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: closed (31 May 2022) | Viewed by 8022

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
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
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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

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Keywords

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

Published Papers (6 papers)

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Research

Article
Multitemporal Spatial Analysis of Land Use and Land Cover Changes in the Lower Jaguaribe Hydrographic Sub-Basin, Ceará, Northeast Brazil
Land 2022, 11(1), 103; https://doi.org/10.3390/land11010103 - 08 Jan 2022
Cited by 1 | Viewed by 588
Abstract
Aquaculture is currently one of the fastest growing food production systems globally, and shrimp is considered one of the most highly valued products. Our study area is the lower Jaguaribe River sub-basin (LJRSB), located in the northeastern part of Ceará in Brazil. The [...] Read more.
Aquaculture is currently one of the fastest growing food production systems globally, and shrimp is considered one of the most highly valued products. Our study area is the lower Jaguaribe River sub-basin (LJRSB), located in the northeastern part of Ceará in Brazil. The aquaculture activity in this area began in the early 1990s and is currently one of the largest shrimp producers in Brazil. This study generated a spatial-temporal analysis of vegetation index and land use and land cover (LULC) using remote sensing images from Landsat satellites processed using geographic information systems (GIS). The findings showed an increase in the water bodies class where shrimp farms are found. In addition, to help us discuss the results, data from the Global Surface Water Explorer was also used to understand this change throughout intra and interannual water variability. Besides shrimp farms’ intensification, agricultural areas in the LJRSB also increased, mainly in the irrigated perimeter lands (IPLs), causing a loss in the Caatinga native vegetation. In summary, over recent years, significant changes have been noticeable in the LJRSB coastal region, caused by an increase in shrimp farms mainly located on the Jaguaribe River margins, destroying the native riparian forest. Full article
(This article belongs to the Special Issue Remote Sensing Analysis of Agricultural Landscapes)
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Article
Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy
Land 2021, 10(11), 1221; https://doi.org/10.3390/land10111221 - 10 Nov 2021
Cited by 1 | Viewed by 618
Abstract
A sustainable bioeconomy would require growing high-yielding bioenergy crops on marginal agricultural areas with minimal inputs. To determine the cost competitiveness and environmental sustainability of such production systems, reliably estimating biomass yield is critical. However, because marginal areas are often small and spread [...] Read more.
A sustainable bioeconomy would require growing high-yielding bioenergy crops on marginal agricultural areas with minimal inputs. To determine the cost competitiveness and environmental sustainability of such production systems, reliably estimating biomass yield is critical. However, because marginal areas are often small and spread across the landscape, yield estimation using traditional approaches is costly and time-consuming. This paper demonstrates the (1) initial investigation of optical remote sensing for predicting perennial bioenergy grass yields at harvest using a linear regression model with the green normalized difference vegetation index (GNDVI) derived from Sentinel-2 imagery and (2) evaluation of the model’s performance using data from five U.S. Midwest field sites. The linear regression model using midsummer GNDVI predicted yields at harvest with R2 as high as 0.879 and a mean absolute error and root mean squared error as low as 0.539 Mg/ha and 0.616 Mg/ha, respectively, except for the establishment year. Perennial bioenergy grass yields may be predicted 152 days before the harvest date on average, except for the establishment year. The green spectral band showed a greater contribution for predicting yields than the red band, which is indicative of increased chlorophyll content during the early growing season. Although additional testing is warranted, this study showed a great promise for a remote sensing approach for forecasting perennial bioenergy grass yields to support critical economic and logistical decisions of bioeconomy stakeholders. Full article
(This article belongs to the Special Issue Remote Sensing Analysis of Agricultural Landscapes)
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Article
Evaluating Landscape Attractiveness with Geospatial Data, A Case Study in Flanders, Belgium
Land 2021, 10(7), 703; https://doi.org/10.3390/land10070703 - 03 Jul 2021
Cited by 5 | Viewed by 2384
Abstract
Attractive landscapes are diverse and resilient landscapes that provide a multitude of essential ecosystem services. The development of landscape policy to protect and improve landscape attractiveness, thereby ensuring the provision of ecosystem services, is ideally adapted to region specific landscape characteristics. In addition, [...] Read more.
Attractive landscapes are diverse and resilient landscapes that provide a multitude of essential ecosystem services. The development of landscape policy to protect and improve landscape attractiveness, thereby ensuring the provision of ecosystem services, is ideally adapted to region specific landscape characteristics. In addition, trends in landscape attractiveness may be linked to certain policies, or the absence of policies over time. A spatial and temporal evaluation of landscape attractiveness is thus desirable for landscape policy development. In this paper, landscape attractiveness was spatially evaluated for Flanders (Belgium) using landscape indicators derived from geospatial data as a case study. Large local differences in landscape quality in (i) rural versus urban areas and (ii) between the seven agricultural regions in Flanders were found. This observed spatial variability in landscape attractiveness demonstrated that a localized approach, considering the geophysical characteristics of each individual region, would be required in the development of landscape policy to improve landscape quality in Flanders. Some trends in landscape attractiveness were related to agriculture in Flanders, e.g., a slight decrease in total agricultural area, decrease in dominance of grassland, maize and cereals, a decrease in crop diversity, sharp increase in the adoption of agri-environmental agreements (AEA) and a decrease in bare soil conditions in winter. The observed trends and spatial variation in landscape attractiveness can be used as a tool to support policy analysis, assess the potential effects of future policy plans, identify policy gaps and evaluate past landscape policy. Full article
(This article belongs to the Special Issue Remote Sensing Analysis of Agricultural Landscapes)
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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
Cited by 5 | Viewed by 1545
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)
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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
Cited by 1 | Viewed by 868
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)
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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
Cited by 1 | Viewed by 1083
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)
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