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Temporal Resolution, a Key Factor in Environmental Risk Assessment II - Integrating Data from Multiple Data Sources

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

Deadline for manuscript submissions: 15 May 2025 | Viewed by 9038

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Special Issue Editors

Dr. Adrian Ursu

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Guest Editor

Faculty of Geography and Geology, University “Alexandru Ioan Cuza”, 700506 Iași, Romania
Interests: land use/land cover changes; image processing; satellite image analysis; digital mapping; natural and environmental risk assessment through remote sensing; urban sprawl and remote sensing; heritage and remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Cristian Constantin Stoleriu

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Guest Editor

Department of Geography, Faculty of Geography and Geology, Alexandru Ioan Cuza University of Iași, 700505 Iași, Romania
Interests: biogeography; hydrology; GIS; remote sensing; geo-informatics; phytogeography; hydrological processes; environmental studies
Special Issues, Collections and Topics in MDPI journals

Dr. Marian Mierlă

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Guest Editor

“Danube Delta” National Institute for Research and Development, 820112 Tulcea, Romania
Interests: geographical information system; remote sensing; image segmentation; image analysis; habitat distribution; species distribution; climate change analysis; land use/cover changes

Special Issue Information

Dear Colleagues,

Scientists can benefit from a vast database of satellite imagery, covering the entire surface of the globe, spanning over 40 years of our timeline. Considering the large number of different types of satellites orbiting the Earth, the available data are not always homogeneous and comparable, but each space mission has managed to collect large packages of systematic data. In recent years, spatial analysis instruments have diversified and evolved significantly from a technological point of view, so we can benefit from satellite images with better spatial, spectral and temporal resolutions. Therefore, we can now easily evaluate the impact of natural or anthropic events on the environment and society, and we can easily estimate the repercussions and provide appropriate solutions.

Good temporal resolution and good-quality satellite images allow for scientists to evaluate the effects of droughts, hails, hurricanes, tornadoes, floods, deforestation, forest fires, mining accidents, pollution, Hazmat accidents, land-use changes, social events, urbanization, wars, etc. Furthermore, having a consistent long-term database of satellite images provides researchers with the opportunity to analyse these phenomena from a historic perspective, and it is possible to evaluate long-term changes in natural local parameters in relation to recent changes in the environment at the global scale.

When we analyse phenomenon over a long period of time, it is necessarily to use various data sources, such as old maps, field analyses or other types of data. If we analyse a natural phenomenon with disastrous effects in detail, we can benefit from data from sources other than remote sensing, such as: Doppler weather radar, ground-penetrating radar (GPR), 3D laser scanning, electromagnetic resistivity surveys, etc. Therefore, this Special Issue focuses on TIME as the determinant factor in the analysis of various phenomena at various spatial scales, but aims also to integrate data from multiple sources.

Dr. Adrian Ursu
Dr. Cristian Constantin Stoleriu
Dr. Marian Mierlă
Guest Editors

Manuscript Submission Information

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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.

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Keywords

time series data and projections
rapid evaluation of the impact of extreme events on the environment and society
climate change
environmental risks
land-use and land-cover changes Multispectral, hyperspectral and LiDAR data from a temporal perspective
ecosystems monitoring from RS data
history and heritage
multiple data source integrated in time evolution analysis

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Published Papers (5 papers)

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Research

22 pages, 24321 KiB

Open AccessArticle

Intelligent Monitoring Applications of Landslide Disaster Knowledge Graphs Based on ChatGLM2

by Zhengrong Wu, Haibo Yang, Yingchun Cai, Bo Yu, Chuangheng Liang, Zheng Duan and Qiuhua Liang

Remote Sens. 2024, 16(21), 4056; https://doi.org/10.3390/rs16214056 - 31 Oct 2024

Viewed by 954

Abstract

Over the years, the field of landslide disaster research has amassed a wealth of data and specialized knowledge. However, these resources originate from a wide array of sources and often feature complex data structures, highlighting a persistent lack of methods to integrate multi-source, heterogeneous data. Traditional landslide monitoring methods typically focus on singular monitoring targets and data sources, which limits a comprehensive understanding of the complex processes involved in landslides. This paper introduces a landslide monitoring model based on a knowledge graph. This model employs P-Tuning to fine-tune ChatGLM2 for the extraction of triples. Differential InSAR (D-InSAR) is utilized to extract ground deformation data, which is then integrated with the knowledge graph for landslide monitoring and analysis. This study focuses on the co-seismic landslide in Jishishan, Gansu, China. By analyzing the landslide knowledge graph and the spatiotemporal deformation map, the results are as follows: (1) For this event, 106 entities and attributes were constructed, along with two recommended calculation routes. (2) The deformation at the earthquake’s central region reached up to 8.784 cm, with a slightly smaller deformation zone to the northwest peaking at 9.662 cm. Significant unilateral subsidence was observed in the mountain range to the southwest. (3) The area affected by the co-seismic landslide primarily includes farmland and villages, covering an area of 0.3408 square kilometers. (4) Analysis based on the knowledge graph indicates that this landslide was primarily caused by the rapid liquefaction of water-saturated soil layers due to the earthquake, resulting in instability. This study contributes to the analysis of post-disaster losses, attribution, and impacts. Full article

(This article belongs to the Special Issue Temporal Resolution, a Key Factor in Environmental Risk Assessment II - Integrating Data from Multiple Data Sources)

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22 pages, 12339 KiB

Open AccessArticle

Robust Trend Analysis in Environmental Remote Sensing: A Case Study of Cork Oak Forest Decline

by Oliver Gutiérrez-Hernández and Luis V. García

Remote Sens. 2024, 16(20), 3886; https://doi.org/10.3390/rs16203886 - 19 Oct 2024

Cited by 3 | Viewed by 1044

Abstract

We introduce a novel methodological framework for robust trend analysis (RTA) using remote sensing data to enhance the accuracy and reliability of detecting significant environmental trends. Our approach sequentially integrates the Theil–Sen (TS) slope estimator, the Contextual Mann–Kendall (CMK) test, and the false discovery rate (FDR) control. This comprehensive method addresses common challenges in trend analysis, such as handling small, noisy datasets with outliers and issues related to spatial autocorrelation, cross-correlation, and multiple testing. We applied this RTA workflow to study tree cover trends in Los Alcornocales Natural Park (Southern Spain), Europe’s largest cork oak forest, analysing interannual changes in tree cover from 2000 to 2022 using Terra MODIS MOD44B data. Our results reveal that the TS estimator provides a robust measure of trend direction and magnitude, but its effectiveness is dramatically enhanced when combined with the CMK test. This combination highlights significant trends and effectively corrects for spatial autocorrelation and cross-correlation, ensuring that genuine environmental signals are distinguished from statistical noise. Unlike previous workflows, our approach incorporates the FDR control, which successfully filtered out 29.6% of false discoveries in the case study, resulting in a more stringent assessment of true environmental trends captured by multi-temporal remotely sensed data. In the case study, we found that approximately one-third of the area exhibits significant and statistically robust declines in tree cover, with these declines being geographically clustered. Importantly, these trends correspond with relevant changes in tree cover, emphasising the ability of RTA to detect relevant environmental changes. Overall, our findings underscore the crucial importance of combining these methods, as their synergy is essential for accurately identifying and confirming robust environmental trends. The proposed RTA framework has significant implications for environmental monitoring, modelling, and management. Full article

(This article belongs to the Special Issue Temporal Resolution, a Key Factor in Environmental Risk Assessment II - Integrating Data from Multiple Data Sources)

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18 pages, 4997 KiB

Open AccessArticle

Spatio-Temporal Knowledge Graph-Based Research on Agro-Meteorological Disaster Monitoring

by Wenyue Zhang, Ling Peng, Xingtong Ge, Lina Yang, Luanjie Chen and Weichao Li

Remote Sens. 2023, 15(18), 4403; https://doi.org/10.3390/rs15184403 - 7 Sep 2023

Cited by 3 | Viewed by 2145

Abstract

Currently, there is a wealth of data and expert knowledge available on monitoring agro-meteorological disasters. However, there is still a lack of technical means to organically integrate and analyze heterogeneous data sources in a collaborative manner. This paper proposes a method for monitoring agro-meteorological disasters based on a spatio-temporal knowledge graph. It employs a semantic ontology framework to achieve the organic fusion of multi-source heterogeneous data, including remote sensing data, meteorological data, farmland data, crop information, etc. And it formalizes expert knowledge and computational models into knowledge inference rules, thereby enabling monitoring, early warning, and disaster analysis of agricultural crops within the observed area. The experimental area for this research is the wheat planting region in three counties in Henan Province. The method is tested using simulation monitoring, early warning, and impact calculation of the past two occurrences of dry hot wind disasters. The experimental results demonstrate that the proposed method can provide more specific and accurate warning information and post-disaster analysis results compared to raw records. The statistical results of NDVI decline also validate the correlation between the severity of wheat damage caused by dry hot winds and the intensity and duration of their occurrences. Regarding remote sensing data, this paper proposes a method that directly incorporates remote sensing data into spatio-temporal knowledge inference calculations. By integrating remote sensing data into the regular monitoring process, the advantages of remote sensing data granted by continuous observation are utilized. This approach represents a beneficial attempt to organically integrate remote sensing and meteorological data for monitoring, early warning, and evaluation analysis of agro-meteorological disasters. Full article

(This article belongs to the Special Issue Temporal Resolution, a Key Factor in Environmental Risk Assessment II - Integrating Data from Multiple Data Sources)

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20 pages, 3904 KiB

Open AccessArticle

Inside Late Bronze Age Settlements in NE Romania: GIS-Based Surface Characterization of Ashmound Structures Using Airborne Laser Scanning and Aerial Photography Techniques

by Casandra Brașoveanu, Alin Mihu-Pintilie and Radu-Alexandru Brunchi

Remote Sens. 2023, 15(17), 4124; https://doi.org/10.3390/rs15174124 - 22 Aug 2023

Cited by 1 | Viewed by 1401

Abstract

The identification and delineation, through aerial photography, of the archaeological structures that present temporal resolution, as well as their characterization based on high-resolution LiDAR (Light Detection and Ranging)-derived DEMs (Digital Elevation Models) are modern techniques widely used in the archaeological prospecting of various landscapes. In this study, we present an application of Airborne Laser Scanning (ALS) and aerial photography (AP) techniques, used in order to compute geomorphometric indices specific to the ashmound structures of Late Bronze Age (LBA) archaeological sites that are visible on the soil surface. The necessity of determining the ashmounds’ geoarchaeological description stems from the fact that despite the majority of archaeologists weighing in on the subject, there is still no accepted explanation regarding their initial functionality. Thus, we believe that the GIS-based high-resolution characterization of 200 ashmound features identified in 21 Noua Culture (NC) archaeological sites will contribute to a better understanding of the ashmounds’ functionality and evolution in the heterogeneous landscape of the study area (NE Romania). Therefore, various shape indices, such as the area (A), perimeter (P), length (L), form factor (RF), circularity ratio (RC), and elongation ratio (RE) were computed for microlevel characterizations of the visible ashmounds’ structures. Additionally, LiDAR-derived DEMs with a 0.5 m resolution were used to generate more surface characteristics such as the slope (S) and hypsometric indices (HI). The outcomes indicate that the ashmounds have relatively diverse shapes (an RF range from 0.37 to 0.77; a RC range from 0.79 to 0.99; a RE range from 0.68 to 0.99), and the micro-relief slightly varies from positive to negative landforms (HI range from 0.34 to 0.61) depending on the erosion intensity (S range from 1.17° to 19.69°) and anthropogenic impact (e.g., current land use and agriculture type). Furthermore, each morphometric parameter is an indicator for surface processes, aiding in the identification of the geomorphologic and surface-erosion aspects that affect the archaeological remains, contributing to the assessment of the conservation status of the ashmound structures within the current landscape configuration. In this regard, this article presents and discusses the remote sensing (RS) techniques used, as well as the morphometric data obtained, exploring the implications of our findings for a better characterization of the NC in Romania. Full article

(This article belongs to the Special Issue Temporal Resolution, a Key Factor in Environmental Risk Assessment II - Integrating Data from Multiple Data Sources)

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18 pages, 5439 KiB

Open AccessArticle

Insights into Spatiotemporal Variations in the NPP of Terrestrial Vegetation in Africa from 1981 to 2018

by Qianjie Wang, Liang Liang, Shuguo Wang, Sisi Wang, Lianpeng Zhang, Siyi Qiu, Yanyan Shi, Jin Shi and Chen Sun

Remote Sens. 2023, 15(11), 2748; https://doi.org/10.3390/rs15112748 - 25 May 2023

Cited by 7 | Viewed by 2042

Abstract

The net primary productivity (NPP) of vegetation is an important indicator used to evaluate the quality of terrestrial ecosystems and characterize the carbon balance of ecosystems. In this study, the spatiotemporal distribution and dynamic change in NPP in Africa from 1981 to 2018 were analyzed using the long time series data of NPP. The results of the trend and fluctuation analysis showed that the NPP in the Sahara arid region in northern Africa and the arid region in South Africa exhibited a significant reduction and a high degree of fluctuation; most of the NPP in the tropical rainforests in central Africa and the deciduous broadleaved forests and deciduous needle-leaved forests on the north and south sides of the tropical rainforests increased and showed a low degree of fluctuation; the Congo basin, Gabon, Cameroon, Ghana, Nigeria, Tanzania, and other regions were affected by human activities, while the NPP in these regions exhibited a significant reduction and a high degree of fluctuation. Anomaly analysis showed that the NPP in Africa generally exhibited a slow upward trend during the period from 1981 and 2018. The trend was basically consistent in different seasons, and can be segmented into three phases: (1) a phase of descent from 1981 to 1992, with the NPP below the average value in most years; (2) a phase of steady growth from 1993 to 2000, reaching a peak in 2000; (3) a phase of fluctuations from 2001 to 2018, where the NPP value was above the average value in all years except 2015 and 2016, when the NPP value was low due to abnormally high temperatures and drought. The Mann–Kendall test further showed that the annual and seasonal NPP in Africa exhibited a significant upward trend, and the mutation time points occurred around 1995. The wavelet time series analysis revealed obvious periodic changes in the time series of NPP in Africa. The annual and seasonal NPP showed clear oscillations on time scales of 7, 20, 29, and 55 years. The 55-year period had the strongest signal, and was the first main period. The study can provide a scientific gist for the sustainable development of environmental ecology, agricultural production, and the social economy in Africa. Full article

(This article belongs to the Special Issue Temporal Resolution, a Key Factor in Environmental Risk Assessment II - Integrating Data from Multiple Data Sources)

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