11 papers)

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Research

24 pages, 10827 KiB  
Article
Toward Precision Agriculture in Outdoor Vertical Greenery Systems (VGS): Monitoring and Early Detection of Stress Events
by Noa Zuckerman, Yafit Cohen, Victor Alchanatis and Itamar M. Lensky
Remote Sens. 2024, 16(2), 302; https://doi.org/10.3390/rs16020302 - 11 Jan 2024
Viewed by 676
Abstract
Vertical greenery systems (VGS) have been proposed as a nature-based solution to mitigate the adverse effects of urban heat islands and climate change in cities. However, large-scale VGS are costly and require ongoing maintenance, typically carried out manually through trial and error based [...] Read more.
Vertical greenery systems (VGS) have been proposed as a nature-based solution to mitigate the adverse effects of urban heat islands and climate change in cities. However, large-scale VGS are costly and require ongoing maintenance, typically carried out manually through trial and error based on professional experience. Advanced management is essential for the sustainability of VGS due to its limited accessibility and associated costs. To address these challenges, we examined the use of remote sensing methods for outdoor VGS monitoring as a basis for a precision agriculture approach for VGS management and maintenance. This study presents the first ongoing monitoring of real-scale VGS using thermal, hyperspectral, and RGB vegetation indices. These indices were employed for the early detection of vegetation stress, focusing on two case studies exhibiting visible yellowing symptoms. Through the application of unsupervised classification techniques, stressed pixels were successfully detected 14–35 days before visual yellowing, achieving an accuracy of 0.85–0.91. Additionally, the thermal index provided valuable information regarding the spatial distribution of watering along the VGS. Stress maps based on noninvasive methods were demonstrated, forming the basis of a spatial decision support system capable of detecting issues related to plant vitality and VGS irrigation management. Full article
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20 pages, 42943 KiB  
Article
Coupling the Calibrated GlobalLand30 Data and Modified PLUS Model for Multi-Scenario Land Use Simulation and Landscape Ecological Risk Assessment
by Zongmin Wang, Mengdan Guo, Dong Zhang, Ruqi Chen, Chaofan Xi and Haibo Yang
Remote Sens. 2023, 15(21), 5186; https://doi.org/10.3390/rs15215186 - 31 Oct 2023
Viewed by 909
Abstract
Rapid economic growth and urbanization have significantly changed the land use distribution and landscape ecological structure, which has a profound impact on the natural environment. A scientific grasp of the characteristics of land use distribution and its impact on landscape ecological risk is [...] Read more.
Rapid economic growth and urbanization have significantly changed the land use distribution and landscape ecological structure, which has a profound impact on the natural environment. A scientific grasp of the characteristics of land use distribution and its impact on landscape ecological risk is a prerequisite for sustainable urban development. This study aimed to calibrate GlobalLand30 data using the normalized difference impervious surface index (NDISI) obtained from Landsat images, thereby providing a more precise foundation for land simulation. Additionally, it sought to improve the accuracy of the patch-generating land use simulation (PLUS) through parameter sensitivity analysis. Building upon this, the research also simulates future land use in Beijing. Lastly, this study introduced an LER index to assess ecological risk in the current and future urban landscapes. The results showed that the GlobalLand30 data were calibrated and PLUS model accuracy was improved to more than 86%. The accuracy of the modified PLUS model based on a Morris sensitivity analysis was increased, and the kappa coefficients were increased by approximately 3%. The results of the multi-scenario simulation showed that under the SSP126-EP scenario, future land use in Beijing could balance urban development and ecological protection, and thus would be more suitable for sustainable development. In the other two scenarios, ecological land will be encroached by urban development. From 2000 to 2020, the degree of LER was generally lower, moderate, or higher, and the overall level of LER showed a downward trend continuing until 2100 in the SSP126-EG scenario. Future land use simulations and LER assessment under multi-scenarios could help decision makers develop multi-scale landscape protection strategies. Full article
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23 pages, 40006 KiB  
Article
Impervious Surface Area Patterns and Their Response to Land Surface Temperature Mechanism in Urban–Rural Regions of Qingdao, China
by Tao Pan, Baofu Li and Letian Ning
Remote Sens. 2023, 15(17), 4265; https://doi.org/10.3390/rs15174265 - 30 Aug 2023
Viewed by 971
Abstract
The expansion of impervious surface area (ISA) in megacities of China often leads to land surface temperature (LST) aggregation effects, which affect living environments by impacting thermal comfort levels, thus becoming an issue of public concern. However, from an urban–rural synchronous comparison perspective, [...] Read more.
The expansion of impervious surface area (ISA) in megacities of China often leads to land surface temperature (LST) aggregation effects, which affect living environments by impacting thermal comfort levels, thus becoming an issue of public concern. However, from an urban–rural synchronous comparison perspective, the study of LST responses to ISA changes is still lacking in the central coastal megalopolises of China. To solve this issue, a collaborative methodology of artificial digitization—fully constrained least squares mixed pixel decomposition—split-window algorithm—PCACA model was established for Qingdao using land use dataset and remote sensing images. The conclusions are below. Long time series of land use monitoring indicated that the expansion ratios of urban and rural areas were 131.29% and 43.42% in the past 50 years (i.e., from 1970 to 2020). Within urban and rural areas, a synchronous ISA increase was observed, with ratios of +9.14% (140.55 km2) and +7.94% (28.04 km2), respectively. Higher ratios and area changes were found in the urban regions, and a similar ISA change pattern in both urban and rural regions was captured by the ISA horizontal epitaxial expansion and vertical density enhancement. Further, the horizontal gradient effect displayed that the mean LSTs were 28.75 °C, 29.77 °C and 31.91 °C in the urban areas and 28.73 °C, 29.66 °C and 31.65 °C in the rural areas in low-, medium-, and high-density ISAs. The vertical density effect showed that the LST change was 1.02 °C and 2.14 °C in the urban areas but 0.93 °C and 1.99 °C in the rural areas during the ISA-density transition from low- to medium- and from medium- to high-density, respectively. Potential surface thermal indicators were assessed, and the urban regions displayed higher sensible heat flux (280.13 W/m2) compared to the rural regions (i.e., 274.76 W/m2). The mechanism effect of the ISA changes on LST in the urban and rural regions was revealed. These findings form a new comparative perspective of the urban–rural synchronous change in the central coastal megalopolis of China and can provide a practical reference for relevant studies. Full article
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19 pages, 4909 KiB  
Article
Monitoring Impervious Surface Area Dynamics in Urban Areas Using Sentinel-2 Data and Improved Deeplabv3+ Model: A Case Study of Jinan City, China
by Jiantao Liu, Yan Zhang, Chunting Liu and Xiaoqian Liu
Remote Sens. 2023, 15(8), 1976; https://doi.org/10.3390/rs15081976 - 08 Apr 2023
Cited by 4 | Viewed by 1530
Abstract
Timely and rapidly mapping impervious surface area (ISA) and monitoring its spatial-temporal change pattern can deepen our understanding of the urban process. However, the complex spectral variability and spatial heterogeneity of ISA caused by the increased spatial resolution poses a great challenge to [...] Read more.
Timely and rapidly mapping impervious surface area (ISA) and monitoring its spatial-temporal change pattern can deepen our understanding of the urban process. However, the complex spectral variability and spatial heterogeneity of ISA caused by the increased spatial resolution poses a great challenge to accurate ISA dynamics monitoring. This research selected Jinan City as a case study to boost ISA mapping performance through integrating the dual-attention CBAM module, SE module and focal loss function into the Deeplabv3+ model using Sentinel-2 data, and subsequently examining ISA spatial-temporal evolution using the generated annual time-series ISA data from 2017 to 2021. The experimental results demonstrated that (a) the improved Deeplabv3+ model achieved satisfactory accuracy in ISA mapping, with Precision, Recall, IoU and F1 values reaching 82.24%, 92.38%, 77.01% and 0.87, respectively. (b) In a comparison with traditional classification methods and other state-of-the-art deep learning semantic segmentation models, the proposed method performed well, qualitatively and quantitatively. (c) The time-series analysis on ISA distribution revealed that the ISA expansion in Jinan City had significant directionality from northeast to southwest from 2017 to 2021, with the number of patches as well as the degree of connectivity and aggregation increasing while the degree of fragmentation and the complexity of shape decreased. Overall, the proposed method shows great potential in generating reliable times-series ISA data and can be better served for fine urban research. Full article
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15 pages, 4282 KiB  
Article
Does Regional Urbanization Promote Balanced Land Development? Evidence from Long Time Series Satellite Imagery
by Jun Qin, Wenjuan Yu, Sheng Li, Weiqi Zhou and Shouyun Shen
Remote Sens. 2023, 15(3), 783; https://doi.org/10.3390/rs15030783 - 30 Jan 2023
Cited by 1 | Viewed by 1219
Abstract
The urban megaregion has been promoted as among the major urbanization forms in New-Type Urbanization in China, which aims to promote more balanced development among cities and between the urban and rural areas in a region. While numerous studies have examined developed land [...] Read more.
The urban megaregion has been promoted as among the major urbanization forms in New-Type Urbanization in China, which aims to promote more balanced development among cities and between the urban and rural areas in a region. While numerous studies have examined developed land expansion in cities worldwide using remotely sensed imagery, fewer have investigated its dynamic process in a rural area and the differences in the growth magnitude and expansion morphology between urban and rural areas. Using Landsat imagery from 1986 to 2020, we examined the spatiotemporal patterns of developed land in both the urban and rural areas in the Changsha–Zhuzhou–Xiangtan urban megaregion, China, using morphological analysis. We found that (1) the differences in the growth magnitude between the urban and rural areas varied between the different-sized cities, with increases in the largest city of Changsha, but decreases in the smaller ones of Zhuzhou and Xiangtan, although there was a slight increase at the megaregional scale. (2) The dynamic process of developed land in rural areas was similar to that in urban areas but showed a clear time-lag effect, where the dominant expansion types in urban areas shifted from edge to infilling expansion and to another edge expansion in 1986–2000, 2000–2010, and 2010–2020, whereas that in rural areas changed from outlying to edge expansion in 1986–2000 and 2000–2020. (3) The positive relationships between the growth speed and outlying and edge expansion suggested that the CZT urban megaregion was in the rapid outward expansion stage. Such a pattern may cause similar ecological effects, such as habitat fragmentation and urban heat archipelagos, to that in the eastern megaregions. Understanding such differences and their changes in the urban and rural areas will help optimize the strategies of urban megaregion sustainability. Full article
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18 pages, 5656 KiB  
Article
Large-Scale Impervious Surface Area Mapping and Pattern Evolution of the Yellow River Delta Using Sentinel-1/2 on the GEE
by Jiantao Liu, Yexiang Li, Yan Zhang and Xiaoqian Liu
Remote Sens. 2023, 15(1), 136; https://doi.org/10.3390/rs15010136 - 26 Dec 2022
Cited by 6 | Viewed by 1518
Abstract
The ecological environment of Yellow River Delta High-efficiency Ecological Economic Zone (YRDHEEZ) is adjacent to the Bohai Sea. The unique geographical location makes it highly sensitive to anthropogenic disturbances. As an important land surface biophysical parameter, the impervious surface area (ISA) can characterize [...] Read more.
The ecological environment of Yellow River Delta High-efficiency Ecological Economic Zone (YRDHEEZ) is adjacent to the Bohai Sea. The unique geographical location makes it highly sensitive to anthropogenic disturbances. As an important land surface biophysical parameter, the impervious surface area (ISA) can characterize the level of urbanization and measure the intensity of human activities, and hence, the timely understanding of ISA dynamic changes is of great significance to protect the ecological safety of the YRDHEEZ. Based on the multi-source and multi-modal Sentinel-1/2 remotely sensed data provided by Google Earth Engine (GEE) cloud computing platform, this study developed a novel approach for the extraction of time-series ISA in the YRDHEEZ through a combination of random forest algorithm and numerous representative features extracted from Sentinel-1/2. Subsequently, we revealed the pattern of the ISA spatial-temporal evolution in this region over the past five years. The results demonstrated that the proposed method has good performance with an average overall accuracy of 94.84% and an average kappa coefficient of 0.9393, which verified the feasibility of the proposed method for large-scale ISA mapping with 10 m. Spatial-temporal evolution analysis revealed that the ISA of the YRDHEEZ decreased from 5211.39 km2 in 2018 to 5147.02 km2 in 2022 with an average rate of −16.09 km2/year in the last 5 years, suggesting that the ISA of YRDHEEZ has decreased while its overall pattern was not significantly changed over time. The presented workflow can provide a reference for large-scale ISA mapping and its evolution analysis, especially in regions on estuarine deltas. Full article
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17 pages, 3194 KiB  
Article
Impacts of Urban Green Space on Land Surface Temperature from Urban Block Perspectives
by Hongmin An, Hongyan Cai, Xinliang Xu, Zhi Qiao and Dongrui Han
Remote Sens. 2022, 14(18), 4580; https://doi.org/10.3390/rs14184580 - 13 Sep 2022
Cited by 12 | Viewed by 3106
Abstract
Urban green space (UGS) can be regarded as an effective approach to mitigate urban heat island (UHI) effects. Many studies have investigated the impacts of composition and configuration of UGS on land surface temperature (LST), while little attention has been paid to the [...] Read more.
Urban green space (UGS) can be regarded as an effective approach to mitigate urban heat island (UHI) effects. Many studies have investigated the impacts of composition and configuration of UGS on land surface temperature (LST), while little attention has been paid to the impacts among different urban blocks. Thus, taking 1835 urban blocks in Beijing as samples, including low-rise point (LRP), low-rise street (LRS), low-rise block (LRB), mid-rise point (MRP), mid-rise street (MRS), mid-rise block (MRB), high-rise point (HRP), high-rise street (HRS) and high-rise block (HRB), this study investigated the impacts of UGS on LST among different urban blocks. The results showed that UGS serves as cold islands among different urban blocks. Percentage of landscape (PLAND) of UGS in all types of urban blocks, edge density (ED) of UGS in MRS, area-weighted fractal dimension index (FRAC_AM) of UGS in HRS and HRB show significantly negative impacts on LST, while aggregation index (AI) of UGS in LRP shows significantly positive impacts. The findings suggest that both composition and configuration of UGS can affect LST among different urban blocks and rational allocation of UGS would be effective for mitigating UHI effects. Full article
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16 pages, 3044 KiB  
Article
Land Use and Land Cover Changes and Prediction Based on Multi-Scenario Simulation: A Case Study of Qishan County, China
by Nina Xiong, Rongxia Yu, Feng Yan, Jia Wang and Zhongke Feng
Remote Sens. 2022, 14(16), 4041; https://doi.org/10.3390/rs14164041 - 19 Aug 2022
Cited by 6 | Viewed by 1628
Abstract
Research on land use change is helpful to better understand the processes and mechanisms of land use changes and provide a decision base for reasonable land development. However, studies on LUCC were mainly conducted for megalopolises and urban agglomerations in China, but there [...] Read more.
Research on land use change is helpful to better understand the processes and mechanisms of land use changes and provide a decision base for reasonable land development. However, studies on LUCC were mainly conducted for megalopolises and urban agglomerations in China, but there is a gap in the scholarly community when it comes to shrinking small cities where the population decreased sharply under the influence of the urban expansion of megacities. Hence, it is necessary to investigate the evolution rule of land use in these regions. This study takes Qishan County in Shanxi Province as the research subject and analyzes the land use change over the last 20 years with remote sensing technology. Comparing the two LUCC models of the CA-Markov Model and the LCM Model, an optimal model is used to predict and simulate land use change under three potential scenarios in 2030. The conclusions are stated as follows: (1) From 2000 to 2020, the cultivated land area increased originally and subsequently decreased, and forest land continued to decrease at a progressively slower speed. In contrast, the urban land area expanded significantly. (2) The comprehensive dynamic change in water land is the most significant, indicating that this is an unstable land resource in the region and more attention should be given to this matter. (3) The scenario of water area protection indicates that the inhibition of the transition of water areas can protect their vulnerable ecological environment without negatively impacting economic development. Furthermore, the ongoing focus on economic development in the region is related to the rapid disappearance of cultivated land, which is not an optimistic perspective for the area’s ecosystem. The results of this study implied land transition features and mechanisms in Qishan County, providing novel insights for decision support for county-level land use planning. Full article
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21 pages, 3608 KiB  
Article
Impacts of Land Use Changes on Net Primary Productivity in Urban Agglomerations under Multi-Scenarios Simulation
by Yuhan Chen, Jia Wang, Nina Xiong, Lu Sun and Jiangqi Xu
Remote Sens. 2022, 14(7), 1755; https://doi.org/10.3390/rs14071755 - 06 Apr 2022
Cited by 33 | Viewed by 3197
Abstract
Land use is closely related to the sustainability of ecological development. This paper employed a patch-generating land use simulation (PLUS) model for the multi-scenario simulation of urban agglomerations. In addition, mathematical analysis methods such as Theil-Sen Median trend analysis, R/S analysis, Getis-Ord Gi* [...] Read more.
Land use is closely related to the sustainability of ecological development. This paper employed a patch-generating land use simulation (PLUS) model for the multi-scenario simulation of urban agglomerations. In addition, mathematical analysis methods such as Theil-Sen Median trend analysis, R/S analysis, Getis-Ord Gi* index and unary linear regression were used to study the temporal and spatial evolution characteristics of net primary productivity (NPP) for the impact of land use changes on NPP in urban agglomerations from 2000 to 2020 and to forecast the future trend of NPP. The results indicate that urban expansion is obvious in the baseline scenario and in the ecological protection scenario. In the scenario of cropland protection, the urban expansion is consistent with the land use plan of the government for 2035. The NPP in Beijing decreased gradually from northwest to southeast. The hot spot areas are concentrated in the densely forested areas in the mountainous areas of northwest. The cold spot areas are mainly concentrated in the periphery of urban areas and water areas. The NPP will continue to increase in forest and other areas under protection and remain stable in impervious surfaces. The NPP of Beijing showed a strong improvement trend and this trend will continue with the right ecological management and urban planning of the government. The study of land use in urban agglomeration and the development trend of vegetation NPP in the future can help policymakers rationally manage future land use dynamics and maintain the sustainable development of urban regional ecosystems. Full article
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20 pages, 2783 KiB  
Article
Spatial and Temporal Variation, Simulation and Prediction of Land Use in Ecological Conservation Area of Western Beijing
by Jia Wang, Junping Zhang, Nina Xiong, Boyi Liang, Zong Wang and Elizabeth L. Cressey
Remote Sens. 2022, 14(6), 1452; https://doi.org/10.3390/rs14061452 - 17 Mar 2022
Cited by 56 | Viewed by 3520
Abstract
Exploring land use change is crucial to planning land space scientifically in a region. Taking the ecological conservation area (ECA) in western Beijing as the study area, we employ ArcGIS 10.2, landscape pattern index and multiple mathematical statistics to explore the temporal and [...] Read more.
Exploring land use change is crucial to planning land space scientifically in a region. Taking the ecological conservation area (ECA) in western Beijing as the study area, we employ ArcGIS 10.2, landscape pattern index and multiple mathematical statistics to explore the temporal and spatial variation of land use from 2000 to 2020. Patch-generating Land Use Simulation (PLUS), Future Land Use Simulation (FLUS) and Markov models were used to simulate and predict the current land use in 2020. The models were evaluated for accuracy, and the more accurate PLUS model was selected and used to simulate and predict the potential land use in the study area in 2030 under two management scenarios. The main findings of this research are: (1) From 2000 to 2020, the construction land increased constantly, and the area of cultivated land and grassland decreased significantly. (2) For predicting the spatial distribution of land use in the study area, the PLUS model was more accurate than the FLUS model. (3) The land-use prediction of the study area in 2030 shows that the area of grassland, forest and water is approximately equal to their corresponding value in 2020, but the construction land increased constantly by occupying the surrounding cultivated land. According to this research, the continuous decrease of cultivated land in favor of increasing construction land will cause losses to the ecological service function of the ECA, which is not beneficial to the sustainable development of the region. Relevant departments should take corresponding measures to reduce this practice and promote sustainable development, particularly in the southern and western areas of the ECA where there is less construction land. Full article
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15 pages, 2184 KiB  
Article
Estimating Aboveground Carbon Stock at the Scale of Individual Trees in Subtropical Forests Using UAV LiDAR and Hyperspectral Data
by Haiming Qin, Weiqi Zhou, Yang Yao and Weimin Wang
Remote Sens. 2021, 13(24), 4969; https://doi.org/10.3390/rs13244969 - 07 Dec 2021
Cited by 11 | Viewed by 3944
Abstract
Accurate estimation of aboveground carbon stock for individual trees is important for evaluating forest carbon sequestration potential and maintaining ecosystem carbon balance. Airborne light detection and ranging (LiDAR) data has been widely used to estimate tree-level carbon stock. However, few studies have explored [...] Read more.
Accurate estimation of aboveground carbon stock for individual trees is important for evaluating forest carbon sequestration potential and maintaining ecosystem carbon balance. Airborne light detection and ranging (LiDAR) data has been widely used to estimate tree-level carbon stock. However, few studies have explored the potential of combining LiDAR and hyperspectral data to estimate tree-level carbon stock. The objective of this study is to explore the potential of integrating unmanned aerial vehicle (UAV) LiDAR with hyperspectral data for tree-level aboveground carbon stock estimation. To achieve this goal, we first delineated individual trees by a CHM-based watershed segmentation algorithm. We then extracted structural and spectral features from UAV LiDAR and hyperspectral data respectively. Then, Pearson correlation analysis was conducted to assess the correlation between LiDAR features, hyperspectral features, and tree-level carbon stock, based on which, features were selected for model development. Finally, we developed tree-level carbon stock estimation models based on the Schumacher–Hall formula and stepwise multiple regression. Results showed that both LiDAR and hyperspectral features were strongly correlated to tree-level carbon stock. Both tree height (H, r = 0.75) and Green index (GI, r = 0.83) had the highest correlation coefficients with tree-level carbon stock in LiDAR and hyperspectral features, respectively. The best model using LiDAR features alone includes the metrics of H, the 10th height percentile of points (PH10), and mean height of points (Hmean), and can explain 74% of the variations in tree-level carbon stock. Similarly, the best model using hyperspectral data includes GI and modified normalized differential vegetation index (mNDVI), and has similar explanatory power (r2 = 0.75). The model that integrates predictors, namely, GI and the 95th height percentile of points (PH95) from hyperspectral and LiDAR data, substantially improves the explanatory power (r2 = 0.89). These results indicated that while either LiDAR data or hyperspectral data alone can estimate tree-level carbon stock with reasonable accuracy, combining LiDAR and hyperspectral features can substantially improve the explanatory power of the model. Such results suggested that tree-level carbon stock estimation can greatly benefit from the complementary nature of LiDAR-detected structural characteristics and hyperspectral-captured spectral information of vegetation. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Study on forest resources evaluation in typical mining area based on hyper spectral remote sensing
Authors: Xiaodong Huang; Xiaoxian Yan; Sike Ma
Affiliation: College of Applied Arts and Science of Beijing Union University Beijing, 100191

Title: Integrated ecological risk evaluation of drilling machines in Yellow River delta
Authors: Jiantao Liu
Affiliation: School of Surveying and Geo-Informatics, Shandong Jianzhu University

Title: Changes of vegetation photosynthesis and response to water constraint in the Yangtze River and Yellow River Basin, China
Authors: Anzhou Zhao
Affiliation: School of urban planning and design, Peking University, 100871,China

Title: Estimating aboveground carbon stock at the scale of individual trees in subtropical forest using UAV LiDAR and hyperspectral data
Authors: Haiming Qin
Affiliation: State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 10085, China

Title: Mapping urban greenspace using high spatial resolution imagery
Authors: Luis Inostroza
Affiliation: Geographisches Institut, IA 6-115 Fakultät für Geowissenschaften, Ruhr-Universität Bochum, German

Title: Remote sensing data fusion for urban green and blue space mapping
Authors: Zhonghao Zhang,
Affiliation: Institutional information: Institute of Urban Studies, School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai, 200234, China

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