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GeoAI and EO Big Data Driven Advances in Earth Environmental Science

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 33665

Special Issue Editors

Dr. Min Huang

E-Mail Website
Guest Editor
School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China
Interests: remote sensing; machine learning; classification; land use land cover; time-series analysis; urban informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Changjiang Xiao

E-Mail Website
Guest Editor
College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
Interests: geospatial big data analysis; spatio-temporal deep learning; time-series modeling and prediction
Prof. Dr. Nengcheng Chen

E-Mail Website
Guest Editor
National Engineering Research Center for Geographic Information System, China University of Geosciences, Wuhan 430074, China
Interests: digital twin; earth observation sensor network; spatio-temporal big data intelligence; geosimulation decision; smart city and smart watershed
Special Issues, Collections and Topics in MDPI journals
Dr. Runze Li

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of California, Irvine, CA 92617, USA
Interests: remote sensing; precipitation; climate change; error modelling; retrieval algorithm improvement
Prof. Dr. Orhan Altan

E-Mail Website1 Website2
Guest Editor
Department of Geomatics, Istanbul Technical University, Istanbul 36626, Turkey
Interests: photogrammetry and remote sensing; geographic information system; hazard and risk management; 3S technology in SDGs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the blowout development of earth observation (EO) technologies (e.g., optical and microwave remote sensing, LiDAR, GNSS, and geospatial sensor web) in recent years, EO data have accumulated quickly to the petabyte-level, which provides the greatest opportunities yet for earth environmental science, but meanwhile pose the grandest challenges yet for the processing of these EO big data. Owing to the development and advancement of Artificial Intelligence (AI), especially Geospatial AI (GeoAI) methods and techniques (e.g., spatiotemporal machine learning and deep learning), the modeling, processing and analysis of the EO big data have arrived at a new paradigm. By integrating the EO big data and the GeoAI methods, more comprehensive and in-depth investigations into earth environmental science become possible.

This Special Issue aims at methodological or applied studies using GeoAI and EO big data for investigating the matter, energy, and information in the hydrosphere, lithosphere, biosphere, and atmosphere on the surface of the Earth. The scale can be local, regional, or global, but large scale and long time-series studies will be preferred. In addition, monitoring and analysis studies of the key thematic indicators for high-impact events or disasters such as droughts, floods, earthquakes, tsunamis, and volcanic eruptions are especially welcome.

Articles may address, but are not limited, to the following topics:

  • Analysis and mining of EO (e.g., optical and microwave remote sensing, LiDAR, GNSS, and geospatial sensor web) big data;
  • Novel GeoAI models and frameworks (e.g., spatiotemporal machine learning/deep learning) for modeling/processing/analyzing of EO big data;
  • Retrievals of environmental variables (e.g., precipitation, land/sea surface temperature, soil moisture, aerosols, vegetation index, sea ice concentration, sea surface salinity, snow cover, chlorophyll-a concentration);
  • Environmental variables monitoring and prediction;
  • Postprocessing of environmental variable retrievals (e.g., multi-source data fusion, downscaling, and image restoration);
  • Extracting information from EO big data (e.g., classification, segmentation, target detection, dynamic monitoring, and prediction);
  • Natural hazards (e.g., drought, flood, waterlogging, wildfire, landslide, surge earthquake, tsunami, and volcanic eruption) monitoring and evaluation;
  • Crop yield estimation;
  • Land cover land use mapping and scenario prediction;
  • Monitoring and analysis of high-impact events (e.g., epidemic outbreaks, oil spills, gas pipeline ruptures, carbon neutrality, and emission peak).

Dr. Min Huang
Dr. Changjiang Xiao
Prof. Dr. Nengcheng Chen
Dr. Runze Li
Prof. Dr. Orhan Altan
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

  • earth observation big data
  • GeoAI
  • multisource/multimodal data fusion
  • long time-series analysis
  • retrievals of environmental variables
  • postprocessing of environmental variables retrievals
  • monitoring, evaluation, and prediction
  • land cover land use
  • natural hazards
  • high-impact events

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  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (16 papers)

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Research

23 pages, 6394 KiB  
Article
Spatiotemporal Dynamics and Prediction of Habitat Quality Based on Land Use and Cover Change in Jiangsu, China
by Ge Shi, Chuang Chen, Qingci Cao, Jingran Zhang, Jinghai Xu, Yu Chen, Yutong Wang and Jiahang Liu
Remote Sens. 2024, 16(22), 4158; https://doi.org/10.3390/rs16224158 - 7 Nov 2024
Viewed by 440
Abstract
Analyzing the spatiotemporal evolution characteristics of urban land use and habitat quality is crucial for the sustainable development of urban ecological environments. This study utilizes the land use data of Jiangsu Province for the years 2000, 2010, and 2020, applying the FLUS model [...] Read more.
Analyzing the spatiotemporal evolution characteristics of urban land use and habitat quality is crucial for the sustainable development of urban ecological environments. This study utilizes the land use data of Jiangsu Province for the years 2000, 2010, and 2020, applying the FLUS model to investigate the driving force behind land expansion and to simulate a prediction for the land use of 2030. By integrating the InVEST model and landscape pattern indices, this study analyzes the spatiotemporal evolution characteristics of habitat quality in Jiangsu Province and uses geographical detector analysis to examine the synergistic effects of influencing factors. The results indicate that, from 2000 to 2020, habitat degradation in Jiangsu Province progressively increased, with the spatial distribution of degradation levels showing a gradual change. Under the ecological protection scenario in 2030, habitat fragmentation was alleviated. Conversely, under the economic development scenario, habitat quality further deteriorated, resulting in the largest area of low-quality regions. Minimal changes occurred under the natural development scenario. (2) The landscape indices in Jiangsu Province experienced significant changes from 2000 to 2020. The continuous expansion of urban land into other land use types led to a trend of fragmentation, with a clear increasing trend in dispersion, sprawl, and Shannon’s diversity index, accompanied by a decrease in cohesion. (3) The dominant interacting factors affecting habitat quality were combinations of socioeconomic factors with other factors, indicating that the economy largely determines the spatial distribution pattern of habitat quality. The findings of this study provide optimization strategies for future spatial planning of land use types in Jiangsu Province and offer references for habitat quality restoration efforts in the region. Full article
(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science)
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18 pages, 11779 KiB  
Article
Exploring Spatial Non-Stationarity and Scale Effects of Natural and Anthropogenic Factors on Net Primary Productivity of Vegetation in the Yellow River Basin
by Xiaolei Wang, Wenxiang He, Yilong Huang, Xing Wu, Xiang Zhang and Baowei Zhang
Remote Sens. 2024, 16(17), 3156; https://doi.org/10.3390/rs16173156 - 27 Aug 2024
Cited by 1 | Viewed by 728
Abstract
Investigating the spatiotemporal dynamics of vegetation net primary productivity (NPP) and its influencing factors are crucial for green and low-carbon development and facilitate human well-being in the Yellow River Basin (YRB). Although the research on NPP has advanced rapidly, in view of the [...] Read more.
Investigating the spatiotemporal dynamics of vegetation net primary productivity (NPP) and its influencing factors are crucial for green and low-carbon development and facilitate human well-being in the Yellow River Basin (YRB). Although the research on NPP has advanced rapidly, in view of the regional particularity of the YRB, the persistence of its NPP change trend needs to be further discussed and more comprehensive impact factors need to be included in the analysis. Meanwhile, the spatial non-stationarity and scale effects of the impact on NPP when multiple factors are involved remain uncertain. Here, we selected a total of twelve natural and anthropogenic factors and used multi-scale geographically weighted regression (MGWR) to disentangle the spatial non-stationary relationship between vegetation NPP and related factors and identify the impact scale difference in the YRB. Additionally, we analyze the spatiotemporal variation trend and persistence of NPP during 2000–2020. The results revealed the following: (1) The annual NPP showed a fluctuating increasing trend, and the vegetation NPP in most regions will exhibit a future trend of increasing to decreasing. (2) The effects of different factors show significant spatial non-stationarity. Among them, the intensity of the impact of most natural factors shows a clear strip-shaped distribution in the east-west direction. It is closely related to the spatial distribution characteristics of natural factors in the YRB. In contrast, the regularity of anthropogenic influences is less obvious. (3) The impact scales of different factors on vegetation NPP were significantly different, and this scale changed with time. The factors with small impact scales could better explain the change in vegetation NPP. Interestingly, the impact size and scale of relative humidity on NPP in the YRB are both larger. This may be due to the arid and semi-arid characteristics of the YRB. Our findings could provide policy makers with specific and quantitative insights for protecting the ecological environment in the YRB. Full article
(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science)
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26 pages, 22243 KiB  
Article
Refined Landslide Susceptibility Mapping Considering Land Use Changes and InSAR Deformation: A Case Study of Yulin City, Guangxi
by Pengfei Li, Huini Wang, Hongli Li, Zixuan Ni, Hongxing Deng, Haigang Sui and Guilin Xu
Remote Sens. 2024, 16(16), 3016; https://doi.org/10.3390/rs16163016 - 17 Aug 2024
Viewed by 837
Abstract
Landslide susceptibility maps (LSMs) are valuable tools typically used by local authorities for land use management and planning activities, supporting decision-makers in urban and infrastructure planning. To address this, we proposed a refined method for landslide susceptibility assessment, which comprehensively considered both static [...] Read more.
Landslide susceptibility maps (LSMs) are valuable tools typically used by local authorities for land use management and planning activities, supporting decision-makers in urban and infrastructure planning. To address this, we proposed a refined method for landslide susceptibility assessment, which comprehensively considered both static and dynamic factors. Neural network methods were used for susceptibility analysis. Land use and land cover (LULC) change and InSAR deformation were then integrated into the traditional susceptibility zoning to obtain a refined susceptibility map with higher accuracy. Validation was conducted on the improved landslide susceptibility map using site landslide data. The results showed that the LULC were proven to be the core driving factors for landslide occurrence in the study area. The GRU model achieved the highest model performance (AUC = 0.886). The introduction of InSAR surface deformation and land use and land cover change data could rationalize the inappropriateness of traditional landslide susceptibility zoning, correcting the false positive and false negative areas in the traditional landslide susceptibility map caused by human activities. Ultimately, 12.25% of the study area was in high-susceptibility zones, with 3.10% of false positive and 0.74% of false negative areas being corrected. The proposed method enabled refined analysis of landslide susceptibility over large areas, providing technical support and disaster prevention and mitigation references for geological hazard susceptibility assessment and land management planning. Full article
(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science)
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23 pages, 9214 KiB  
Article
DCFF-Net: Deep Context Feature Fusion Network for High-Precision Classification of Hyperspectral Image
by Zhijie Chen, Yu Chen, Yuan Wang, Xiaoyan Wang, Xinsheng Wang and Zhouru Xiang
Remote Sens. 2024, 16(16), 3002; https://doi.org/10.3390/rs16163002 - 15 Aug 2024
Cited by 1 | Viewed by 982
Abstract
Hyperspectral images (HSI) contain abundant spectral information. Efficient extraction and utilization of this information for image classification remain prominent research topics. Previously, hyperspectral classification techniques primarily relied on statistical attributes and mathematical models of spectral data. Deep learning classification techniques have recently been [...] Read more.
Hyperspectral images (HSI) contain abundant spectral information. Efficient extraction and utilization of this information for image classification remain prominent research topics. Previously, hyperspectral classification techniques primarily relied on statistical attributes and mathematical models of spectral data. Deep learning classification techniques have recently been extensively utilized for hyperspectral data classification, yielding promising outcomes. This study proposes a deep learning approach that uses polarization feature maps for classification. Initially, the polar co-ordinate transformation method was employed to convert the spectral information of all pixels in the image into spectral feature maps. Subsequently, the proposed Deep Context Feature Fusion Network (DCFF-NET) was utilized to classify these feature maps. The model was validated using three open-source hyperspectral datasets: Indian Pines, Pavia University, and Salinas. The experimental results indicated that DCFF-NET achieved excellent classification performance. Experimental results on three public HSI datasets demonstrated that the proposed method accurately recognized different objects with an overall accuracy (OA) of 86.68%, 94.73%, and 95.14% based on the pixel method, and 98.15%, 99.86%, and 99.98% based on the pixel-patch method. Full article
(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science)
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23 pages, 10890 KiB  
Article
Multiple Land-Use Simulations and Driving Factor Analysis by Integrating a Deep Cascade Forest Model and Cellular Automata: A Case Study in the Pearl River Delta, China
by Haoming Zhuang, Xiaoping Liu, Yuchao Yan, Bingjie Li, Changjiang Wu and Wenkai Liu
Remote Sens. 2024, 16(15), 2750; https://doi.org/10.3390/rs16152750 - 27 Jul 2024
Viewed by 943
Abstract
Cellular automata (CA) models have been extensively employed to predict and understand the spatiotemporal dynamics of land use. Driving factors play a significant role in shaping and driving land-use changes. Mining land-use transition rules from driving factors and quantifying the contribution of driving [...] Read more.
Cellular automata (CA) models have been extensively employed to predict and understand the spatiotemporal dynamics of land use. Driving factors play a significant role in shaping and driving land-use changes. Mining land-use transition rules from driving factors and quantifying the contribution of driving factors to land-use dynamics are fundamental aspects of CA simulation. However, existing CA models have limitations in obtaining accurate transition rules and reliable interpretations simultaneously for multiple land-use simulations. In this study, we constructed a CA model based on a tree-based deep learning algorithm, deep cascade forest (DCF), to improve multiple land-use simulations and driving factors analysis. The DCF algorithm was utilized to mine accurate multiple land-use transition rules without overfitting to improve CA simulation accuracy. Additionally, a novel ensemble mean decrease of impurity (MDI) factor importance analysis method (DCF-MDI), which leverages the cascade structure of the DCF model, was proposed to qualify the contribution of each driving factor to land-use dynamics stably and efficiently. To evaluate the effectiveness of the proposed DCF-CA, we applied the model to simulate land-use distributions and explore the driving mechanisms of land-use dynamics in the Pearl River Delta (PRD), China, from 2000 to 2010. Compared to existing models, the proposed DCF-CA model exhibits the highest accuracy (FoM = 23.79%, PA = 39.77%, UA = 36.35%, OA = 91.50%), which demonstrates its superiority in mining accurate transition rules for capturing multiple land-use dynamics. Factor importance analysis reveals that the proposed DCF-MDI method yields more stable ranking orders and lower standard deviation of contribution weights (<0.10%) compared to the traditional method, indicating its robustness to random disturbances and effectiveness in elucidating the driving mechanisms of land-use dynamics. The DCF-CA model proposed in this study, demonstrating high simulation accuracy and reliable interpretability simultaneously, can provide substantial support for sustainable land use management. Full article
(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science)
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24 pages, 3332 KiB  
Article
U-Net Ensemble for Enhanced Semantic Segmentation in Remote Sensing Imagery
by Ivica Dimitrovski, Vlatko Spasev, Suzana Loshkovska and Ivan Kitanovski
Remote Sens. 2024, 16(12), 2077; https://doi.org/10.3390/rs16122077 - 8 Jun 2024
Cited by 2 | Viewed by 2894
Abstract
Semantic segmentation of remote sensing imagery stands as a fundamental task within the domains of both remote sensing and computer vision. Its objective is to generate a comprehensive pixel-wise segmentation map of an image, assigning a specific label to each pixel. This facilitates [...] Read more.
Semantic segmentation of remote sensing imagery stands as a fundamental task within the domains of both remote sensing and computer vision. Its objective is to generate a comprehensive pixel-wise segmentation map of an image, assigning a specific label to each pixel. This facilitates in-depth analysis and comprehension of the Earth’s surface. In this paper, we propose an approach for enhancing semantic segmentation performance by employing an ensemble of U-Net models with three different backbone networks: Multi-Axis Vision Transformer, ConvFormer, and EfficientNet. The final segmentation maps are generated through a geometric mean ensemble method, leveraging the diverse representations learned by each backbone network. The effectiveness of the base U-Net models and the proposed ensemble is evaluated on multiple datasets commonly used for semantic segmentation tasks in remote sensing imagery, including LandCover.ai, LoveDA, INRIA, UAVid, and ISPRS Potsdam datasets. Our experimental results demonstrate that the proposed approach achieves state-of-the-art performance, showcasing its effectiveness and robustness in accurately capturing the semantic information embedded within remote sensing images. Full article
(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science)
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25 pages, 10286 KiB  
Article
Construction of an Ecological Security Pattern in Rapidly Urbanizing Areas Based on Ecosystem Sustainability, Stability, and Integrity
by Daohong Gong, Min Huang and Hui Lin
Remote Sens. 2023, 15(24), 5728; https://doi.org/10.3390/rs15245728 - 14 Dec 2023
Cited by 7 | Viewed by 1680
Abstract
The escalating pace of urbanization and human activities presents formidable challenges to landuse patterns and ecological environments. Achieving a harmonious coexistence between humans and nature of high quality has emerged as a global imperative. Constructing an ecological security pattern has become an essential [...] Read more.
The escalating pace of urbanization and human activities presents formidable challenges to landuse patterns and ecological environments. Achieving a harmonious coexistence between humans and nature of high quality has emerged as a global imperative. Constructing an ecological security pattern has become an essential approach to mitigating the adverse ecological impacts of urban sprawl, safeguarding human well-being, and promoting the healthy development of ecosystems. Focusing on ecosystem sustainability, stability, and integrity, this study constructed the ecological security pattern in rapidly urbanizing areas, emphasizing achieving a well-balanced integration of urban expansion and ecological preservation. Ecological sources were identified by an evaluation system of “ecosystem service function–ecological sensitivity–landscape connectivity”. Resistance surfaces were constructed by integrating natural and human factors. Ecological corridors and nodes were extracted by methods such as the minimum cumulative resistance and gravity models. Taking Nanchang City as an example, the results show that there were 15 ecological sources, primarily woodland, displaying a distinct “island” phenomenon. Additionally, there were 41 ecological corridors with a combined length of 2170.54 km, exhibiting a dense distribution in the southwest and a sparse distribution in the northeast. The city was found to encompass 122 ecological nodes, predominantly situated along the corridors near the ecological sources, indicating a strong spatial aggregation pattern. An optimized ecological security pattern of “one ring, two belts, three zones, and multiple nodes” was proposed for synergizing ecological protection, restoration, and rapid urbanizing. Full article
(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science)
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14 pages, 5229 KiB  
Article
Novel Grid Collection and Management Model of Remote Sensing Change Detection Samples
by Daoye Zhu, Bing Han, Elisabete A. Silva, Shuang Li, Min Huang, Fuhu Ren and Chengqi Cheng
Remote Sens. 2023, 15(23), 5528; https://doi.org/10.3390/rs15235528 - 27 Nov 2023
Viewed by 1121
Abstract
Remote sensing data have become an important data source for urban and regional change detection, owing to their advantages of authenticity, objectivity, immediacy, and low cost. The method of collection and management for remote sensing change detection samples (RS_CDS) assumes a crucial role [...] Read more.
Remote sensing data have become an important data source for urban and regional change detection, owing to their advantages of authenticity, objectivity, immediacy, and low cost. The method of collection and management for remote sensing change detection samples (RS_CDS) assumes a crucial role in the effectiveness of remote sensing intelligent change detection (RSICD). To achieve rapid collection and real-time sharing of RS_CDS, this study proposes a grid collection and management model of RS_CDS based on GeoSOT (GCAM-GeoSOT), including the grid collection method of RS_CDS (GCM-SD) and grid management method of RS_CDS (GMM-SD). To verify the feasibility and retrieval efficiency of GMM-SD, Oracle and PostgreSQL databases were combined and the retrieval efficiency and database capacity were compared with the corresponding spatial databases, Oracle Spatial and PostgreSQL + PostGIS, respectively. The experimental results showed that GMM-SD not only ensures the reasonable capacity consumption of the database but also has a higher retrieval efficiency for the RS_CDS. This results in a noteworthy comprehensive performance enhancement, with a 47.63% improvement compared to Oracle Spatial and a 40.24% improvement compared to PostgreSQL + PostGIS. Full article
(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science)
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21 pages, 5570 KiB  
Article
Integrated Node Infrastructure for Future Smart City Sensing and Response
by Dong Chen, Xiang Zhang, Wei Zhang and Xing Yin
Remote Sens. 2023, 15(14), 3699; https://doi.org/10.3390/rs15143699 - 24 Jul 2023
Viewed by 1984
Abstract
Emerging smart cities and digital twins are currently built from heterogenous cutting-edge low-power remote sensing systems limited by diverse inefficient communication and information technologies. Future smart cities delivering time-critical services and responses must transition towards utilizing massive numbers of sensors and more efficient [...] Read more.
Emerging smart cities and digital twins are currently built from heterogenous cutting-edge low-power remote sensing systems limited by diverse inefficient communication and information technologies. Future smart cities delivering time-critical services and responses must transition towards utilizing massive numbers of sensors and more efficient integrated systems that rapidly communicate intelligent self-adaptation for collaborative operations. Here, we propose a critical futuristic integrated communication element named City Sensing Base Station (CSBS), inspired by base stations for cell phones that address similar concerns. A CSBS is designed to handle massive volumes of heterogeneous observation data that currently need to be upgraded by middleware or registered. It also provides predictive and interpolation modelling for the control of sensors and response units such as emergency services and drones. A prototype of CSBS demonstrated that it could unify readily available heterogeneous sensing devices, including surveillance video, unmanned aerial vehicles, and ground sensor webs. Collaborative observation capability was also realized by integrating different object detection sources using advanced computer-vision technologies. Experiments with a traffic accident and water pipeline emergency showed sensing and intelligent analyses were greatly improved. CSBS also significantly reduced redundant Internet connections while maintaining high efficiency. This innovation successfully integrates high-density, high-diversity, and high-precision sensing in a distributed way for the future digital twin of cities. Full article
(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science)
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23 pages, 7796 KiB  
Article
Hybrid Deep Learning and S2S Model for Improved Sub-Seasonal Surface and Root-Zone Soil Moisture Forecasting
by Lei Xu, Hongchu Yu, Zeqiang Chen, Wenying Du, Nengcheng Chen and Min Huang
Remote Sens. 2023, 15(13), 3410; https://doi.org/10.3390/rs15133410 - 5 Jul 2023
Cited by 2 | Viewed by 2117
Abstract
Surface soil moisture (SSM) and root-zone soil moisture (RZSM) are key hydrological variables for the agricultural water cycle and vegetation growth. Accurate SSM and RZSM forecasting at sub-seasonal scales would be valuable for agricultural water management and preparations. Currently, weather model-based soil moisture [...] Read more.
Surface soil moisture (SSM) and root-zone soil moisture (RZSM) are key hydrological variables for the agricultural water cycle and vegetation growth. Accurate SSM and RZSM forecasting at sub-seasonal scales would be valuable for agricultural water management and preparations. Currently, weather model-based soil moisture predictions are subject to large uncertainties due to inaccurate initial conditions and empirical parameterization schemes, while the data-driven machine learning methods have limitations in modeling long-term temporal dependences of SSM and RZSM because of the lack of considerations in the soil water process. Thus, here, we innovatively integrate the model-based soil moisture predictions from a sub-seasonal-to-seasonal (S2S) model into a data-driven stacked deep learning model to construct a hybrid SSM and RZSM forecasting framework. The hybrid forecasting model is evaluated over the Yangtze River Basin and parts of Europe from 1- to 46-day lead times and is compared with four baseline methods, including the support vector regression (SVR), random forest (RF), convolutional long short-term memory (ConvLSTM) and the S2S model. The results indicate substantial skill improvements in the hybrid model relative to baseline models over the two study areas spatiotemporally, in terms of the correlation coefficient, unbiased root mean square error (ubRMSE) and RMSE. The hybrid forecasting model benefits from the long-lead predictive skill from S2S and retains the advantages of data-driven soil moisture memory modeling at short-lead scales, which account for the superiority of hybrid forecasting. Overall, the developed hybrid model is promising for improved sub-seasonal SSM and RZSM forecasting over global and local areas. Full article
(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science)
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18 pages, 7363 KiB  
Article
Mapping Insect-Proof Screened Citrus Orchards Using Sentinel-2 MSl Time-Series Images
by Guobin Yu, Li Zhang, Lingxia Luo, Guihua Liu, Zongyi Chen and Shanshan Xiong
Remote Sens. 2023, 15(11), 2867; https://doi.org/10.3390/rs15112867 - 31 May 2023
Cited by 1 | Viewed by 1764
Abstract
Citrus is a crucial agricultural commodity of the hilly subtropical regions of southern China. Attempts in recent years to combat the destructive disease Huanglongbing (HLB) have led to citrus orchards being covered with insect-proof screens (IPS). Understanding which citrus orchards are covered by [...] Read more.
Citrus is a crucial agricultural commodity of the hilly subtropical regions of southern China. Attempts in recent years to combat the destructive disease Huanglongbing (HLB) have led to citrus orchards being covered with insect-proof screens (IPS). Understanding which citrus orchards are covered by IPS is crucial for regional water and soil conservation, as well as control of plastic pollution. However, monitoring of orchards is complicated by IPS spectral interference in remotely sensed image classification. Here, an optimal feature combination scheme is developed and tested for mapping citrus orchards that use IPS. Seasonal Sentinel-2 images from 2021 were used to define indices for vegetation, plastic mulch, red edge, and texture. These were combined with topographic and land surface temperature using random forest classification to determine optimal feature discrimination combinations for orchards in Xunwu County, Jiangxi Province. Results show: (1) significantly higher visible light reflectance from IPS orchards ensures spectral discrimination between IPS covered and uncovered orchards. (2) After feature optimization, the seasonal spectral band has the highest accuracy (86%) in single feature classification. The addition of conventional indices and topographic-temperature features improves classification to 92%. (3) Xunwu County had 460 km2 of citrus orchard cover in 2021, with 88 km2 (19%) of that total being covered with IPS. Our method effectively and accurately maps citrus orchards with or without IPS coverage at 10 m resolution. The effective monitoring of large-scale IPS in other regions can now support the development of local and regional sustainable agricultural policies. Full article
(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science)
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20 pages, 13099 KiB  
Article
Mapping the Age of Subtropical Secondary Forest Using Dense Landsat Time Series Data: An Ensemble Model
by Shaoyu Zhang, Jun Yu, Hanzeyu Xu, Shuhua Qi, Jin Luo, Shiming Huang, Kaitao Liao and Min Huang
Remote Sens. 2023, 15(8), 2067; https://doi.org/10.3390/rs15082067 - 14 Apr 2023
Cited by 3 | Viewed by 3022
Abstract
Quantifying secondary forest age (SFA) is essential to evaluate the carbon processes of forest ecosystems at regional and global scales. However, the successional stages of secondary forests remain poorly understood due to low-frequency thematic maps. This study aimed to estimate SFA with higher [...] Read more.
Quantifying secondary forest age (SFA) is essential to evaluate the carbon processes of forest ecosystems at regional and global scales. However, the successional stages of secondary forests remain poorly understood due to low-frequency thematic maps. This study aimed to estimate SFA with higher frequency and more accuracy by using dense Landsat archives. The performances of four time-series change detection algorithms—moving average change detection (MACD), Continuous Change Detection and Classification (CCDC), LandTrendr (LT), and Vegetation Change Tracker (VCT)—for detecting forest regrowth were first evaluated. An ensemble model was then developed to determine more accurate timings for forest regrowth based on the evaluation results. Finally, after converting the forest regrowth year to the SFA, the spatiotemporal and topographical distributions of the SFA were analyzed. The proposed ensemble model was validated in Jiangxi province, China, which is located in a subtropical region and has experienced drastic forest disturbances, artificial afforestation, and natural regeneration. The results showed that: (1) the developed ensemble model effectively determined forest regrowth time with significantly decreased omission and commission rates compared to the direct use of the four single algorithms; (2) the optimal ensemble model combining the independent algorithms obtained the final SFA for Jiangxi province with the lowest omission and commission rates in the spatial domain (14.06% and 24.71%) and the highest accuracy in the temporal domain (R2 = 0.87 and root mean square error (RMSE) = 3.17 years); (3) the spatiotemporal and topographic distribution from 1 to 34 years in the 2021 SFA map was analyzed. This study demonstrated the feasibility of using change detection algorithms for estimating SFA at regional to national scales and provides a data foundation for forest ecosystem research. Full article
(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science)
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23 pages, 17238 KiB  
Article
Monthly Ocean Primary Productivity Forecasting by Joint Use of Seasonal Climate Prediction and Temporal Memory
by Lei Xu, Hongchu Yu, Zeqiang Chen, Wenying Du, Nengcheng Chen and Chong Zhang
Remote Sens. 2023, 15(5), 1417; https://doi.org/10.3390/rs15051417 - 2 Mar 2023
Cited by 2 | Viewed by 2385
Abstract
Ocean primary productivity generated by phytoplankton is critical for ocean ecosystems and the global carbon cycle. Accurate ocean primary productivity forecasting months in advance is beneficial for marine management. Previous persistence-based prediction studies ignore the temporal memories of multiple relevant factors and the [...] Read more.
Ocean primary productivity generated by phytoplankton is critical for ocean ecosystems and the global carbon cycle. Accurate ocean primary productivity forecasting months in advance is beneficial for marine management. Previous persistence-based prediction studies ignore the temporal memories of multiple relevant factors and the seasonal forecasting skill drops quickly with increasing lead time. On the other hand, the emerging ensemble climate forecasts are not well considered as new predictability sources of ocean conditions. Here we proposed a joint forecasting model by combining the seasonal climate predictions from ten heterogeneous models and the temporal memories of relevant factors to examine the monthly predictability of ocean productivity from 0.5- to 11.5-month lead times. The results indicate that a total of ~90% and ~20% productive oceans are expected to be skillfully predicted by the combination of seasonal SST predictions and local memory at 0.5- and 4.5-month leads, respectively. The joint forecasting model improves by 10% of the skillfully predicted areas at 6.5-month lead relative to the prediction by productivity persistence. The hybrid data-driven and model-driven forecasting approach improves the predictability of ocean productivity relative to individual predictions, of which the seasonal climate predictions contribute largely to the skill improvement over the equatorial Pacific and Indian Ocean. These findings highlight the advantages of the integration of climate predictions and temporal memory for ocean productivity forecasting and may provide useful seasonal forecasting information for ocean ecosystem management. Full article
(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science)
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17 pages, 5417 KiB  
Article
Rice Yield Prediction in Hubei Province Based on Deep Learning and the Effect of Spatial Heterogeneity
by Shitong Zhou, Lei Xu and Nengcheng Chen
Remote Sens. 2023, 15(5), 1361; https://doi.org/10.3390/rs15051361 - 28 Feb 2023
Cited by 15 | Viewed by 3103
Abstract
Timely and accurate crop yield information can ensure regional food security. In the field of predicting crop yields, deep learning techniques such as long short-term memory (LSTM) and convolutional neural networks (CNN) are frequently employed. Many studies have shown that the predictions of [...] Read more.
Timely and accurate crop yield information can ensure regional food security. In the field of predicting crop yields, deep learning techniques such as long short-term memory (LSTM) and convolutional neural networks (CNN) are frequently employed. Many studies have shown that the predictions of models combining the two are better than those of single models. Crop growth can be reflected by the vegetation index calculated using data from remote sensing. However, the use of pure remote sensing data alone ignores the spatial heterogeneity of different regions. In this paper, we tested a total of three models, CNN-LSTM, CNN and convolutional LSTM (ConvLSTM), for predicting the annual rice yield at the county level in Hubei Province, China. The model was trained by ERA5 temperature (AT) data, MODIS remote sensing data including the Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP) and Soil-Adapted Vegetation Index (SAVI), and a dummy variable representing spatial heterogeneity; rice yield data from 2000–2019 were employed as labels. Data download and processing were based on Google Earth Engine (GEE). The downloaded remote sensing images were processed into normalized histograms for the training and prediction of deep learning models. According to the experimental findings, the model that included a dummy variable to represent spatial heterogeneity had a stronger predictive ability than the model trained using just remote sensing data. The prediction performance of the CNN-LSTM model outperformed the CNN or ConvLSTM model. Full article
(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science)
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21 pages, 9514 KiB  
Article
Visualization of Environmental Sensing Data in the Lake-Oriented Digital Twin World: Poyang Lake as an Example
by Hao Chen, Chaoyang Fang and Xin Xiao
Remote Sens. 2023, 15(5), 1193; https://doi.org/10.3390/rs15051193 - 21 Feb 2023
Cited by 7 | Viewed by 2970
Abstract
Access to real-time environmental sensing data is key to representing real-time environmental changes in the digital twin of lakes. The visualization of environmental sensing data is an important part of establishing a digital twin of lakes. In the past, environmental sensing data display [...] Read more.
Access to real-time environmental sensing data is key to representing real-time environmental changes in the digital twin of lakes. The visualization of environmental sensing data is an important part of establishing a digital twin of lakes. In the past, environmental sensing data display methods were either charts or two-dimensional map-based visualization methods. Breaking through the traditional visualization methods of environmental sensing data and realizing a multi-dimensional and multi-view display of environmental sensing data in a digital twin of lakes is something that this particular paper tries to resolve. This study proposes a visualization framework to integrate, manage, analyze, and visualize the environmental sensing data in the digital twin of lakes. In addition, this study also seeks to realize the coupling expression of geospatial data and long-term monitoring sequence data. Different visualization methods are used to realize the visualization of environmental sensing data in the digital twin of lakes. Using vector and scalar visualization methods to display ambient wireless sensor monitoring data in a digital twin of lakes provides researchers with richer visualization methods and means for deeper analysis. Using video fusion technology to display environmental sensing video surveillance data strengthens the integration of the virtual environment and real space and saves time for position identification using video surveillance. These findings may also help realize the integration and management of real-time environmental sensing data in a digital twin of lakes. The visualization framework uses various visualization methods to express the monitoring data of environmental wireless sensors and increases the means of visualizing environmental sensing data in the world of a lake digital twin. This visualization framework is also a general approach that can be applied to all similar lakes, or other geographical scenarios where environmental sensing devices are deployed. The establishment of a digital twin of Poyang Lake has certain practical significance for improving the digital management level of Poyang Lake and monitoring its ecological changes. Poyang Lake is used as an example to verify the proposed framework and method, which shows that the framework can be applied to the construction of a lake-oriented digital twin. Full article
(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science)
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15 pages, 4196 KiB  
Article
The Multiple Perspective Response of Vegetation to Drought on the Qinghai-Tibetan Plateau
by Yuying Zhu, Huamin Zhang, Mingjun Ding, Lanhui Li and Yili Zhang
Remote Sens. 2023, 15(4), 902; https://doi.org/10.3390/rs15040902 - 6 Feb 2023
Cited by 10 | Viewed by 2431
Abstract
The Qinghai-Tibetan Plateau (QTP) is a global center of cold and dry, where the most extensive fragile alpine vegetation exists. Quantitative analysis of drought event characteristics and vegetation response to drought on the QTP is indispensable for understanding the increasing drought events in [...] Read more.
The Qinghai-Tibetan Plateau (QTP) is a global center of cold and dry, where the most extensive fragile alpine vegetation exists. Quantitative analysis of drought event characteristics and vegetation response to drought on the QTP is indispensable for understanding the increasing drought events in a warming climate which exacerbate adverse influence on extremely alpine ecosystems. Here, using the standardized precipitation evapotranspiration index (SPEI) and the normalized difference vegetation index (NDVI) from 2000 to 2018, this study analyzed the characteristics of drought events, their temporal impacts, and the stability of vegetation response to drought on the QTP. Results showed that: the characteristics of drought events on the QTP have clear spatial heterogeneity. When compared to the east monsoon region, most of the western regions have higher frequency and lower intensity of drought events. Drought has significant temporal effects on vegetation in grassland areas of the QTP during the growing season, which reach their peak in July and August. The 0–1-month and 3-month time scales were the optimal lagged and accumulated time during the growing season, respectively. The stability of vegetation response to drought showed significant spatial heterogeneity and varied with eco-geographical regions and vegetation types. Generally, forest areas showed high resistance (74.09) and resilience (2.26), followed by crop and grassland areas. Full article
(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science)
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