New Insights in Remote Sensing of Land Use

A special issue of Land (ISSN 2073-445X).

Deadline for manuscript submissions: closed (25 October 2022) | Viewed by 13225

Special Issue Editors


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Guest Editor
National Research Council (CNR), Institute of Methodologies for Environmental Analysis (IMAA), 85050 Tito Scalo, PZ, Italy
Interests: land cover dynamics; earth-surface/climate interactions; EO data for land cover monitoring and modelling; land degradation and desertification; time series analysis
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
National Research Council (CNR), Institute of Methodologies for Environmental Analysis (IMAA), 85050 Tito Scalo (PZ), Italy
Interests: satellite remote sensing; fire detection and monitoring, volcano monitoring, natural hazards

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Guest Editor
School of Life and Environmental Sciences, Schmid College of Science and Technology, Chapman University, One University Drive, Orange, CA 92866, USA
Interests: remote sensing; natural hazards; urban environment; atmospheric pollution
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of remote sensing (RS) technologies for mapping, monitoring and assessing the land use (LU) and its changes (LUC) has become widespread in the recent years. In fact, thanks to the capability of the RS systems to collect systematically and repetitively data, accurate information of the Earth’s cover has been acquired at various spatial, spectral and temporal resolutions at global scale.

Moreover, thanks to the current policies (i.e., Full, Open and Free data access) of Earth-Observing imagery access (e.g., EC ESA Sentinel imagery) and the free access to different EO data catalogues (e.g., U.S. Geological Survey-Landsat), the huge amount of the free available satellite data has accelerated the development of innovative approaches aimed to better evaluate LU/LUC. On the other hand, continuous technological improvements and the increasing computational capacities of the cloud-based systems (e.g., Google Earth Engine and Copernicus DIAS) have started new challenges not only in the RS community.

For this Special Issue, we welcome research papers focusing on:

  • Use of optical and radar RS data for LU mapping/monitoring;
  • Long-term and multi-temporal LU/LUC assessment by means of RS technologies (UAV, aerial, satellite, LiDAR) and/or their integration and combination;
  • RS applications for LUC in the urban, agriculture and natural areas;
  • Development of new tools and models for evaluating LU/LUC that can support regional and local policies/actions devoted to natural hazard risk reduction;
  • Insight on LU trends and their impact on the climate changes;

Review papers on the use of RS technologies in the field of LU/LUC are also welcome.

Dr. Nicola Genzano
Dr. Maria Lanfredi
Dr. Giuseppe Mazzeo
Prof. Dr. Ramesh P. Singh
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. 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.

Keywords

  • remote sensing
  • land use
  • optical, thermal and SAR measurements
  • change detection
  • time-series
  • natural hazard
  • climate change
  • cloud-based systems
  • environmental impacts
  • urban and rural areas

Published Papers (4 papers)

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Research

25 pages, 5422 KiB  
Article
Multi-Decadal Assessment of Soil Loss in a Mediterranean Region Characterized by Contrasting Local Climates
by Caterina Samela, Vito Imbrenda, Rosa Coluzzi, Letizia Pace, Tiziana Simoniello and Maria Lanfredi
Land 2022, 11(7), 1010; https://doi.org/10.3390/land11071010 - 2 Jul 2022
Cited by 13 | Viewed by 2042
Abstract
Soil erosion is one of the most widespread soil degradation phenomena worldwide. Mediterranean landscapes, due to some peculiar characteristics, such as fragility of soils, steep slopes, and rainfall distribution during the year, are particularly subject to this phenomenon, with severe and complex issues [...] Read more.
Soil erosion is one of the most widespread soil degradation phenomena worldwide. Mediterranean landscapes, due to some peculiar characteristics, such as fragility of soils, steep slopes, and rainfall distribution during the year, are particularly subject to this phenomenon, with severe and complex issues for agricultural production and biodiversity protection. In this paper, we present a diachronic approach to the analysis of soil loss, which aims to account for climate variability and land cover dynamics by using remote data about rainfall and land cover to guarantee sufficient observational continuity. The study area (Basilicata, Southern Italy) is characterized by different local climates and ecosystems (temperate, Csa and Csb; arid steppic, Bsk; and cold, Dsb and Dsc), and is particularly suited to represent the biogeographical complexity of the Mediterranean Italy. The well-known Revised Universal Soil Loss Equation (RUSLE) was applied by integrating information from remote sensing to carry out decadal assessments (1994, 2004, 2014, and 2021) of the annual soil loss. Changes in the rainfall regime and vegetation cover activity were derived from CHIRPS and Landsat data, respectively, to obtain updated information useful for dynamical studies. For the analyzed region, soil loss shows a slight reduction (albeit always remarkable) over the whole period, and distinct spatial patterns between lowland Bsk and Mediterranean mountain Dsb and Dsc climate areas. The most alarming fact is that most of the study area showed soil erosion rates in 2021 greater than 11 t/ha*y, which is considered by the OECD (Organization for Economic Cooperation and Development) the threshold for identifying severe erosion phenomena. A final comparison with local studies shows, on average, differences of about 5 t ha−1 y−1 (minimum 2.5 and maximum 7) with respect to the local estimates obtained with the RUSLE model. The assessment at a regional scale provided an average 9.5% of soil loss difference for the arable lands and about 10% for all cultivated areas. The spatial-temporal patterns enhance the relevance of using the cover management factor C derived from satellite data rather than land cover maps, as remote observations are able to highlight the heterogeneity in vegetation density within the same vegetation cover class, which is particularly relevant for agricultural areas. For mountain areas, the adoption of a satellite-gridded rainfall dataset allowed the detection of erosion rate fluctuations due to rainfall variability, also in the case of sparse or absent ground pluviometric stations. The use of remote data represents a precious added value to obtain a dynamic picture of the spatial-temporal variability of soil loss and new insights into the sustainability of soil use in a region whose economy is mostly based on agriculture and the exploitation of natural resources. Full article
(This article belongs to the Special Issue New Insights in Remote Sensing of Land Use)
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18 pages, 5969 KiB  
Article
Robust Satellite-Based Identification and Monitoring of Forests Having Undergone Climate-Change-Related Stress
by Carolina Filizzola, Maria Antonia Carlucci, Nicola Genzano, Emanuele Ciancia, Mariano Lisi, Nicola Pergola, Francesco Ripullone and Valerio Tramutoli
Land 2022, 11(6), 825; https://doi.org/10.3390/land11060825 - 31 May 2022
Cited by 5 | Viewed by 2479
Abstract
Climate-induced drought events are responsible for forest decline and mortality in different areas of the world. Forest response to drought stress periods may be different, in time and space, depending on vegetation type and local factors. Stress analysis may be carried out by [...] Read more.
Climate-induced drought events are responsible for forest decline and mortality in different areas of the world. Forest response to drought stress periods may be different, in time and space, depending on vegetation type and local factors. Stress analysis may be carried out by using field methods, but the use of remote sensing may be needed to highlight the effects of climate-change-induced phenomena at a larger spatial and temporal scale. In this context, satellite-based analyses are presented in this work to evaluate the drought effects during the 2000s and the possible climatological forcing over oak forests in Southern Italy. To this aim, two approaches based on the well-known Normalized Difference Vegetation Index (NDVI) were used: one based on NDVI values, averaged over selected decaying and non-decaying forests; another based on the Robust Satellite Techniques (RST). The analysis of the first approach mainly gave us overall information about 1984–2011 rising NDVI trends, despite a general decrease around the 2000s. The second, more refined approach was able to highlight a different drought stress impact over decaying and non-decaying forests. The combined use of the RST-based approach, Landsat satellite data, and Google Earth Engine (GEE) platform allowed us to identify in space domain and monitor over time significant oak forest changes and climate-driven effects (e.g., in 2001) from the local to the Basilicata region scale. By this way, the decaying status of the Gorgoglione forest was highlighted two years before the first visual field evidence (e.g., dryness of apical branches, bark detachment, root rot disease). The RST exportability to different satellite sensors and vegetation types, the availability of suitable satellite data, and the potential of GEE suggest the possibility of long-term monitoring of forest health, from the local to the global scale, to provide useful information to different end-user classes. Full article
(This article belongs to the Special Issue New Insights in Remote Sensing of Land Use)
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27 pages, 10401 KiB  
Article
Thirty Years of Land Use/Land Cover Changes and Their Impact on Urban Climate: A Study of Kano Metropolis, Nigeria
by Auwalu Faisal Koko, Yue Wu, Ghali Abdullahi Abubakar, Akram Ahmed Noman Alabsi, Roknisadeh Hamed and Muhammed Bello
Land 2021, 10(11), 1106; https://doi.org/10.3390/land10111106 - 20 Oct 2021
Cited by 8 | Viewed by 3655
Abstract
Rapid urban expansion and the alteration of global land use/land cover (LULC) patterns have contributed substantially to the modification of urban climate, due to variations in Land Surface Temperature (LST). In this study, the LULC change dynamics of Kano metropolis, Nigeria, were analysed [...] Read more.
Rapid urban expansion and the alteration of global land use/land cover (LULC) patterns have contributed substantially to the modification of urban climate, due to variations in Land Surface Temperature (LST). In this study, the LULC change dynamics of Kano metropolis, Nigeria, were analysed over the last three decades, i.e., 1990–2020, using multispectral satellite data to understand the impact of urbanization on LST in the study area. The Maximum Likelihood classification method and the Mono-window algorithm were utilised in classifying land uses and retrieving LST data. Spectral indices comprising the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) were also computed. A linear regression analysis was employed in order to examine the correlation between land surface temperature and the various spectral indices. The results indicate significant LULC changes and urban expansion of 152.55 sq. km from 1991 to 2020. During the study period, the city’s barren land and water bodies declined by approximately 172.58 sq. km and 26.55 sq. km, respectively, while vegetation increased slightly by 46.58 sq. km. Further analysis showed a negative correlation between NDVI and LST with a Pearson determination coefficient (R2) of 0.6145, 0.5644, 0.5402, and 0.5184 in 1991, 2000, 2010, and 2020 respectively. NDBI correlated positively with LST, having an R2 of 0.4132 in 1991, 0.3965 in 2000, 0.3907 in 2010, and 0.3300 in 2020. The findings of this study provide critical climatic data useful to policy- and decision-makers in optimizing land use and mitigating the impact of urban heat through sustainable urban development. Full article
(This article belongs to the Special Issue New Insights in Remote Sensing of Land Use)
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18 pages, 88184 KiB  
Article
Mapping Rural Settlements from Landsat and Sentinel Time Series by Integrating Pixel- and Object-Based Methods
by Ru Xu
Land 2021, 10(3), 244; https://doi.org/10.3390/land10030244 - 1 Mar 2021
Cited by 10 | Viewed by 3558
Abstract
Rural settlements account for 45% of the world’s population and are targeted places for poverty eradication. However, compared to urban footprints, the distribution of rural settlements is not well characterized in most existing land use and land cover maps because of their patchy [...] Read more.
Rural settlements account for 45% of the world’s population and are targeted places for poverty eradication. However, compared to urban footprints, the distribution of rural settlements is not well characterized in most existing land use and land cover maps because of their patchy and scattered organization and relative stability over time. In this study, we proposed a pixel- and object-based method to map rural settlements by employing spectral-texture-temporal information from Landsat and Sentinel time series. Spectral indices (maximum normalized difference vegetation index (NDVI) and minimum normalized difference built-up index (NDBI composite) and texture indices (vertical transmit and vertical receive (VV) polarization of mean synthetic aperture radar (SAR) composite) were calculated from all available Landsat and Sentinel-1A data from 1 January 2016 to 31 December 2018. These features were then stacked for segmentation to extract potential rural settlement objects. To better differentiate settlements from bare soil, the gradient of annual NDVI maximum (namely, gradient of change, use gradient for simplicity) from 1 January 1987 to 31 December 2018 was used. The rural training samples were selected from global urban footprint (GUF) products with a post filtering process to remove sample noise. Scatter plots between pixel- and object-based values per feature were delineated by t-distribution ellipses to determine the thresholds. Finally, pixel- and object-based thresholds were applied to four features (NDVI, NDBI, VV, gradient) in Google Earth Engine (GEE) to obtain the distribution of rural settlements in eight selected Asian regions. The derived maps of rural settlements showed consistent accuracy, with a producer’s accuracy (PA) of 0.87, user’s accuracy (UA) of 0.93 and overall accuracy (OA) reaching 90% in different landscape conditions, which are better than existing land cover products. Full article
(This article belongs to the Special Issue New Insights in Remote Sensing of Land Use)
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