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Using Time Series Analysis of Remote Sensing Images to Detect...
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Using Time Series Analysis of Remote Sensing Images to Detect Changes in Land Condition

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Systems and Global Change".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 19516

Special Issue Editor

Dr. Eddie J.B. van Etten

E-Mail Website
Guest Editor
School of Science, Edith Cowan University, Perth, WA 6027, Australia
Interests: landscape ecology; fire ecology; eucalyptus forests; arid land ecology; restoration ecology; plant–animal interactions
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remotely sensed images of the earth surface are now widely used to detect temporal changes in land condition and land use, including detecting trends in land degradation, demonstrating the effects of land management interventions, and delineating more rapid changes in vegetation such as burned areas. Data from satellites with regular captures over long periods and large areas (e.g., MODIS, Landsat) are particularly valuable in this regard as they form a readily accessible databank from which to examine longer-term trends in land status and features across a range of spatial scales. Time-series analysis is an often preferred method for land change detection because of its ability to detect both broad trends and recurring biological events, as well as being amenable to predicting future trends and events. This Special Edition of Land will focus on studies which successfully use time-series modeling of remotely sensed imagery to inform land management. Although our emphasis will be on papers which demonstrate successful application of time-series analysis to address land management problems, we are also interested in papers which develop novel or interesting time-series modeling methodology, including those applicable to detecting non-seasonal trends and events, such as those typically occurring in arid lands. Papers on the detection and/or prediction of burned area trends or the effects of human activity, including grazing, on land condition will be particularly welcomed. 

Dr. Eddie J.B. van Etten
Guest Editor

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. Land is an international peer-reviewed open access monthly 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 2600 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.

Published Papers (4 papers)

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Research

24 pages, 7080 KiB  
Article
An Alternative Method for the Generation of Consistent Mapping to Monitoring Land Cover Change: A Case Study of Guerrero State in Mexico
by René Vázquez-Jiménez, Raúl Romero-Calcerrada, Rocío N. Ramos-Bernal, Patricia Arrogante-Funes and Carlos J. Novillo
Land 2021, 10(7), 731; https://0-doi-org.brum.beds.ac.uk/10.3390/land10070731 - 12 Jul 2021
Cited by 3 | Viewed by 2128
Abstract
Land cover is crucial for ecosystems and human activities. Therefore, monitoring land cover changes has become relevant in recent years. This study proposes an alternative method based on conventional change detection techniques combined with maximum likelihood (MaxLike) supervised classification of satellite images to [...] Read more.
Land cover is crucial for ecosystems and human activities. Therefore, monitoring land cover changes has become relevant in recent years. This study proposes an alternative method based on conventional change detection techniques combined with maximum likelihood (MaxLike) supervised classification of satellite images to generate consistent Land Use/Land Cover (LULC) maps. The novelty of this method is that the supervised classification is applied in an earlier stage of change detection exclusively to identified dynamics zones. The LULC categories of the stable zones are acquired from an initial date’s previously elaborated base map. The methodology comprised the use of Landsat images from 2011 and 2016, applying the Sun Canopy Sensor (SCS + C) topographic correction model enhanced through the classification of slopes, using derived topographic corrected images with NDVI, and employing Tasseled Cap (TC) Brightness-Greenness-Wetness indices and Principal Components (PCs). The study incorporated a comparative analysis of the consistency of the LULC mapping, which is generated based on control areas. The results show that the proposed method, although slightly laborious, is viable and fully automatable. The generated LULC map is accurate and robust and achieves a Kappa concordance index of 87.53. Furthermore, the boundary consistency was visually superior to the conventional classified map. Full article
(This article belongs to the Special Issue Using Time Series Analysis of Remote Sensing Images to Detect Changes in Land Condition)
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35 pages, 29798 KiB  
Article
Multispectral Sentinel-2 and SAR Sentinel-1 Integration for Automatic Land Cover Classification
by Paolo De Fioravante, Tania Luti, Alice Cavalli, Chiara Giuliani, Pasquale Dichicco, Marco Marchetti, Gherardo Chirici, Luca Congedo and Michele Munafò
Land 2021, 10(6), 611; https://0-doi-org.brum.beds.ac.uk/10.3390/land10060611 - 07 Jun 2021
Cited by 18 | Viewed by 4933
Abstract
The study of land cover and land use dynamics are fundamental to understanding the radical changes that human activity is causing locally and globally and to analyse the continuous metamorphosis of landscape. In Europe, the Copernicus Program offers numerous territorial monitoring tools to [...] Read more.
The study of land cover and land use dynamics are fundamental to understanding the radical changes that human activity is causing locally and globally and to analyse the continuous metamorphosis of landscape. In Europe, the Copernicus Program offers numerous territorial monitoring tools to users and decision makers, such as Sentinel data. This research aims at developing and implementing a land cover mapping and change detection methodology through the classification of Copernicus Sentinel-1 and Sentinel-2 satellite data. The goal is to create a versatile and economically sustainable algorithm capable of rapidly processing large amounts of data, allowing the creation of national-scale products with high spatial resolution and update frequency for operational purposes. Great attention was paid to compatibility with the main activities planned in the near future at the national and European level. In this sense, a land cover classification system consistent with the European specifications of the EAGLE group has been adopted. The methodology involves the definition of distinct sets of decision rules for each of the land cover macro-classes and for the land cover change classes. The classification refers to pixels’ spectral and backscatter characteristics, exploiting the main multi-temporal indices while proposing two new ones: the NDCI to distinguish between broad-leaved and needle-leaved trees, and the Burned Index (BI) to identify burned areas. This activity allowed for the production of a land cover map for 2018 and the change detection related to forest disturbances and land consumption for 2017–2018, reaching an overall accuracy of 83%. Full article
(This article belongs to the Special Issue Using Time Series Analysis of Remote Sensing Images to Detect Changes in Land Condition)
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26 pages, 6091 KiB  
Article
Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China
by Zaheer Abbas, Guang Yang, Yuanjun Zhong and Yaolong Zhao
Land 2021, 10(6), 584; https://0-doi-org.brum.beds.ac.uk/10.3390/land10060584 - 01 Jun 2021
Cited by 57 | Viewed by 6785
Abstract
Land use land cover (LULC) transition analysis is a systematic approach that helps in understanding physical and human involvement in the natural environment and sustainable development. The study of the spatiotemporal shifting pattern of LULC, the simulation of future scenarios and the intensity [...] Read more.
Land use land cover (LULC) transition analysis is a systematic approach that helps in understanding physical and human involvement in the natural environment and sustainable development. The study of the spatiotemporal shifting pattern of LULC, the simulation of future scenarios and the intensity analysis at the interval, category and transition levels provide a comprehensive prospect to determine current and future development scenarios. In this study, we used multitemporal remote sensing data from 1980–2020 with a 10-year interval, explanatory variables (Digital Elevation Model (DEM), slope, population, GDP, distance from roads, distance from the city center and distance from streams) and an integrated CA-ANN approach within the MOLUSCE plugin of QGIS to model the spatiotemporal change transition potential and future LULC simulation in the Greater Bay Area. The results indicate that physical and socioeconomic driving factors have significant impacts on the landscape patterns. Over the last four decades, the study area experienced rapid urban expansion (4.75% to 14.75%), resulting in the loss of forest (53.49% to 50.57%), cropland (21.85% to 16.04%) and grassland (13.89% to 12.05%). The projected results (2030–2050) also endorse the increasing trend in built-up area, forest, and water at the cost of substantial amounts of cropland and grassland. Full article
(This article belongs to the Special Issue Using Time Series Analysis of Remote Sensing Images to Detect Changes in Land Condition)
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25 pages, 11927 KiB  
Article
Landslide Susceptibility Mapping of Central and Western Greece, Combining NGI and WoE Methods, with Remote Sensing and Ground Truth Data
by Charalampos Kontoes, Constantinos Loupasakis, Ioannis Papoutsis, Stavroula Alatza, Eleftheria Poyiadji, Athanassios Ganas, Christina Psychogyiou, Mariza Kaskara, Sylvia Antoniadi and Natalia Spanou
Land 2021, 10(4), 402; https://0-doi-org.brum.beds.ac.uk/10.3390/land10040402 - 12 Apr 2021
Cited by 16 | Viewed by 4276
Abstract
The exploitation of remote sensing techniques has substantially improved pre- and post- disaster landslide management over the last decade. A variety of landslide susceptibility methods exists, with capabilities and limitations related to scale and spatial accuracy issues, as well as data availability. The [...] Read more.
The exploitation of remote sensing techniques has substantially improved pre- and post- disaster landslide management over the last decade. A variety of landslide susceptibility methods exists, with capabilities and limitations related to scale and spatial accuracy issues, as well as data availability. The Interferometric Synthetic Aperture Radar (InSAR) capabilities have significantly contributed to the detection, monitoring, and mapping of landslide phenomena. The present study aims to point out the contribution of InSAR data in landslide detection and to evaluate two different scale landslide models by comparing a heuristic to a statistical method for the rainfall-induced landslide hazard assessment. Aiming to include areas with both high and low landslide occurrence frequencies, the study area covers a large part of the Aetolia–Acarnania and Evritania prefectures, Central and Western Greece. The landslide susceptibility product provided from the weights of evidence (WoE) method proved more accurate, benefitting from the expert opinion and the landslide inventory. On the other hand, the Norwegian Geological Institute (NGI) methodology has the edge on its immediate implementation, with minimum data requirements. Finally, it was proved that using sequential SAR image acquisitions gives the benefit of an updated landslide inventory, resulting in the generation of, on request, updated landslide susceptibility maps. Full article
(This article belongs to the Special Issue Using Time Series Analysis of Remote Sensing Images to Detect Changes in Land Condition)
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