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School of Artificial Intelligence, Shenzhen Polytechnic, Shenzhen 518055, China
School of Urban Design, Wuhan University, Wuhan 430072, China
MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University, Shenzhen 518060, China
Dr. Ivan Lizaga
Isotope Bioscience Laboratory - ISOFYS, Department of Green Chemistry and Technology, Ghent University, Coupure Links 653, 9000 Ghent, Belgium
Dr. Zipeng Zhang
College of Geographical and Remote Sciences, Xinjiang University, Urumqi 830017, China
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Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications

Abstract submission deadline
31 July 2024
Manuscript submission deadline
31 October 2024
Viewed by
22225

Topic Information

Dear Colleagues,

The geographic environment is a complex concept encompassing various natural elements of the Earth's surface and human activities. Natural conditions such as climate, land, and rivers are fundamental to human beings' emergence, survival, and development. Conversely, human activities also significantly impact the geographic environment. The interaction and mutual influence between natural conditions and human activities constitute the geographic environment of the Earth. However, previous research on geographic environment monitoring has often focused on specific objects and spatial and/or temporal scales, making it difficult to comprehensively understand the distinctive characteristics, shifts, and interconnections within the geographic environment. Currently, field surveys, monitoring stations, sensor networks, multisource remote sensing (satellite, airborne, and ground-based), geospatial big data, and especially the development of remote sensing technology and geographic environment monitoring networks enable the observation of multidimensional and multiscale geographic environmental conditions over extended periods, high frequencies, and multiple scales. Through multiscale monitoring, geographic environmental data can be obtained from global to local and macro- to microscales. Integrated data analysis from different scales can lead to a better understanding of the geographic environment's overall characteristics and changing patterns. Diversifying geographic environment monitoring methods has expanded the depth, breadth, and accuracy of geographic process simulation and analysis. Geographic environmental monitoring has a wide range of objects and scientific application scenarios, such as ecosystem services, natural resource distribution, water resource management, climate change research, disaster monitoring, and environmental protection. In summary, research on multiscale geographic environmental elements is of great significance for deepening the understanding of the complexity of the Earth system, predicting environmental changes, supporting sustainable development, and promoting interdisciplinary communication and collaboration. Therefore, this topic aims to collect innovative original manuscripts on the theoretical, methodological, and applied aspects of multiscale geographic environmental monitoring. In addition, review articles and meta-analysis papers on these topics are also welcome.

  • Scale effects of spatial heterogeneity of geographic environmental elements;
  • Spatiotemporal analysis of geographic environmental elements;
  • Theories, technologies, and methods of geographic environmental monitoring;
  • Assessment of ecosystem services;
  • Land surface process simulation;
  • Scale dependence and threshold effects in geographic environmental research;
  • Theories, systems, and methods of geographic environmental evaluation;
  • Fusion and scale conversion of multisource heterogeneous data products;
  • Uncertainty in the monitoring of geographic environmental elements.

Dr. Jingzhe Wang
Dr. Yangyi Wu
Dr. Yinghui Zhang
Dr. Ivan Lizaga
Dr. Zipeng Zhang
Topic Editors

Keywords

  • geographic environmental elements
  • multiscale observation
  • spatiotemporal analysis
  • geographic environmental simulation
  • scale effects
  • uncertainty analysis
  • sustainable development goals

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Climate
climate 		3.7 5.2 2013 19.7 Days CHF 1800 Submit
Drones
drones 		4.8 6.1 2017 17.9 Days CHF 2600 Submit
Forests
forests 		2.9 4.5 2010 16.9 Days CHF 2600 Submit
Land
land 		3.9 3.7 2012 14.8 Days CHF 2600 Submit
Remote Sensing
remotesensing 		5.0 7.9 2009 23 Days CHF 2700 Submit
ISPRS International Journal of Geo-Information
ijgi 		3.4 6.2 2012 35.5 Days CHF 1700 Submit

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Published Papers (18 papers)

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16 pages, 4480 KiB  
Article
Toward a More Robust Estimation of Forest Biomass Carbon Stock and Carbon Sink in Mountainous Region: A Case Study in Tibet, China
by Guanting Lyu, Xiaoyi Wang, Xieqin Huang, Jinfeng Xu, Siyu Li, Guishan Cui and Huabing Huang
Remote Sens. 2024, 16(9), 1481; https://doi.org/10.3390/rs16091481 - 23 Apr 2024
Viewed by 375
Abstract
Mountainous forests are pivotal in the global carbon cycle, serving as substantial reservoirs and sinks of carbon. However, generating a reliable estimate remains a considerable challenge, primarily due to the lack of representative in situ measurements and proper methods capable of addressing their [...] Read more.
Mountainous forests are pivotal in the global carbon cycle, serving as substantial reservoirs and sinks of carbon. However, generating a reliable estimate remains a considerable challenge, primarily due to the lack of representative in situ measurements and proper methods capable of addressing their complex spatial variation. Here, we proposed a deep learning-based method that combines Residual convolutional neural networks (ResNet) with in situ measurements, microwave (Sentinel-1 and VOD), and optical data (Sentinel-2 and Landsat) to estimate forest biomass and track its change over the mountainous regions. Our approach, integrating in situ measurements across representative elevations with multi-source remote sensing images, significantly improves the accuracy of biomass estimation in Tibet’s complex mountainous forests (R2 = 0.80, root mean squared error = 15.8 MgC ha−1). Moreover, ResNet, which addresses the vanishing gradient problem in deep neural networks by introducing skip connections, enables the extraction of complex spatial patterns from limited datasets, outperforming traditional optical-based or pixel-based methods. The mean value of forest biomass was estimated as 162.8 ± 21.3 MgC ha−1, notably higher than that of forests at comparable latitudes or flat regions in China. Additionally, our findings revealed a substantial forest biomass carbon sink of 3.35 TgC year−1 during 2015–2020, which is largely underestimated by previous estimates, mainly due to the underestimation of mountainous carbon stock. The significant carbon density, combined with the underestimated carbon sink in mountainous regions, emphasizes the urgent need to reassess mountain forests to better approximate the global carbon budget. Full article
(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
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23 pages, 10293 KiB  
Article
Spatial-Temporal Evolution Characteristics and Driving Force Analysis of NDVI in Hubei Province, China, from 2000 to 2022
by Peng Chen, Hongzhong Pan, Yaohui Xu, Wenxiang He and Huaming Yao
Forests 2024, 15(4), 719; https://doi.org/10.3390/f15040719 - 19 Apr 2024
Viewed by 460
Abstract
Exploring the characteristics of vegetation dynamics and quantitatively analyzing the potential drivers and the strength of their interactions are of great significance to regional ecological environmental protection and sustainable development. Therefore, based on the 2000–2022 MODIS NDVI dataset, supplemented by climatic, topographic, surface [...] Read more.
Exploring the characteristics of vegetation dynamics and quantitatively analyzing the potential drivers and the strength of their interactions are of great significance to regional ecological environmental protection and sustainable development. Therefore, based on the 2000–2022 MODIS NDVI dataset, supplemented by climatic, topographic, surface cover, and anthropogenic data for the same period, the Sen+Mann–Kendall trend analysis, coefficient of variation, and Hurst exponent were employed to examine the spatial and temporal characteristics and trends of NDVI in Hubei Province, and a partial correlation analysis and geographical detector were used to explore the strength of the influence of driving factors on the spatial differentiation of NDVI in vegetation and the underlying mechanisms of interaction. The results showed that (1) the mean NDVI value of vegetation in Hubei Province was 0.762 over 23 years, with an overall increasing trend and fluctuating upward at a rate of 0.01/10a (p < 0.005); geospatially, there is a pattern of “low east and high west”; the spatial change in NDVI shows a trend of “large-scale improvement in the surrounding hills and mountains and small-scale degradation in the middle plains”; it also presents the spatial fluctuation characteristics of “uniform distribution in general, an obvious difference between urban and rural areas, and a high fluctuation of rivers and reservoirs”, (2) the future trend of NDVI in 70.76% of the region in Hubei Province is likely to maintain the same trend as that of the 2000–2022 period, with 70.78% of the future development being benign and dominated by sustained improvement, and (3) a combination of partial correlation analysis and geographical detector analysis of the drivers of vegetation NDVI change shows that land cover type and soil type are the main drivers; the interactions affecting the distribution and change characteristics of NDVI vegetation all showed two-factor enhancement or nonlinear enhancement relationships. This study contributes to a better understanding of the change mechanisms in vegetation NDVI in Hubei Province, providing support for differentiated ecological protection and project implementation. Full article
(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
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15 pages, 7440 KiB  
Article
Characteristics of Foehn Wind in Urumqi, China, and Their Relationship with EI Niño and Extreme Heat Events in the Last 15 Years
by Maoling Ayitikan, Xia Li, Yusufu Musha, Qing He, Shuting Li, Yuting Zhong and Kai Cheng
Climate 2024, 12(4), 56; https://doi.org/10.3390/cli12040056 - 19 Apr 2024
Viewed by 431
Abstract
Dry and hot Foehn wind weather often occurs in Urumqi, China, due to its canyon terrain. This directly impacts the lives and health of local people. Using surface meteorological variables (including the hourly wind, temperature, humidity, and pressure) measured in situ at the [...] Read more.
Dry and hot Foehn wind weather often occurs in Urumqi, China, due to its canyon terrain. This directly impacts the lives and health of local people. Using surface meteorological variables (including the hourly wind, temperature, humidity, and pressure) measured in situ at the Urumqi Meteorological Station and ERA5 reanalysis from the European Centre for Medium-Range Weather Forecasts in the past 15 years (2008–2022), the characteristics of Foehn wind and their relationship with EI Niño and extreme high-temperature events in Urumqi are analyzed. The results show that the annual distributions of Foehn wind present a fluctuating pattern, and the highest frequency occurred in 2015. Compared to the summer (July) and winter (February) seasons, Foehn wind occurs most frequently in spring (March, April, May) and autumn (September, October, and November). Daily variations in Foehn wind occur most frequently from 9:00 a.m. to 14:00 p.m. In particular, high levels are found at 10:00 a.m. and 11:00 a.m. in April and May. In 2011, 2012, and 2014, the average wind speed of FW exceeded 6 m/s, and the lowest average wind speed was 3.8 m/s in 2021. The temperature and relative humidity changes (ΔT and ΔRH) caused by Foehn wind are the most significant in winter and when Foehn wind begins to occur. The high-temperature hours related to Foehn wind weather in Urumqi represented 25% of the total in the past 15 years. During the EI Niño period, the amount of Foehn wind in Urumqi significantly increased; The correlation coefficient beteewn slide anomaly of Foehn days and the Oceanic Niño Index is as high as 0.71. Specifically, Foehn wind activity aggravates extreme high-temperature events. This study provides indications for Foehn wind weather forecasting in Urumqi. Full article
(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
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18 pages, 8566 KiB  
Article
The Impact of Urbanization on Spatial–Temporal Variation in Vegetation Phenology: A Case Study of the Yangtze River Delta, China
by Enyan Zhu, Dan Fang, Lisu Chen, Youyou Qu and Tao Liu
Remote Sens. 2024, 16(5), 914; https://doi.org/10.3390/rs16050914 - 05 Mar 2024
Viewed by 577
Abstract
The response of vegetation phenology to urbanization has become a growing concern. As impervious surfaces change as urbanization advances, the variation in vegetation phenology at the dynamic urbanization level was analyzed to significantly quantify the impact of urbanization processes on vegetation phenology. Based [...] Read more.
The response of vegetation phenology to urbanization has become a growing concern. As impervious surfaces change as urbanization advances, the variation in vegetation phenology at the dynamic urbanization level was analyzed to significantly quantify the impact of urbanization processes on vegetation phenology. Based on the MOD13Q1 vegetation index product from 2001 to 2020, vegetation phenology parameters, including the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (GSL), were extracted, and the spatial–temporal variation in vegetation phenology, as well as its response to urbanization, was comprehensively analyzed. The results reveal that (1) from 2001 to 2020, the average rates of change for the SOS, EOS, and GSL were 0.41, 0.16, and 0.57 days, respectively. (2) The vegetation phenology changes showed significant spatial–temporal differences at the urbanization level. With each 10% increase in the urbanization level, the SOS and EOS were advanced and delayed by 0.38 and 0.34 days, respectively. (3) The urban thermal environment was a major factor in the impact of urbanization on the SOS and EOS. Overall, this study elucidated the dynamic reflection of urbanization in phenology and revealed the complex effects of urbanization on vegetation phenology, thus helping policymakers to develop effective strategies to improve urban ecological management. Full article
(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
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19 pages, 10631 KiB  
Article
Improving the Detection Effect of Long-Baseline Lightning Location Networks Using PCA and Waveform Cross-Correlation Methods
by Ting Zhang, Jiaquan Wang, Qiming Ma and Liping Fu
Remote Sens. 2024, 16(5), 885; https://doi.org/10.3390/rs16050885 - 02 Mar 2024
Cited by 1 | Viewed by 523
Abstract
Ultra-long-distance and high-precision lightning location technology is an important means to realize low-cost and wide-area lightning detection. This paper carried out research on the high-precision location technology of very-low-frequency (VLF) lightning electromagnetic pulse based on the Asia-Pacific Lightning Location Network (APLLN) deployed in [...] Read more.
Ultra-long-distance and high-precision lightning location technology is an important means to realize low-cost and wide-area lightning detection. This paper carried out research on the high-precision location technology of very-low-frequency (VLF) lightning electromagnetic pulse based on the Asia-Pacific Lightning Location Network (APLLN) deployed in 2018. Two key technologies are proposed in this paper: one is the calculation method of signal arrival time using very-low-frequency lightning electromagnetic pulse waveform, and the other is the compression transmission technology of lightning electromagnetic pulse waveform based on a signal principal component analysis. The results of a comparison and evaluation of the improved APLLN with the ADTD system show that the APLLN has a relative location efficiency of 69.1% and an average location error within the network of 4.5 km. Full article
(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
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17 pages, 20307 KiB  
Article
Analysis of Changes in Ecological Environment Quality and Influencing Factors in Chongqing Based on a Remote-Sensing Ecological Index Mode
by Yizhuo Liu, Tinggang Zhou and Wenping Yu
Land 2024, 13(2), 227; https://doi.org/10.3390/land13020227 - 12 Feb 2024
Cited by 1 | Viewed by 884
Abstract
Chongqing is a large municipality in southwestern China, having the characteristics of a vast jurisdiction, complex topography, and a prominent dual urban–rural structure. It is vitally important to optimize the spatial layout of land and efficiency of natural resource allocation, achieve sustainable development, [...] Read more.
Chongqing is a large municipality in southwestern China, having the characteristics of a vast jurisdiction, complex topography, and a prominent dual urban–rural structure. It is vitally important to optimize the spatial layout of land and efficiency of natural resource allocation, achieve sustainable development, and conduct influence assessment and causation analysis in this region. Here, using the Google Earth Engine platform, we selected Landsat remote-sensing (RS) images from the period 2000–2020 and constructed a remote-sensing ecological index (RSEI) model. Considering the urban spatial pattern division in Chongqing, the Sen + Mann–Kendall analytical approach was employed to assess the fluctuating quality of the ecological environment in different sectors of Chongqing. Subsequently, single-factor and interaction detectors in the Geodetector software tool were used to conduct causation analysis on the RSEI, with the use of eight elements: elevation, slope, aspect, precipitation, temperature, population, land use, and nighttime lighting. Findings indicate that, over the course of the investigation period, the eco-quality in Chongqing displayed a pattern of degradation, succeeded by amelioration. The RSEI decreased from 0.700 in 2000 to 0.590 in 2007, and then gradually recovered to 0.716 in 2018. Overall, the eco-environment quality of Chongqing improved. Spatially, changes in the RSEI were consistent with the planning and positioning of the urban spatial pattern. The main new urban area and periphery of the central urban area showed a slight deterioration, while other regions showed marked improvement. The combined effect of any two elements enhanced the explanatory power of a single factor, with elevation, temperature, and land use being the strongest explanatory elements of eco-quality in Chongqing. The most influential factor explaining the spatial variation of the RSEI was determined to be the combined impact of elevation and land use. At the temporal scale, elements related to human activities showed the most evident trend in explanatory power. Full article
(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
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16 pages, 2249 KiB  
Article
Monitoring Soil Salinity Classes through Remote Sensing-Based Ensemble Learning Concept: Considering Scale Effects
by Huifang Chen, Jingwei Wu and Chi Xu
Remote Sens. 2024, 16(4), 642; https://doi.org/10.3390/rs16040642 - 09 Feb 2024
Viewed by 940
Abstract
Remote sensing (RS) technology can rapidly obtain spatial distribution information on soil salinization. However, (1) the scale effects resulting from the mismatch between ground-based “point” salinity data and remote sensing pixel-based “spatial” data often limit the accuracy of remote sensing monitoring of soil [...] Read more.
Remote sensing (RS) technology can rapidly obtain spatial distribution information on soil salinization. However, (1) the scale effects resulting from the mismatch between ground-based “point” salinity data and remote sensing pixel-based “spatial” data often limit the accuracy of remote sensing monitoring of soil salinity, and (2) the same salinity RS monitoring model usually provides inconsistent or sometimes conflicting explanations for different data. Therefore, based on Landsat 8 imagery and synchronously collected ground-sampling data of two typical study regions (denoted as N and S, respectively) of the Yichang Irrigation Area in the Hetao Irrigation District for May 2013, this study used geostatistical methods to obtain “relative truth values” of salinity corresponding to the Landsat 8 pixel scale. Additionally, based on Landsat 8 multispectral data, 14 salinity indices were constructed. Subsequently, the Correlation-based Feature Selection (CFS) method was used to select sensitive features, and a strategy similar to the concept of ensemble learning (EL) was adopted to integrate the single-feature-sensitive Bayesian classification (BC) model in order to construct an RS monitoring model for soil salinization (Nonsaline, Slightly saline, Moderately saline, Strongly saline, and Solonchak). The research results indicated that (1) soil salinity exhibits moderate to strong variability within a 30 m scale, and the spatial heterogeneity of soil salinity needs to be considered when developing remote sensing models; (2) the theoretical models of salinity variance functions in the N and S regions conform to the exponential model and the spherical model, with R2 values of 0.817 and 0.967, respectively, indicating a good fit for the variance characteristics of salinity and suitability for Kriging interpolation; and (3) compared to a single-feature BC model, the soil salinization identification model constructed using the concept of EL demonstrated better potential for robustness and effectiveness. Full article
(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
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20 pages, 14108 KiB  
Article
Machine-Learning-Assisted Characterization of Regional Heat Islands with a Spatial Extent Larger than the Urban Size
by Yin Du, Zhiqing Xie, Lingling Zhang, Ning Wang, Min Wang and Jingwen Hu
Remote Sens. 2024, 16(3), 599; https://doi.org/10.3390/rs16030599 - 05 Feb 2024
Cited by 1 | Viewed by 939
Abstract
Surface urban heat islands (SUHIs) can extend beyond the urban boundaries and greatly affect the thermal environment of continuous regions over an agglomeration. Traditional urban-rural dichotomy depending on the built-up and non-urban lands is challenged in characterizing regional SUHIs, such as how to [...] Read more.
Surface urban heat islands (SUHIs) can extend beyond the urban boundaries and greatly affect the thermal environment of continuous regions over an agglomeration. Traditional urban-rural dichotomy depending on the built-up and non-urban lands is challenged in characterizing regional SUHIs, such as how to accurately quantify the intensity, spatial pattern, and scales of SUHIs, which are vulnerable to SUHIs, and what the optimal scale for conducting measures to mitigate the SUHIs. We propose a machine-learning-assisted solution to address these problems based on the thermal similarity in the Yangtze River Delta of China. We first identified the regional-level SUHI zone of approximately 42,328 km2 and 38,884 km2 and the areas that have no SUHI effects from the annual cycle of land surface temperatures (LSTs) retrieved from Terra and Aqua satellites. Defining SUHI as an anomaly on background condition, random forest (RF) models were further adopted to fit the LSTs in the areas without the SUHI effects and estimate the LST background and SUHI intensity at each grid point in the SUHI zone. The RF models performed well in fitting rural LSTs with a simulation error of approximately 0.31 °C/0.44 °C for Terra/Aqua satellite data and showed a good generalization ability in estimating the urban LST background. The RF-estimated daytime Aqua/SUHI intensity peaked at approximately 6.20 °C in August, and the Terra/SUHI intensity had two peaks of approximately 3.18 and 3.81 °C in May and August, with summertime RF-estimated SUHIs being more reliable than other SUHI types owing to the smaller simulation error of less than 1.0 °C in July–September. This machine-learning-assisted solution identified an optimal SUHI scale of 30,636 km2 and a zone of approximately 23,631 km2 that is vulnerable to SUHIs, and it provided the SUHI intensity and statistical reliability for each grid point identified as being part of the SUHI. Urban planners and decision-makers can focus on the statistically reliable RF-estimated summertime intensities in SUHI zones that have an LST annual cycle similar to that of large cities in developing effective strategies for mitigating adverse SUHI effects. In addition, the selection of large cities might strongly affect the accuracy of identifying the SUHI zone, which is defined as the areas that have an LST annual cycle similar to large cities. Water bodies might reduce the RF performance in estimating the LST background over urban agglomerations. Full article
(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
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21 pages, 7054 KiB  
Article
Analysis and Prediction of Spatial and Temporal Evolution of Ecosystem Service Value on the Northern Slopes of the Kunlun Mountains Based on Land Use
by Zhichao Zhang, Yang Wang, Haisheng Tang and Zhen Zhu
Land 2023, 12(12), 2123; https://doi.org/10.3390/land12122123 - 30 Nov 2023
Viewed by 760
Abstract
The ecological environment in the mountainous areas of southern Xinjiang is very sensitive and fragile, and identifying the ecological asset retention within the mountainous areas is a top priority at the current stage in the context of comprehensive environmental management in arid zones. [...] Read more.
The ecological environment in the mountainous areas of southern Xinjiang is very sensitive and fragile, and identifying the ecological asset retention within the mountainous areas is a top priority at the current stage in the context of comprehensive environmental management in arid zones. This study examines the conversion and ecosystem service values between different land types within the mountainous areas based on a time series of land-use data from 1990 to 2020, and the results show that: (1) The value of ecosystem services on the northern slopes of the Kunlun Mountains shows an overall increasing trend. It increased from CNY 308.645 billion in 1990 to CNY 326.550 billion in 2020. Among them, the value of ecosystem services increased significantly between 2000 and 2010, with an increase of CNY 39.857 billion. Regulatory services accounted for more than 66% of the value of each ecosystem service. (2) Land use on the northern slopes of the Kunlun Mountains has changed significantly since 1990. The areas of cropland, forest land, grassland, watershed, and construction land have all shown an upward trend, with the greatest increase in construction land. The area of unutilized land, on the other hand, has slightly decreased. (3) The value of ecosystem services within the northern slopes of the Kunlun Mountains was spatially high in the south, low in the north, and higher in the west than in the east. The study also found a significant positive spatial correlation between ecosystem service values. In the spatial distribution, the increasing areas were mainly distributed in the southeast, and the decreasing areas were in the north. Changes in land types are expected to include an increase in the area of grassland and woodland, a decrease in unutilized land and cropland, and an overall improvement in the ecological environment of the northern slopes of the Kunlun Mountains in the next decade. This study also provides lessons and references for sustainable development and ecological protection in ecologically fragile regions. Full article
(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
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25 pages, 6814 KiB  
Article
Assessing the Effects of Subjective and Objective Measures on Housing Prices with Street View Imagery: A Case Study of Suzhou
by Jin Zhu, Yao Gong, Changchang Liu, Jinglong Du, Ci Song, Jie Chen and Tao Pei
Land 2023, 12(12), 2095; https://doi.org/10.3390/land12122095 - 22 Nov 2023
Cited by 1 | Viewed by 1076
Abstract
The price of a house is affected by both the subjective and objective factors of the street environment in a neighborhood. However, the relationships between these factors and housing prices are not fully understood. Street view imagery (SVI) has recently emerged as a [...] Read more.
The price of a house is affected by both the subjective and objective factors of the street environment in a neighborhood. However, the relationships between these factors and housing prices are not fully understood. Street view imagery (SVI) has recently emerged as a new data source for housing price studies. The SVI contains both objective and subjective information and can be used to extract objective measurements describing the physical environment and subjective measurements depicting human perceptions. Compared to conventional methods, there is consistency between subjective and objective information extracted from SVIs, and the two types of information are acquired from the perspective of the human visual perceptual system. Therefore, using both objective and subjective information extracted from street view images to study their relationship with housing prices has several advantages. In this study, focusing on the city of Suzhou, China, we extracted subjective perception and objective view indices from SVIs and systematically assessed their effects on housing prices. The global ordinary least squares (OLS) regression model and the local geographically weighted regression (GWR) model were used to model the correlations between these measures and housing prices. The OLS reveals that overall objective measures have stronger explanatory power, and built environment factors have a greater impact on housing prices. GWR shows that subjective factors can explain more variance in housing prices on the local scale and that home buyers care more about the subjective perceptions of the neighborhood’s surroundings. The map of the GWR local coefficients demonstrates that the perception indicators have both positive and negative effects on housing prices in different places. In addition, a Monte Carlo test was performed to verify the spatially varying relationships between these measures. Our findings provide important references for urban designers and guide various applications, such as safe neighborhood design and sustainable city planning. Full article
(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
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16 pages, 10830 KiB  
Technical Note
Dynamics of Spring Snow Cover Variability over Northeast China
by Taotao Zhang and Xiaoyi Wang
Remote Sens. 2023, 15(22), 5330; https://doi.org/10.3390/rs15225330 - 12 Nov 2023
Viewed by 816
Abstract
Spring snow cover variability over Northeast China (NEC) has a profound influence on the local grain yield and even the food security of the country, but its drivers remain unclear. In the present study, we investigated the spatiotemporal features and the underlying mechanisms [...] Read more.
Spring snow cover variability over Northeast China (NEC) has a profound influence on the local grain yield and even the food security of the country, but its drivers remain unclear. In the present study, we investigated the spatiotemporal features and the underlying mechanisms of spring snow cover variability over NEC during 1983–2018 based on the satellite-derived snow cover data and atmospheric reanalysis products. The empirical orthogonal function (EOF) analysis showed that the first EOF mode (EOF1) explains about 50% of the total variances and characterizes a coherent snow cover variability pattern over NEC. Further analyses suggested that the formation of the EOF1 mode is jointly affected by the atmospheric internal variability and the sea surface temperature (SST) anomaly at the interannual timescale. Specifically, following a negative phase of the atmospheric teleconnection of the Polar–Eurasian pattern, a prominent cyclonic circulation appears over NEC, which increases the snowfall over the east of NEC by enhancing the water vapor transport and decreases the air temperature through reducing the solar radiation and intensifying the cold advection. As a result, the snow cover has increased over NEC. Additionally, the tripole structure of the North Atlantic spring SST anomaly could excite a wave-train-type anomalous circulation propagating to NEC that further regulates the snow cover variability by altering the atmospheric dynamic and thermodynamic conditions and the resultant air temperature and snowfall. Our results have important implications on the understanding of the spring snow cover anomaly over NEC and the formulation of the local agricultural production plan. Full article
(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
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19 pages, 26073 KiB  
Article
Influence of Climate, Topography, and Hydrology on Vegetation Distribution Patterns—Oasis in the Taklamakan Desert Hinterland
by Lei Peng, Yanbo Wan, Haobo Shi, Abudureyimu Anwaier and Qingdong Shi
Remote Sens. 2023, 15(22), 5299; https://doi.org/10.3390/rs15225299 - 09 Nov 2023
Viewed by 1233
Abstract
Vegetation in natural desert hinterland oases is an important component of terrestrial ecosystems. Determining how desert vegetation responds to natural variability is critical for a better understanding of desertification processes and their future development. The aim of this study is to characterize the [...] Read more.
Vegetation in natural desert hinterland oases is an important component of terrestrial ecosystems. Determining how desert vegetation responds to natural variability is critical for a better understanding of desertification processes and their future development. The aim of this study is to characterize the spatial distribution of vegetation in the natural desert hinterland and to reveal how different environmental factors affect vegetation changes. Taking a Taklamakan Desert hinterland oasis as our research object, we analyzed the effects of different environmental factors on desert vegetation using a time-series normalized difference vegetation index (NDVI) combined with meteorological, topographic, and hydrological data, including surface water and groundwater data. Vegetation was distributed in areas with high surface water frequency, shallow groundwater levels, relatively flat terrain, and dune basins. NDVI datasets show greening trends in oasis areas over the past 20 years. The frequency of surface water distribution influences water accessibility and effectiveness and shapes topography, thus affecting the spatial distribution pattern of vegetation. In this study, areas of high surface water frequency corresponded with vegetation distribution. The spatial distribution of groundwater depth supports the growth and development of vegetation, impacting the pattern of vegetation growth conditions. Vegetation is most widely distributed in areas where the groundwater burial depth is 3.5–4.5 m. This study provides data for restoring riparian vegetation, ecological water transfer, and sustainable development. Full article
(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
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17 pages, 2374 KiB  
Article
Production–Living–Ecological Spatial Function Identification and Pattern Analysis Based on Multi-Source Geographic Data and Machine Learning
by Ziqiang Bu, Jingying Fu, Dong Jiang and Gang Lin
Land 2023, 12(11), 2029; https://doi.org/10.3390/land12112029 - 07 Nov 2023
Cited by 1 | Viewed by 920
Abstract
Land use cannot be simply understood as land cover. The same land may carry different functions, such as production, living, and ecological applications; the dominant function of land will affect and restrict other uses. Disorderly urbanization and industrialization have led to an intensification [...] Read more.
Land use cannot be simply understood as land cover. The same land may carry different functions, such as production, living, and ecological applications; the dominant function of land will affect and restrict other uses. Disorderly urbanization and industrialization have led to an intensification of conflicts among the production, living, and ecological functions of land, which is a major constraint on regional sustainable development. This paper took the perspective of land-use function and used multi-source data such as Sentinel remote-sensing imagery, VIIRS night-time light data, and POIs to classify land-use functions on a large scale in the Beijing–Tianjin–Hebei (BTH) urban agglomeration. The specific research process was as follows. Firstly, the BTH region was multi-scale-segmented based on Sentinel remote-sensing data. Then, the spectral, texture, shape, and socio-economic features of each small area after segmentation were extracted. Moreover, a PLES land-use classification system oriented towards land-use function was established, and a series of representative samples were selected. Subsequently, a random forest model was trained using these samples; then, the trained model was used for the large-scale analysis of land use in the entire BTH region. Finally, the spatial distribution patterns and temporal–spatial evolution characteristics of PLES in the BTH region from 2016 to 2021 were analyzed from the macro level to the micro level. Full article
(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
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14 pages, 3640 KiB  
Article
Homogenous Climatic Regions for Targeting Green Water Management Technologies in the Abbay Basin, Ethiopia
by Degefie Tibebe, Mekonnen Adnew Degefu, Woldeamlak Bewket, Ermias Teferi, Greg O’Donnell and Claire Walsh
Climate 2023, 11(10), 212; https://doi.org/10.3390/cli11100212 - 23 Oct 2023
Viewed by 1751
Abstract
Spatiotemporal climate variability is a leading environmental constraint to the rain-fed agricultural productivity and food security of communities in the Abbay basin and elsewhere in Ethiopia. The previous one-size-fits-all approach to soil and water management technology targeting did not effectively address climate-induced risks [...] Read more.
Spatiotemporal climate variability is a leading environmental constraint to the rain-fed agricultural productivity and food security of communities in the Abbay basin and elsewhere in Ethiopia. The previous one-size-fits-all approach to soil and water management technology targeting did not effectively address climate-induced risks to rain-fed agriculture. This study, therefore, delineates homogenous climatic regions and identifies climate-induced risks to rain-fed agriculture that are important to guide decisions and the selection of site-specific technologies for green water management in the Abbay basin. The k-means spatial clustering method was employed to identify homogenous climatic regions in the study area, while the Elbow method was used to determine an optimal number of climate clusters. The k-means clustering used the Enhancing National Climate Services (ENACTS) daily rainfall, minimum and maximum temperatures, and other derived climate variables that include daily rainfall amount, length of growing period (LGP), rainfall onset and cessation dates, rainfall intensity, temperature, potential evapotranspiration (PET), soil moisture, and AsterDEM to define climate regions. Accordingly, 12 climate clusters or regions were identified and mapped for the basin. Clustering a given geographic region into homogenous climate classes is useful to accurately identify and target locally relevant green water management technologies to effectively address local-scale climate-induced risks. This study also provided a methodological framework that can be used in the other river basins of Ethiopia and, indeed, elsewhere. Full article
(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
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22 pages, 12575 KiB  
Article
Identifying Major Diurnal Patterns and Drivers of Surface Urban Heat Island Intensities across Local Climate Zones
by Yongjuan Guan, Jinling Quan, Ting Ma, Shisong Cao, Chengdong Xu and Jiali Guo
Remote Sens. 2023, 15(20), 5061; https://doi.org/10.3390/rs15205061 - 21 Oct 2023
Cited by 1 | Viewed by 1192
Abstract
Deepening the understanding of diurnal characteristics and driving mechanisms of surface urban heat islands (SUHIs) across different local climate zones (LCZs) and time scales is of great significance for guiding urban surface heat mitigation. However, a comprehensive investigation of SUHIs from the diurnal, [...] Read more.
Deepening the understanding of diurnal characteristics and driving mechanisms of surface urban heat islands (SUHIs) across different local climate zones (LCZs) and time scales is of great significance for guiding urban surface heat mitigation. However, a comprehensive investigation of SUHIs from the diurnal, local, multi-seasonal, and interactive perspectives remains a large gap. Here, we generalized major diurnal patterns of LCZ-based SUHI intensities (SUHIIs) throughout 2020 over the urban area of Beijing, China, based on diurnal temperature cycle modeling, block-level LCZ mapping, and hierarchical clustering. A geographical detector was then employed to explore the individual and interactive impacts of 10 morphological, socioeconomic, and meteorological factors on the multi-temporal spatial differentiations of SUHIIs. Results indicate six prevalent diurnal SUHII patterns with distinct features among built LCZ types. LCZs 4 and 5 (open high- and mid-rise buildings) predominantly display patterns one, two, and five, characterized by an afternoon increase and persistently higher values during the night. Conversely, LCZs 6, 8, and 9 (open, large, and sparsely built low-rise buildings) mainly exhibit patterns three, four, and six, with a decrease in SUHII during the afternoon and lower intensities at night. The maximum/minimum SUHIIs occur in the afternoon–evening/morning for patterns 1–3 but in the morning/afternoon for patterns 5–6. In all four seasons, the enhanced vegetation index (EVI) and gross domestic product (GDP) have the top two individual effects for daytime spatial differentiations of SUHIIs, while the air temperature (TEM) has the largest explanatory power for nighttime differentiations of SUHIIs. All factor interactions are categorized as two-factor or nonlinear enhancements, where nighttime interactions exhibit notably greater explanatory powers than daytime ones. The strongest interactions are EVI ∩ GDP (q = 0.80) during the day and TEM ∩ EVI (q = 0.86) at night. The findings of this study contribute to an improved interpretation of the diurnal continuous dynamics of local SUHIIs in response to various environmental conditions. Full article
(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
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23 pages, 8488 KiB  
Article
Unexpected Expansion of Rare-Earth Element Mining Activities in the Myanmar–China Border Region
by Emmanuel Chinkaka, Julie Michelle Klinger, Kyle Frankel Davis and Federica Bianco
Remote Sens. 2023, 15(18), 4597; https://doi.org/10.3390/rs15184597 - 19 Sep 2023
Cited by 1 | Viewed by 5245
Abstract
Mining for rare earth elements is rapidly increasing, driven by current and projected demands for information and energy technologies. Following China’s Central Government’s 2012 strategy to shift away from mining in favor of value-added processing, primary extraction has increased outside of China. Accordingly, [...] Read more.
Mining for rare earth elements is rapidly increasing, driven by current and projected demands for information and energy technologies. Following China’s Central Government’s 2012 strategy to shift away from mining in favor of value-added processing, primary extraction has increased outside of China. Accordingly, changes in mineral exploitation in China and Myanmar have garnered considerable attention in the past decade. The prevailing assumption is that mining in China has decreased while mining in Myanmar has increased, but the dynamic in border regions is more complex. Our empirical study used Google Earth Engine (GEE) to characterize changes in mining surface footprints between 2005 and 2020 in two rare earth mines located on either side of the Myanmar–China border, within Kachin State in northern Myanmar and Nujiang Prefecture in Yunnan Province in China. Our results show that the extent of the mining activities increased by 130% on China’s side and 327% on Myanmar’s side during the study period. We extracted surface reflectance images from 2005 and 2010 from Landsat 5 TM and 2015 and 2020 images from Landsat 8 OLI. The Normalized Vegetation Index (NDVI) was applied to dense time-series imagery to enhance landcover categories. Random Forest was used to categorize landcover into mine and non-mine classes with an overall accuracy of 98% and a Kappa Coefficient of 0.98, revealing an increase in mining extent of 2.56 km2, covering the spatial mining footprint from 1.22 km2 to 3.78 km2 in 2005 and 2020, respectively, within the study area. We found a continuous decrease in non-mine cover, including vegetation. Both mines are located in areas important to ethnic minority groups, agrarian livelihoods, biodiversity conservation, and regional watersheds. The finding that mining surface areas increased on both sides of the border is significant because it shows that national-level generalizations do not align with local realities, particularly in socially and environmentally sensitive border regions. The quantification of such changes over time can help researchers and policymakers to better understand the shifting geographies and geopolitics of rare earth mining, the environmental dynamics in mining areas, and the particularities of mineral extraction in border regions. Full article
(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
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26 pages, 18934 KiB  
Article
A Comparison of Six Forest Mapping Products in Southeast Asia, Aided by Field Validation Data
by Bin Liu, Xiaomei Yang, Zhihua Wang, Yaxin Ding, Junyao Zhang and Dan Meng
Remote Sens. 2023, 15(18), 4584; https://doi.org/10.3390/rs15184584 - 18 Sep 2023
Cited by 1 | Viewed by 948
Abstract
Currently, many globally accessible forest mapping products can be utilized to monitor and assess the status of and changes in forests. However, substantial disparities exist among these products due to variations in forest definitions, classification methods, and remote sensing data sources. This becomes [...] Read more.
Currently, many globally accessible forest mapping products can be utilized to monitor and assess the status of and changes in forests. However, substantial disparities exist among these products due to variations in forest definitions, classification methods, and remote sensing data sources. This becomes particularly conspicuous in regions characterized by significant deforestation, like Southeast Asia, where forest mapping uncertainty is more pronounced, presenting users with challenges in selecting appropriate datasets across diverse regions. Moreover, this situation impedes the further enhancement of accuracy for forest mapping products. The aim of this research is to assess the consistency and accuracy of six recently produced forest mapping products in Southeast Asia. These products include three 10 m land cover products (Finer Resolution Observation and Monitoring Global LC (FROM-GLC10), ESA WorldCover 10 m 2020 (ESA2020), and ESRI 2020 Land Cover (ESRI2020)) and three forest thematic mapping products (Global PALSAR-2 Forest/Non-Forest map (JAXA FNF2020), global 30 m spatial distribution of forest cover in 2020 (GFC30_2020), and Generated_Hansen2020, which was synthesized based on Hansen TreeCover2010 (Hansen2010) and Hansen Global Forest Change (Hansen GFC) for the year 2020). Firstly, the research compared the area and spatial consistency. Next, accuracy was assessed using field validation points and manual densification points. Finally, the research analyzed the geographical environmental and biophysical factors influencing consistency. The results show that ESRI2020 had the highest overall accuracy for forest, followed by ESA2020, FROM-GLC10, and Generated_Hansen2020. Regions with elevations ranging from 200 to 3000 m and slopes below 15° or above 25° showed high spatial consistency, whereas other regions showed low consistency. Inconsistent regions showed complex landscapes heavily influenced by human activities; these regions are prone to being confused with shrubs and cropland and are also impacted by rubber and oil palm plantations, significantly affecting the accuracy of forest mapping. Based on the research findings, ESRI2020 is recommended for mountainous areas and abundant forest regions. However, in areas significantly affected by human activities, such as forest and non-forest edges and mixed areas of plantations and natural forests, caution should be taken with product selection. The research has identified areas of forest inconsistency that require attention in future forest mapping. To enhance our understanding of forest mapping and generate high-precision forest cover maps, it is recommended to incorporate multi-source data, subdivide forest types, and increase the number of sample points. Full article
(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
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22 pages, 11112 KiB  
Article
An Evaluation of Sun-Glint Correction Methods for UAV-Derived Secchi Depth Estimations in Inland Water Bodies
by Edvinas Tiškus, Martynas Bučas, Diana Vaičiūtė, Jonas Gintauskas and Irma Babrauskienė
Drones 2023, 7(9), 546; https://doi.org/10.3390/drones7090546 - 23 Aug 2023
Cited by 2 | Viewed by 1312
Abstract
This study investigates the application of unoccupied aerial vehicles (UAVs) equipped with a Micasense RedEdge-MX multispectral camera for the estimation of Secchi depth (SD) in inland water bodies. The research analyzed and compared five sun-glint correction methodologies—Hedley, Goodman, Lyzenga, Joyce, and threshold-removed glint—to [...] Read more.
This study investigates the application of unoccupied aerial vehicles (UAVs) equipped with a Micasense RedEdge-MX multispectral camera for the estimation of Secchi depth (SD) in inland water bodies. The research analyzed and compared five sun-glint correction methodologies—Hedley, Goodman, Lyzenga, Joyce, and threshold-removed glint—to model the SD values derived from UAV multispectral imagery, highlighting the role of reflectance accuracy and algorithmic precision in SD modeling. While Goodman’s method showed a higher correlation (0.92) with in situ SD measurements, Hedley’s method exhibited the smallest average deviation (0.65 m), suggesting its potential in water resource management, environmental monitoring, and ecological modeling. The study also underscored the quasi-analytical algorithm (QAA) potential in estimating SD due to its flexibility to process data from various sensors without requiring in situ measurements, offering scalability for large-scale water quality surveys. The accuracy of SD measures calculated using QAA was related to variability in water constituents of colored dissolved organic matter and the solar zenith angle. A practical workflow for SD acquisition using UAVs and multispectral data is proposed for monitoring inland water bodies. Full article
(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
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