Special Issue "Remote Sensing Analysis of Agricultural Landscapes"

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Landscape Ecology".

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editors

Prof. Dr. Anders Wästfelt
E-Mail Website
Guest Editor
Department of human geography, Stockholm University, 114 19 Stockholm, Sweden
Interests: agriculture geography; remote sensing; welfare economics
Prof. Dr. Alejandro Rescia
E-Mail Website
Guest Editor
Department of Biodiversity, Ecology and Evolution, Complutense University Madrid, 28040 Madrid, Spain
Interests: ecology; landscape ecology; biodiversity conservation; restoration; socio-ecological systems; spatial resilience, cultural landscapes
Special Issues and Collections in MDPI journals
Prof. Dr. Samir Sayadi Gmada
E-Mail Website1 Website2
Guest Editor
Department of Agri-Food Chain Economics, Institute of Agricultural Research and Training (IFAPA), 18080 Granada, Spain
Interests: agricultural residues; waste and byproduct sustainable management; circular bioeconomy; landscape and ecosystem services valuation; multifunctionality of agriculture; sustainable rural development; sustainable agri-food value chain and labels; new consumers/social demands and concerns; sustainable tourism
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Agricultural landscapes all over the world are vital for food provisioning, but they are also a representation of natural and cultural heritage. In contemporary times, the globalization of economies and climate change are inducing strong pressure to adapt to new circumstances and agricultural landscapes are changing rapidly everywhere. Remote sensing has been used for the analysis of agriculture productivity and precision farming, and, more seldom, for landscape analysis, but the potential for further development is huge. This Special Issue invites all kinds of remote landscape studies, which combine agricultural studies and landscape analysis with the use of remote sensed data. Both quantitative and qualitative studies are welcomed.

Prof. Dr. Anders Wästfelt
Prof. Dr. Alejandro Rescia
Prof. Dr. Samir Sayadi Gmada
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 papers will be 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 1800 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

  • agricultural landscapes
  • landscape analysis
  • satellite images
  • food production
  • natural and cultural heritage

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Exploring the Regional Dynamics of U.S. Irrigated Agriculture from 2002 to 2017
Land 2021, 10(4), 394; https://doi.org/10.3390/land10040394 - 09 Apr 2021
Viewed by 930
Abstract
The United States has a geographically mature and stable land use and land cover system including land used as irrigated cropland; however, changes in irrigation land use frequently occur related to various drivers. We applied a consistent methodology at a 250 m spatial [...] Read more.
The United States has a geographically mature and stable land use and land cover system including land used as irrigated cropland; however, changes in irrigation land use frequently occur related to various drivers. We applied a consistent methodology at a 250 m spatial resolution across the lower 48 states to map and estimate irrigation dynamics for four map eras (2002, 2007, 2012, and 2017) and over four 5-year mapping intervals. The resulting geospatial maps (called the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset or MIrAD-US) involved inputs from county-level irrigated statistics from the U.S. Department of Agriculture, National Agricultural Statistics Service, agricultural land cover from the U.S. Geological Survey National Land Cover Database, and an annual peak vegetation index derived from expedited MODIS satellite imagery. This study investigated regional and periodic patterns in the amount of change in irrigated agriculture and linked gains and losses to proximal causes and consequences. While there was a 7% overall increase in irrigated area from 2002 to 2017, we found surprising variability by region and by 5-year map interval. Irrigation land use dynamics affect the environment, water use, and crop yields. Regionally, we found that the watersheds with the largest irrigation gains (based on percent of area) included the Missouri, Upper Mississippi, and Lower Mississippi watersheds. Conversely, the California and the Texas–Gulf watersheds experienced fairly consistent irrigation losses during these mapping intervals. Various drivers for irrigation dynamics included regional climate fluctuations and drought events, demand for certain crops, government land or water policies, and economic incentives like crop pricing and land values. The MIrAD-US (Version 4) was assessed for accuracy using a variety of existing regionally based reference data. Accuracy ranged between 70% and 95%, depending on the region. Full article
(This article belongs to the Special Issue Remote Sensing Analysis of Agricultural Landscapes)
Show Figures

Figure 1

Article
Cropland Abandonment in Slovakia: Analysis and Comparison of Different Data Sources
Land 2021, 10(4), 334; https://doi.org/10.3390/land10040334 - 25 Mar 2021
Viewed by 505
Abstract
This study compares different nationwide multi-temporal spatial data sources and analyzes the cropland area, cropland abandonment rates and transformation of cropland to other land cover/land use categories in Slovakia. Four multi-temporal land cover/land use data sources were used: The Historic Land Dynamics Assessment [...] Read more.
This study compares different nationwide multi-temporal spatial data sources and analyzes the cropland area, cropland abandonment rates and transformation of cropland to other land cover/land use categories in Slovakia. Four multi-temporal land cover/land use data sources were used: The Historic Land Dynamics Assessment (HILDA), the Carpathian Historical Land Use Dataset (CHLUD), CORINE Land Cover (CLC) data and Landsat images classification. We hypothesized that because of the different spatial, temporal and thematic resolution of the datasets, there would be differences in the resulting cropland abandonment rates. We validated the datasets, compared the differences, interpreted the results and combined the information from the different datasets to form an overall picture of long-term cropland abandonment in Slovakia. The cropland area increased until the Second World War, but then decreased after transition to the communist regime and sharply declined following the 1989 transition to an open market economy. A total of 49% of cropland area has been transformed to grassland, 34% to forest and 15% to urban areas. The Historical Carpathian dataset is the more reliable long-term dataset, and it records 19.65 km2/year average cropland abandonment for 1836–1937, 154.44 km2/year for 1938–1955 and 140.21 km2/year for 1956–2012. In comparison, the Landsat, as a recent data source, records 142.02 km2/year abandonment for 1985–2000 and 89.42 km2/year for 2000–2010. These rates, however, would be higher if the dataset contained urbanisation data and more precise information on afforestation. The CORINE Land Cover reflects changes larger than 5 ha, and therefore the reported cropland abandonment rates are lower. Full article
(This article belongs to the Special Issue Remote Sensing Analysis of Agricultural Landscapes)
Show Figures

Graphical abstract

Article
Mapping Impacts of Human Activities from Nighttime Light on Vegetation Cover Changes in Southeast Asia
Land 2021, 10(2), 185; https://doi.org/10.3390/land10020185 - 11 Feb 2021
Viewed by 592
Abstract
It is commonly believed that the impacts of human activities have decreased the natural vegetation cover, while some promotion of the vegetation growth has also been found. In this study, negative or positive correlations between human impacts and vegetation cover were tested in [...] Read more.
It is commonly believed that the impacts of human activities have decreased the natural vegetation cover, while some promotion of the vegetation growth has also been found. In this study, negative or positive correlations between human impacts and vegetation cover were tested in the Southeast Asia (SEA) region during 2012–2018. The Visible Infrared Imaging Radiometer Suite—Day/Night Band (VIIRS/DNB) nocturnal data were used as a measure of human activities and the moderate resolution imaging spectroradiometer (MODIS)/normalized difference vegetation index (NDVI) diurnal data were used as a measure of vegetation cover. The temporal segmentation method was introduced to calculate features of two sets of time series with spatial resolution of about 500 m, including the overall trend, maximum trend, start date, and change duration. The regions with large variation in human activities (V-change region) were first extracted by the Gaussian fitted method, and 8.64% of the entire SEA (VIIRS overall trend <−0.2 or >0.4) was set as the target analysis area. According to statistics, the average overall VIIRS trend for the V-change region in SEA was about 2.12, with a slight NDVI increment. The time lag effect was also found between vegetation cover and human impacts change, with an average of 10.26 months. Our results indicated a slight green overall trend in the SEA region over the most recent 7 years. The spatial pattern of our trend analysis results can be useful for vegetation management and regional planning. Full article
(This article belongs to the Special Issue Remote Sensing Analysis of Agricultural Landscapes)
Show Figures

Figure 1

Back to TopTop