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

Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 26917

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Special Issue Editors

Dr. Bing Xue

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Institute for Advanced Sustainability Studies (IASS), 14467 Potsdam, Germany
Interests: human–natural system and sustainability; urbanization
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jun Yang

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Guest Editor

Jangho Architecture College, Northeastern University, Shenyang 110169, China
Interests: spatial analysis; land use modelling; land use change; urban climatology
Special Issues, Collections and Topics in MDPI journals

Dr. Huidong Li

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Key Laboratory of Forest Ecology and Management, Chinese Academy of Sciences, Shenyang, China
Interests: urban climatology; regional climate modelling; ecological modelling; ecological processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Along with the fast development of society and economy, the interaction between human and natural ecosystems is becoming more intensive, forming the human–nature coupled ecosystem. The increasing human activity in the coupled ecosystem, such as urban landscape adjustment, afforestation, disafforestation, and irrigation, has largely changed the regional ecological patterns and structures. These changes will in turn affect the regional social-economic development. Understanding the interaction between human and natural ecosystems is therefore essential for regional sustainability. Remote sensing can monitor ecosystems at a large scale and provides efficient tools to study ecosystem structure and dynamics as well as carbon–water cycles. However, different from the typical natural ecosystem, the human–nature coupled ecosystem is usually highly heterogeneous and complicated in terms of both spatial and temporal. It is, thus, difficult to accurately identify and quantify the interaction between humans and natural ecosystems at a large scale, resulting in the poor understanding and evaluation of their impacts on regional sustainability. Given the increasing interaction between humans and the natural ecosystem, developing innovative methods, indicators, and frameworks to utilize the remote sensing technique for the regional human-natural coupled ecosystem is becoming essential and urgent. This Special Issue focuses on the latest research advances in remote sensing technologies and their applications for observing, understanding, modeling, and communicating interaction between humans and natural ecosystems. We will look at new methodological approaches, framework, and indicators towards mapping (1) human activity (i.e., urbanization, urban greening, and ecological engineering) and (2) their impacts on regional ecology, climate, water resources, and social-economic development. Submissions in the form of research articles, reviews, perspectives, and case studies are all welcome.

Dr. Bing Xue
Prof. Dr. Jun Yang
Dr. Huidong Li
Guest Editor

Manuscript Submission Information

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Keywords

human activity
urban expansion
urban greening
ecological engineering
spatial interaction
human-natural coupled ecosystem
regional ecology and sustainability

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Published Papers (11 papers)

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21 pages, 15254 KiB

Open AccessArticle

Temporal and Spatial Evolution Analysis and Correlation Measurement of Urban–Rural Fringes Based on Nighttime Light Data

by Bo Fu and Bing Xue

Remote Sens. 2024, 16(1), 88; https://doi.org/10.3390/rs16010088 - 25 Dec 2023

Cited by 1 | Viewed by 1382

Abstract

Rural–urban fringe areas serve as crucial transitional zones within urban structures, and their spatiotemporal evolution holds significant reference value for scientifically planning urban configurations. The existing research predominantly focuses on large cities, overlooking the spatiotemporal evolution mechanisms of small- to medium-sized cities. This study employs nighttime light data as the data source to ensure continuous and consistent data, overcoming administrative boundaries. Taking Taizhou City as a case study, a combination of the threshold method and an improved Mann–Kendall algorithm is employed to reveal the evolution process of fringe areas. And a gravity model is utilized to unearth the interaction relationships among regions. The results indicate that from 2010 to 2020, the urban area expanded from 1097 km² to 2791 km², with fringe areas experiencing initial contraction followed by gradual expansion. The central urban areas of Jiaojiang, Luqiao, and Huangyan gradually merged, forming a concentrated region. Linhai maintained a high level of attraction, while spatial gravity weakened in other areas. This study quantitatively analyzes the expansion trends of fringe areas in small- to medium-sized cities, elucidating the attractive effects of time–cost distance and land resources on development and providing valuable support for subsequent spatial planning and governance. Full article

(This article belongs to the Special Issue Remote Sensing of Interaction between Human and Natural Ecosystem)

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22 pages, 11385 KiB

Open AccessArticle

The Compound Effects of Highway Reconstruction and Climate Change on Vegetation Activity over the Qinghai Tibet Plateau: The G318 Highway as a Case Study

by Linghui Guo, Yao Li, Yuanyuan Luo, Jiangbo Gao, Hebing Zhang, Youfeng Zou and Shaohong Wu

Remote Sens. 2023, 15(23), 5473; https://doi.org/10.3390/rs15235473 - 23 Nov 2023

Cited by 1 | Viewed by 1113

Abstract

The Qinghai–Tibet Plateau (QTP) is among one of the most sensitive regions to global environmental change worldwide. Although climate change and engineering construction on the QTP have jointly modified the regional vegetation activity, little is known about how this affects the vegetation variation. Using Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data from 2000–2021, this study investigated the spatiotemporal variation of vegetation activity and the compound effects of climate change and reconstruction along the Tibetan section of the G318 national highway (TG318) through a novel contribution quantification model and partial correlation analysis, as well as through a structural equation model (SEM). The results showed that the mean growing-season EVI increased significantly at a rate of about 0.0020/year in the western side of the TG318 after reconstruction but fluctuated in the east. Reconstruction generally had a significant effect on the mean growing-season EVI, with contributions of 7.67%, 19.12%, 18.24%, and −4.15% in different sections of the TG318, whereas climate change contributed from −10.14% to 8.84% of the total variation. The mean growing-season EVI negatively correlated with snow cover and minimum temperature in humid and sub-humid regions, whereas it was positively related with vapor pressure in semi-arid regions. Moreover, there existed an obvious lag effect of climate change on the mean growing-season EVI, with lag time generally decreasing from west to east and apparent heterogeneity among different months and regions. These findings will help better understand the environmental impacts along the engineering corridors and provide a scientific basis for ecological conservation in the QTP regions. Full article

(This article belongs to the Special Issue Remote Sensing of Interaction between Human and Natural Ecosystem)

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24 pages, 11993 KiB

Open AccessArticle

Effects of Land Cover Change on Vegetation Carbon Source/Sink in Arid Terrestrial Ecosystems of Northwest China, 2001–2018

by Haiyang Tu, Guli Jiapaer, Tao Yu, Liancheng Zhang, Bojian Chen, Kaixiong Lin and Xu Li

Remote Sens. 2023, 15(9), 2471; https://doi.org/10.3390/rs15092471 - 8 May 2023

Cited by 6 | Viewed by 2402

Abstract

The arid terrestrial ecosystem carbon cycle is one of the most important parts of the global carbon cycle, but it is vulnerable to external disturbances. As the most direct factor affecting the carbon cycle, how land cover change affects vegetation carbon sources/sinks in arid terrestrial ecosystems remains unclear. In this study, we chose the arid region of northwest China (ARNWC) as the study area and used net ecosystem productivity (NEP) as an indicator of vegetation carbon source/sink. Subsequently, we described the spatial distribution and temporal dynamics of vegetation carbon sources/sinks in the ARNWC from 2001–2018 by combining the Carnegie-Ames-Stanford Approach (CASA) and a soil microbial heterotrophic respiration (R_H) model and assessed the effects of land cover change on them through modeling scenario design. We found that land cover change had an obvious positive impact on vegetation carbon sinks. Among them, the effect of land cover type conversion contributed to an increase in total NEP of approximately 1.77 Tg C (reaching 15.55% of the original value), and after simultaneously considering the effect of vegetation growth enhancement, it contributed to an increase in total NEP of approximately 14.75 Tg C (reaching 129.61% of the original value). For different land cover types, cropland consistently contributed the most to the increment of NEP, and the regeneration of young and middle-aged forests also led to a significant increase in forest carbon sinks. Thus, our findings provide a reference for assessing the effects of land cover change on vegetation carbon sinks, and they indicated that cropland expansion and anthropogenic management dominated the growth of vegetation carbon sequestration in the ARNWC, that afforestation also benefits the carbon sink capacity of terrestrial ecosystems, and that attention should be paid to restoring and protecting native vegetation in forestland and grassland regions in the future. Full article

(This article belongs to the Special Issue Remote Sensing of Interaction between Human and Natural Ecosystem)

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20 pages, 3939 KiB

Open AccessArticle

Coupled Coordination Analysis between Urbanization and Eco-Environment in Ecologically Fragile Areas: A Case Study of Northwestern Sichuan, Southwest China

by Yike Li, Xinzhe Li and Tao Lu

Remote Sens. 2023, 15(6), 1661; https://doi.org/10.3390/rs15061661 - 19 Mar 2023

Cited by 7 | Viewed by 2440

Abstract

In China, rapid urbanization in recent decades has led to increasingly serious ecological and environmental problems, threatening sustainable development. Thus, a clear understanding of the relationship between urbanization and eco-environment is the basis for achieving regional sustainable development. However, despite the current global explosion of research interests on this topic, few studies have focused on ecologically fragile areas. To fill this gap, taking Aba Autonomous Prefecture in the eastern Qinghai-Tibet Plateau as a case study, we explored the relationship between urbanization and eco-environment from 2001 to 2018 using a coupled coordination degree model. The results show that the urbanization level and eco-environmental quality in Aba Prefecture achieved stable and continuous improvements from 0.202 to 0.428 and 0.372 to 0.422, respectively. Moreover, the coupling degree between them ranged from 0.524 to 0.652, indicating that the study area had transformed from uncoordinated development in the initial stage to transformation development in the final stage. Additionally, over the 18 years, the coordinated state of urbanization and eco-environment improved, with the coordinated level increasing from reluctant to moderate coordination after 2011. Lastly, we confirmed that urbanization in Aba Prefecture had an overall positive effect on the local eco-environment, although it gradually decreased as urbanization progressed. These findings have important implications for political decision-makers to achieve high-quality development in ecologically fragile areas. Full article

(This article belongs to the Special Issue Remote Sensing of Interaction between Human and Natural Ecosystem)

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18 pages, 21033 KiB

Open AccessArticle

Spatiotemporal Patterns of Evapotranspiration in Central Asia from 2000 to 2020

by Xingming Hao, Xue Fan, Zhuoyi Zhao and Jingjing Zhang

Remote Sens. 2023, 15(4), 1150; https://doi.org/10.3390/rs15041150 - 20 Feb 2023

Cited by 4 | Viewed by 2263

Abstract

Evapotranspiration (ET) affects the dry and wet conditions of a region, particularly in arid Central Asia, where changes in evapotranspiration profoundly influence society, the economy, and ecosystems. However, the changing trends in and driving factors of evapotranspiration in Central Asia remain unclear. Therefore, we used estimated ET and reanalysis data to answer research questions. Our results showed that (1) potential evapotranspiration (PET) and ET showed a generally downward trend, in which PET and ET decreased in 37.93% and 17.42% of the total area, respectively. However, PET and ET showed opposite trends in 59.41% of the study area, mainly showing a decrease in PET and an increase in ET. (2) The absolute contribution rates of vegetation–human activity coupling factor (VH), PET, and precipitation (P) to ET were 43.19%, 40.02%, and 16.79%, respectively, and the VH was the main determiner of ET. (3) Transpiration (ET_c) dominated the change in ET in 56.4% of the region, whereas soil evaporation (ET_s) dominated the change in ET in the rest of the region. The coverage threshold that determines the dominant contributions of ET_c and ETs to ET was approximately 18–19%. Below this coverage threshold, the contribution rate of ET_s to ET exceeded that of ET_c and vice versa. In the context of global climate change, this study can provide scientific support for the restoration of water resources and sustainability evaluation of water resources. Full article

(This article belongs to the Special Issue Remote Sensing of Interaction between Human and Natural Ecosystem)

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21 pages, 9410 KiB

Open AccessArticle

A Structure Identification Method for Urban Agglomeration Based on Nighttime Light Data and Railway Data

by Zhiwei Xie, Mingliang Yuan, Fengyuan Zhang, Min Chen, Meng Tian, Lishuang Sun, Guoqing Su and Ruizhao Liu

Remote Sens. 2023, 15(1), 216; https://doi.org/10.3390/rs15010216 - 30 Dec 2022

Cited by 6 | Viewed by 2316

Abstract

The urban spatial structure is a key feature of the distribution of social and economic resources. The spatial structure of an urban agglomeration is an abstract relationship expression of urbanization. Urban agglomerations develop for multiple reasons, including urban planning and natural evolution. To date, most research related to urban agglomeration has been based on single data source, which is a limitation. This research aims to propose a spatial structure identification method for urban agglomerations via a complex network based on nighttime light data and railway data. Firstly, we extracted the urban built-up area using defense meteorological satellite program/operational line scanner (DMSP/OLS) data, and divided it into urban objects to obtain the nighttime light urban network (NLUN) by borough. Secondly, we aggregated railway stations at municipal level using railway operation data to obtain the railway urban network (RUN). Following this, we established a composite urban network (CUN) consisting of the NLUN and the RUN based on the composite adjacency matrix. Finally, the Louvain algorithm and the comprehensive strength index (CSI) were used to detect the communities and central nodes of the CUN and obtain the urban agglomerations and core cities. The results show that urban agglomeration identification based on the CUN has the best accuracy, which is 5.72% and 15.94% higher than that of the NLUN and RUN, respectively. Core cities in the urban agglomeration identified by the CSI in the CUN are at least 3.04% higher than those in the single-source urban network. In addition, the distribution pattern of Chinese urban agglomerations in the study area is expressed as “three vertical”, and the development level of urban agglomeration shows an unbalanced trend. Full article

(This article belongs to the Special Issue Remote Sensing of Interaction between Human and Natural Ecosystem)

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19 pages, 8886 KiB

Open AccessArticle

Construction and Optimization of Ecological Security Pattern in the Loess Plateau of China Based on the Minimum Cumulative Resistance (MCR) Model

by Hong Wei, Hui Zhu, Jun Chen, Haoyang Jiao, Penghui Li and Liyang Xiong

Remote Sens. 2022, 14(22), 5906; https://doi.org/10.3390/rs14225906 - 21 Nov 2022

Cited by 30 | Viewed by 3030

Abstract

With accelerating urbanization, the regional ecological security pattern (ESP) faces unprecedented threats. The situation is particularly serious in the Loess plateau of China (LPC) due to the fragile ecological environment and poor natural conditions. Constructing an ecological network and optimizing the ESP is significant for guiding regional development and maintaining the stability of the ecological process. This study constructed an ecological security network by integrating the minimum cumulative resistance (MCR) model and morphological spatial-pattern-analysis approach in LPC. Additionally, the optimization scheme of the regional ESP has also been proposed. Results show that the ecological source area is about 57,757.8 km², 9.13% of the total area, and is mainly distributed in the southeast of the study area. The spatial distribution of ecological sources shows specific agglomeration characteristics. The ecological security network constructed contains 24 main ecological corridors, 72 secondary ecological corridors, and 53 ecological nodes. Referring to the identified ecological sources area, corridors, nodes, and other core components, the “two barriers, five corridors, three zones and multipoint” ESP optimization scheme was presented. This research hopes to provide a valuable reference for constructing the ecological security network and optimizing ecological space in ecologically fragile areas of western China. Full article

(This article belongs to the Special Issue Remote Sensing of Interaction between Human and Natural Ecosystem)

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22 pages, 6088 KiB

Open AccessArticle

Quantitative Evaluation of Reclamation Intensity Based on Regional Planning Theory and Human–Marine Coordination Since 1974: A Case Study of Shandong, China

by Baijing Liu, Meng Gong, Xiaoqing Wu and Ziyang Wang

Remote Sens. 2022, 14(15), 3822; https://doi.org/10.3390/rs14153822 - 8 Aug 2022

Cited by 1 | Viewed by 2012

Abstract

Increased reclamation activity has adversely affected the conservation of coastal environments. The interactions between reclamation activities and their interference with the natural and functional properties of coastal zones increase the difficulty of marine spatial planning and eco-environmental management. In this study, an evaluation method for describing the intensity of the reclamation activity (RAI) based on regional planning theory and human–marine coordination theory was proposed, and a quantitative evaluation index system was constructed. The method was applied to Shandong Province in China via geographic information system (GIS) spatial analysis. The results reveal that there was an obvious increase in the RAI from 1974 to 2021, with the total reclamation scale index and coordination of reclamation activities index being the most prominent. In addition, it was found that 2007–2017 was the peak period of infilling reclamation in Shandong Province. The natural coastlines are mainly occupied by enclosed mariculture and saltern, which should be strictly managed. The proposed index system can effectively identify the spatiotemporal characteristics of the reclamation intensity and can be used to efficiently determine management priorities. It provides a theoretical basis for regional reclamation management and can be conveniently adopted by management departments for coastal environmental protection. Full article

(This article belongs to the Special Issue Remote Sensing of Interaction between Human and Natural Ecosystem)

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22 pages, 12616 KiB

Open AccessArticle

Annual Maps of Built-Up Land in Guangdong from 1991 to 2020 Based on Landsat Images, Phenology, Deep Learning Algorithms, and Google Earth Engine

by Han Xu, Xiangming Xiao, Yuanwei Qin, Zhi Qiao, Shaoqiu Long, Xianzhe Tang and Luo Liu

Remote Sens. 2022, 14(15), 3562; https://doi.org/10.3390/rs14153562 - 25 Jul 2022

Cited by 8 | Viewed by 2748

Abstract

Accurate mapping of built-up land is essential for urbanization monitoring and ecosystem research. At present, remote sensing is one of the primary means used for real-time and accurate surveying and mapping of built-up land, due to the long time series and multi-information advantages of existing remote sensing images and the ability to obtain highly precise year-by-year built-up land maps. In this study, we obtained feature-enhanced data regarding built-up land from Landsat images and phenology-based algorithms and proposed a method that combines the use of the Google Earth Engine (GEE) and deep learning approaches. The Res-UNet++ structural model was improved for built-up land mapping in Guangdong from 1991 to 2020. Experiments show that overall accuracy of built-up land map in the study area in 2020 was 0.99, the kappa coefficient was 0.96, user accuracy of built-up land was 0.98, and producer accuracy was 0.901. The trained model can be applied to other years with good results. The overall accuracy (OA) of the assessment results every five years was above 0.97, and the kappa coefficient was above 0.90. From 1991 to 2020, built-up land in Guangdong has expanded significantly, the area of built-up land has increased by 71%, and the proportion of built-up land has increased by 3.91%. Our findings indicate that the combined approach of GEE and deep learning algorithms can be developed into a large-scale, long time-series of remote sensing classification techniques framework that can be useful for future land-use mapping research. Full article

(This article belongs to the Special Issue Remote Sensing of Interaction between Human and Natural Ecosystem)

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19 pages, 41576 KiB

Open AccessArticle

Identifying Spatiotemporal Heterogeneity of PM_2.5 Concentrations and the Key Influencing Factors in the Middle and Lower Reaches of the Yellow River

by Hongbo Zhao, Yaxin Liu, Tianshun Gu, Hui Zheng, Zheye Wang and Dongyang Yang

Remote Sens. 2022, 14(11), 2643; https://doi.org/10.3390/rs14112643 - 31 May 2022

Cited by 13 | Viewed by 2424

Abstract

Fine particulate matter (PM_2.5) is a harmful air pollutant that seriously affects public health and sustainable urban development. Previous studies analyzed the spatial pattern and driving factors of PM_2.5 concentrations in different regions. However, the spatiotemporal heterogeneity of various influencing factors on PM_2.5 was ignored. This study applies the geographically and temporally weighted regression (GTWR) model and geographic information system (GIS) analysis methods to investigate the spatiotemporal heterogeneity of PM_2.5 concentrations and the influencing factors in the middle and lower reaches of the Yellow River from 2000 to 2017. The findings indicate that: (1) the annual average of PM_2.5 concentrations in the middle and lower reaches of the Yellow River show an overall trend of first rising and then decreasing from 2000 to 2017. In addition, there are significant differences in inter-province PM_2.5 pollution in the study area, the PM_2.5 concentrations of Tianjin City, Shandong Province, and Henan Province were far higher than the overall mean value of the study area. (2) PM_2.5 concentrations in western cities showed a declining trend, while it had a gradually rising trend in the middle and eastern cities of the study area. Meanwhile, the PM_2.5 pollution showed the characteristics of path dependence and region locking. (3) the PM_2.5 concentrations had significant spatial agglomeration characteristics from 2000 to 2017. The “High-High (H-H)” clusters were mainly concentrated in the southern Hebei Province and the northern Henan Province, and the “Low-Low (L-L)” clusters were concentrated in northwest marginal cities in the study area. (4) The influencing factors of PM_2.5 have significant spatiotemporal non-stationary characteristics, and there are obvious differences in the direction and intensity of socio-economic and natural factors. Overall, the variable of temperature is one of the most important natural conditions to play a positive impact on PM_2.5, while elevation makes a strong negative impact on PM_2.5. Car ownership and population density are the main socio-economic influencing factors which make a positive effect on PM_2.5, while the variable of foreign direct investment (FDI) plays a strong negative effect on PM_2.5. The results of this study are useful for understanding the spatiotemporal distribution characteristics of PM_2.5 concentrations and formulating policies to alleviate haze pollution by policymakers in the Yellow River Basin. Full article

(This article belongs to the Special Issue Remote Sensing of Interaction between Human and Natural Ecosystem)

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16 pages, 13994 KiB

Open AccessTechnical Note

Marine Oil Spill Detection with X-Band Shipborne Radar Using GLCM, SVM and FCM

by Bo Li, Jin Xu, Xinxiang Pan, Long Ma, Zhiqiang Zhao, Rong Chen, Qiao Liu and Haixia Wang

Remote Sens. 2022, 14(15), 3715; https://doi.org/10.3390/rs14153715 - 3 Aug 2022

Cited by 15 | Viewed by 2913

Abstract

Marine oil spills have a significant adverse impact on the economy, ecology, and human health. Rapid and effective oil spill monitoring action is extraordinarily important for controlling marine pollution. A marine oil spill detection scheme based on X-band shipborne radar image with machine learning is proposed here. First, the original shipborne radar image collected on Dalian 7.16 oil spill accident was transformed into a Cartesian coordinate system and noise suppressed. Then, texture features and SVM were used to indicate the effective monitoring location of ocean waves. Third, FCM was applied to classify the oil films and ocean waves. Finally, the oil spill detection result was transformed back to a polar coordinate system. Compared with an improved active contour model and another oil spill detection method with SVM, our method performed more intelligently. It can provide data support for marine oil spill emergency response. Full article

(This article belongs to the Special Issue Remote Sensing of Interaction between Human and Natural Ecosystem)

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