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Remote Sensing for Land Change Science: Looking at Land Surface as a Coupled Human-Environment System

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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: closed (31 August 2024) | Viewed by 11162

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Special Issue Editors

Dr. Maria Lanfredi

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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
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Dr. Rosa Coluzzi

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Guest Editor

National Research Council (CNR), Institute of Methodologies for Environmental Analysis (IMAA), 85050 Tito Scalo, PZ, Italy
Interests: land-atmosphere processes; land change trade-offs for ecosystem services and biodiversity
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Dr. Vito Imbrenda

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Guest Editor

National Research Council (CNR), Institute of Methodologies for Environmental Analysis (IMAA), 85050 Tito Scalo, PZ, Italy
Interests: ecological remote sensing; GIS; landscape ecology; landscape metrics; land use management; land cover and land use change; spatial analysis; ecosystem services and goods
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Dr. Tiziana Simoniello

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Guest Editor

National Research Council (CNR), Institute of Methodologies for Environmental Analysis (IMAA), 85050 Tito Scalo, PZ, Italy
Interests: EO data calibration and processing; land surface phenology; land degradation; RS in forestry and natural resource management; RS in ecology and conservation; EO data integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is the natural continuation of our previous issue, entitled “Advances of Remote Sensing in the Analysis of the Spatial and Temporal Variability of Land Surface”, which shared Editors with the present edition and recently ended successfully. We aim to frame land surface research in the context of environmental change and sustainability by focusing on the complexity of the dynamics at the interface of physical, ecological, and social systems. Land change science relies on the integration of a wide range of data and analysis methods. Additionally, remote sensing is a major source of information for the accurate monitoring of land-change rates and patterns, creating long-term records to quantify change over time on the local and the global scales. On the basis of these data, researchers can determine the origins and consequences of the observed changes, predict the impact of future changes, and use new knowledge to inform strategic land management and policy making.

We aim to collect papers on the recent advances in the use of remote sensing to evaluate land change patterns/processes and the impacts of interconnected environmental and social issues, with a particular focus on urbanization, deforestation, land take, and natural disasters.

Topics of interest include but are not limited to:

Traditional and new remote sensing sensors/products for monitoring land surface change;
Land use change drivers and impacts;
Multifunctional landscapes;
Resilience and vegetation recovery;
Forest dynamics and anthropic impacts;
Natural Capital accounting;
Land degradation and desertification;
Interplay between changes in climate, land use, and land cover;
Urban sprawl and sustainability;
Ecological sustainability;
Geohazard (floods, landslides, drought, etc.);
Land surface energy fluxes;
Inland water dynamics;
Retrieving, mapping, and time-series recording;
Airborne/spaceborne lidar applications;
EO observations to support decision-making processes;
Space economy;
Linkages between proposed causal variables and land change;
Land management and land policy;
Night-light data to estimate social and economic activities.

Dr. Maria Lanfredi
Dr. Rosa Coluzzi
Dr. Vito Imbrenda
Dr. Tiziana Simoniello
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

sustainability
land change detection
climate
geohazard
natural capital
anthropic drivers of land change
space economy

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Published Papers (6 papers)

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21 pages, 42176 KiB

Open AccessArticle

Application of Getis-Ord Correlation Index (Gi) for Burned Area Detection Improvement in Mediterranean Ecosystems (Southern Italy and Sardinia) Using Sentinel-2 Data

by Antonio Lanorte, Gabriele Nolè and Giuseppe Cillis

Remote Sens. 2024, 16(16), 2943; https://doi.org/10.3390/rs16162943 - 12 Aug 2024

Cited by 1 | Viewed by 961

Abstract

This study collects the results obtained using the Getis-Ord local spatial autocorrelation index (Gi) with the aim of improving the classification of burned area detection maps generated from spectral indices (i.e., dNBR index) derived from Sentinel-2 satellite data. Therefore, the work proposes an adaptive thresholding approach that also includes the application of a similarity index (Sorensen–Dice Similarity Index) with the aim of adaptively correcting classification errors (false-positive burned pixels) related to the spectral response of burned/unburned areas. In this way, two new indices derived from the application of the Getis-Ord local autocorrelation analysis were created to test their effectiveness. Three wildfire events were considered, two of which occurred in Southern Italy in the summer of 2017 and one in Sardinia in the summer of 2019. The accuracy assessment analysis was carried out using the CEMS (Copernicus Emergency Management Service) on-demand maps. The results show the remarkable performance of the two new indices in terms of their ability to reduce the false positives generated by dNBR. In the three sites considered, the false-positive reduction percentage was around 95–96%. The proposed approach seems to be adaptable to different vegetation contexts, and above all, it could be a useful tool for mapping burned areas to support post-fire management activities. Full article

(This article belongs to the Special Issue Remote Sensing for Land Change Science: Looking at Land Surface as a Coupled Human-Environment System)

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27 pages, 13334 KiB

Open AccessArticle

Influences of Climate Variability on Land Use and Land Cover Change in Rural South Africa

by Buster Percy Mogonong, Wayne Twine, Gregor Timothy Feig, Helga Van der Merwe and Jolene T. Fisher

Remote Sens. 2024, 16(7), 1200; https://doi.org/10.3390/rs16071200 - 29 Mar 2024

Cited by 1 | Viewed by 1498

Abstract

Changes in land use and land cover over space and time are an indication of biophysical, socio-economic, and political dynamics. In rural communities, land-based livelihood strategies such as agriculture are crucial for sustaining livelihoods in terms of food provision and as a source of local employment and income. In recent years, African studies have documented an overall decline in the extent of small-scale crop farming, with many crop fields left abandoned. This study uses rural areas in three former apartheid homelands in South Africa as a case study to quantify patterns and trends in the overall land cover change and small-scale agricultural lands related to changes in climate over a 38-year period. Random forest classification was applied on the Landsat imagery to detect land use and land cover change, achieving an overall accuracy of above 80%. Rainfall and temperature anomalies, as well as the Standardized Precipitation Evapotranspiration Index (SPEI) were used as climate proxies to assess the influence of climate variability on crop farming, as the systems investigated rely completely on rainfall. Agricultural land declined from 107.5 km² to 49.5 km² in Umhlabuyalingana; 54 km² to 1.6 km² in Joe Morolong; and 254.6 km² to 7.4 km² in Mangaung between 1984 and 2022. Declines in cropland cover, precipitation, and the SPEI were highly correlated. We argue that climatic variability influences crop farming activities; however, this could be one factor in a suite of drivers that interact together to influence the cropping practices in rural areas. Full article

(This article belongs to the Special Issue Remote Sensing for Land Change Science: Looking at Land Surface as a Coupled Human-Environment System)

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20 pages, 3676 KiB

Open AccessArticle

Improving Colored Dissolved Organic Matter (CDOM) Retrievals by Sentinel2-MSI Data through a Total Suspended Matter (TSM)-Driven Classification: The Case of Pertusillo Lake (Southern Italy)

by Emanuele Ciancia, Alessandra Campanelli, Roberto Colonna, Angelo Palombo, Simone Pascucci, Stefano Pignatti and Nicola Pergola

Remote Sens. 2023, 15(24), 5718; https://doi.org/10.3390/rs15245718 - 13 Dec 2023

Cited by 1 | Viewed by 1312

Abstract

Colored dissolved organic matter (CDOM) is a significant constituent of aquatic systems and biogeochemical cycles. Satellite CDOM retrievals are challenging in inland waters, due to overlapped absorption properties of bio-optical parameters, like Total Suspended Matter (TSM). In this framework, we defined an accurate CDOM model using Sentinel2-MSI (S2-MSI) data in Pertusillo Lake (Southern Italy) adopting a classification scheme based on satellite TSM data. Empirical relationships were established between the CDOM absorption coefficient, a_CDOM (440), and reflectance band ratios using ground-based measurements. The Green-to-Red (B3/B4 and B3/B5) and Red-to-Blue (B4/B2 and B5/B2) band ratios showed good relationships (R² ≥ 0.75), which were further improved according to sub-region division (R² up to 0.93). The best accuracy of B3/B4 in the match-ups between S2-MSI-derived and in situ band ratios proved the exportability on S2-MSI data of two B3/B4-based a_CDOM (440) models, namely the fixed (for the whole PL) and the switching one (according to sub-region division). Although they both exhibited good agreements in a_CDOM (440) retrievals (R² ≥ 0.69), the switching model showed the highest accuracy (RMSE of 0.0155 m⁻¹). Finally, the identification of areas exposed to different TSM patterns can assist with refining the calibration/validation procedures to achieve more accurate a_CDOM (440) retrievals. Full article

(This article belongs to the Special Issue Remote Sensing for Land Change Science: Looking at Land Surface as a Coupled Human-Environment System)

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21 pages, 6548 KiB

Open AccessArticle

Investigating the Identification and Spatial Distribution Characteristics of Camellia oleifera Plantations Using High-Resolution Imagery

by Yajing Li, Enping Yan, Jiawei Jiang, Dan Cao and Dengkui Mo

Remote Sens. 2023, 15(21), 5218; https://doi.org/10.3390/rs15215218 - 2 Nov 2023

Cited by 1 | Viewed by 1390

Abstract

Camellia oleifera is a vital economic crop of southern China. Accurate mapping and monitoring of Camellia oleifera plantations are essential for promoting sustainable operations within the Camellia oleifera industry. However, traditional remote sensing interpretation methods are no longer feasible for the large-scale extraction of plantation areas. This study proposes a novel deep learning-based method that utilizes GF-2 remote sensing imagery to achieve precise mapping and efficient monitoring of Camellia oleifera plantations. First, we conducted a comparative analysis of the performance of various semantic segmentation models using a self-compiled dataset of Camellia oleifera plantations. Subsequently, we proceeded to validate the prediction results obtained from the most effective deep-learning network model for Camellia oleifera plantations in Hengyang City. Finally, we incorporated DEM data to analyze the spatial distribution patterns. The findings indicate that the U-Net++ network model outperforms other semantic segmentation methods when applied to our self-generated dataset of Camellia oleifera plantations. It achieves a recall rate of 0.89, a precision rate of 0.92, and an mIOU of 0.83, demonstrating the effectiveness of the proposed method in identifying and monitoring Camellia oleifera plantations. By combining the predicted results with the data from DEM, we discovered that these plantations are typically situated at elevations ranging from 50 to 200 m, with slopes below 25°, and facing south or southeast. Moreover, a significant positive spatial correlation and clustering phenomenon are observed among the townships in Hengyang City. The method proposed in this study facilitates rapid and precise identification and monitoring of Camellia oleifera plantations, offering significant theoretical support and a scientific foundation for the management and ecological conservation of Camellia oleifera plantations. Full article

(This article belongs to the Special Issue Remote Sensing for Land Change Science: Looking at Land Surface as a Coupled Human-Environment System)

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17 pages, 7034 KiB

Open AccessArticle

Do Ecological Restoration Projects Undermine Economic Performance? A Spatially Explicit Empirical Study in Loess Plateau, China

by Shicheng Li, Jinqian Xie and Basanta Paudel

Remote Sens. 2023, 15(12), 3035; https://doi.org/10.3390/rs15123035 - 9 Jun 2023

Cited by 7 | Viewed by 3225

Abstract

Exploring the complex relationship between ecological restoration and economic development is valuable for decision makers to formulate policy for sustainable development. The large-scale environmental restoration program—Grain for Green—was mainly implemented in the Loess Plateau of China to improve the soil retention service. However, whether this world-famous program affects local economic development has not been fully explored. In this study, using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and spatializing the gross domestic product (GDP) based on the remotely sensed nightlight data, we explored the tradeoff between environment (i.e., soil retention service) and economy (i.e., GDP) for the Loess Plateau in a spatially explicit way. We found that the soil retention service increased prominently over the past 40 years, especially after implementing the Grain for Green project. Meanwhile, the GDP increased about nine-fold over the past four decades from 4.52 to 40.29 × 10⁷ USD. A win–win situation of soil retention and economic development was achieved in the Loess Plateau of China, particularly in the loess gully and loess hilly gully regions of the Loess Plateau. The win–win situation of soil retention and economic development was as a result of the Grain for Green program, the optimization of industrial structure, and the increase in non-agriculture employment. Compared with previous studies, more spatial information was available for the Loess Plateau in this study, which is more valuable to policymakers. Full article

(This article belongs to the Special Issue Remote Sensing for Land Change Science: Looking at Land Surface as a Coupled Human-Environment System)

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14 pages, 2553 KiB

Open AccessTechnical Note

Socioeconomic and Climate Effects on Changes in Wetlands in China during a Three-Decade Period of Rapid Growth

by Ying Ge, Petr Sklenička and Zhongbing Chen

Remote Sens. 2023, 15(6), 1683; https://doi.org/10.3390/rs15061683 - 21 Mar 2023

Cited by 1 | Viewed by 1830

Abstract

China has experienced dramatic economic growth and social development, especially in the period between 1978 and 2008. The biodiversity and the socioeconomic sustainability in China were under threat, and the loss of wetlands was a significant aspect of ecological deterioration in the country at that time. However, the driving factors for the loss of wetlands are not well understood, probably due to a lack of accurate country-scale data. This study analyzes the changes in China’s wetland area between 1978 and 2008 (1978, 1990, 2000, and 2008) and the interchange between different wetland types from 1990 to 2000. We select 29 socioeconomic parameters (per capita GDP, primary industry added value, secondary industry ratio, total population, arable land, pesticide use, aquatic products, railway mileage, domestic wastewater, urban sewage treatment capacity, etc.) and three meteorological parameters (annual temperature, annual precipitation, and annual sunshine) to analyze the driving forces of changes in wetlands. The factor analysis based on these parameters shows that two factors can explain 65.8% of the total variation from the data, while eight parameters can explain 59.7%. Furthermore, multiple linear regression analysis reveals that five factors are of great significance in explaining wetland change in China, which are annual temperature (p < 0.001), inland waterway mileage (p < 0.001), urban land acquisition (p = 0.01), secondary industry ratio (p = 0.014), and railway mileage (p = 0.02). In conclusion, climate change (especially temperature) and inland waterway mileage are the primary factors for changes in the wetlands in China, and other socioeconomic indicators, especially from industrial and construction factors, also play an important role in changes in wetlands during China’s rapid economic development. In order to enhance wetland conservation efforts in China, we recommend prioritizing efforts to mitigate climate change on wetlands, promoting sustainable development policies, restoring and creating wetlands in urban areas, and utilizing advanced technologies to obtain accurate data. Full article

(This article belongs to the Special Issue Remote Sensing for Land Change Science: Looking at Land Surface as a Coupled Human-Environment System)

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