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Special Issue "Monitoring Agricultural Land-Use Change and Land-Use Intensity"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 31 December 2017

Special Issue Editors

Guest Editor
Dr. Fabian Löw

Co-Founder and Managing Partner, MapTailor Geospatial Consulting GbR, Nassestrasse 13, 53113 Bonn, Germany
Website | E-Mail
Interests: Agricultural monitoring; Crop mapping; Crop yield; Central Asia; Copernicus; EO for agriculture; Image classification; Land abandonment; Machine learning; Natural hazards; Time series analysis; Uncertainty
Guest Editor
Prof. Dr. Alexander Prishchepov

Associate Professor University of Copenhagen, Department of Natural Resources and Environmental Management (IGN), Øster Voldgade 10, 1350 København K, Denmark
Website | E-Mail
Fax: +45 35 33 13 86
Interests: remote sensing of land-use land-cover change (LULCC), understanding the drivers of LULCC, sustainable land use
Guest Editor
Dr. Florian Schierhorn

Structural Development of Farms and Rural Areas (Structural Change), Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Theodor-Lieser-Str. 2, 06120 Halle (Saale), Germany
Website | E-Mail
Phone: +49 345 2928-335
Fax: +49 345 2928-399
Interests: land change science; crop growth modeling
Guest Editor
Prof. Dr. Clement Atzberger

University of Natural Resources and Life Sciences (BOKU), A-1190 Vienna, Austria
Website | E-Mail
Phone: +43 (1) 47654 5101
Interests: time series analysis; vegetation monitoring and dynamics; land surface phenology; drought early warning systems; EO for agriculture, forestry and natural resource management; imaging spectroscopy; radiative transfer modeling; machine learning; neural nets; vegetation biophysical variables

Special Issue Information

Dear Colleagues,

Globally, agricultural production will need to increase to meet the food demand of the growing population, changing diets, and a rising importance of bioenergy. Agricultural expansion has already led to marked increases in agricultural production, albeit at substantial environmental costs. At the same time, agricultural land abandonment, i.e. the abolishment of cropping and livestock grazing activities, is widespread in many parts of the world.

The potential for further agricultural expansion is limited or would entail high environmental costs. Hence, one solution to mitigate the unavoidable increase of agricultural production is to intensify land-use on already cultivated lands, or the recultivation of abandoned lands. In order to understand the potential for intensification (or recultivation), information on spatial and temporal patterns of agricultural land-use change, land-use intensity, and/or abandonment at multiple geographic scales is required.

However, mapping the proxies of land-use abandonment or intensification, such as multiple annual cropping, intercropping, application of fertilizers, crop rotation techniques, or irrigation of cultivated fields as time-series, would require the development of novel methods. Recently, freeing satellite remote sensing data archives and the establishment of freely accessible satellite data programs, such as the European earth observation program Copernicus, makes it attractive to utilize the synergy of different sensors and data fusion techniques to address the societal needs to map land-use and its intensity.

This Special Issue on Monitoring Agricultural Land-Use Change and Land-Use Intensity will draw from ongoing advancements and novel developments in earth observation methodologies to assess agricultural land-use intensity and land-use change, with special emphasis on “big remotely-sensed data” analysis, data fusion techniques, time-series analysis, etc. We specifically encourage the submission of studies on the mapping of grazing patterns in grassland ecosystems and land abandonment. Papers that address the integration of remote sensing, and both the biophysical and human dimensions of agricultural land-use and land-use change, are also encouraged.

With these issues in mind, we invite you to submit methodological and applied studies, as well as review papers, with respect to, but not limited to, the following topics:

  • Assessment of spatial and temporal patterns of cropland expansion/contraction—abandonment and intensification/de-intensification;
  • Assessment of spatial and temporal patterns of grazing;
  • Data fusion techniques (SAR, LiDAR, optical remote sensing products) and analysis of big remotely-sensed data analysis for agricultural intensity and change studies;
  • Interdisciplinary studies on the utilization of remote sensing to map agricultural land-use change and linkage with socio-economic, biodiversity, and ecosystem processes.

Dr. Fabian Löw
Prof. Dr. Alexander Prishchepov
Dr. Florian Schierhorn
Prof. Dr. Clement Atzberger
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. Remote Sensing 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 1600 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

  • land-use change
  • land-use intensity
  • intensification and de-intensification
  • farm- and cropland abandonment
  • drivers of land-use change and abandonment

Published Papers (7 papers)

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Research

Open AccessArticle High Resolution Mapping of Cropping Cycles by Fusion of Landsat and MODIS Data
Remote Sens. 2017, 9(12), 1232; doi:10.3390/rs9121232
Received: 9 November 2017 / Revised: 23 November 2017 / Accepted: 27 November 2017 / Published: 29 November 2017
PDF Full-text (25639 KB) | HTML Full-text | XML Full-text
Abstract
Multiple cropping, a common practice of intensive agriculture that grows crops multiple times in the agricultural land in one growing season, is an effective way to fulfill the food demand given limited cropland areas. Deriving cropping cycles from satellite data provides the spatial
[...] Read more.
Multiple cropping, a common practice of intensive agriculture that grows crops multiple times in the agricultural land in one growing season, is an effective way to fulfill the food demand given limited cropland areas. Deriving cropping cycles from satellite data provides the spatial distribution of cropping intensities that allows for monitoring of the multiple cropping activities over large areas. Although efforts have been made to map cropping cycles at 500 m or coarser resolution, producing cropping cycle maps at high resolution remain challenging because data from single satellite sensor do not provide sufficient spatiotemporal observations. In this paper, we generate dense time series of satellite data at 30 m resolution by fusion of Landsat and MODIS data, and derive the cropping cycles from the fused time series data. The method achieves overall accuracies of 92.5% and 89.2%, respectively, for two typical regions of multiple cropping in China using samples identified based on satellite time series data, and an overall accuracy of 81.2% for four subregions using all samples identified based on multi-temporal high resolution images. The mapped crop cycles show to be reasonable geographically and agree with the national census data. The fusion approach provides a feasible way to map cropping cycles at 30 m resolution and enables improved depiction of the spatial distribution of multiple cropping. Full article
(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity)
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Open AccessArticle Crop Classification and LAI Estimation Using Original and Resolution-Reduced Images from Two Consumer-Grade Cameras
Remote Sens. 2017, 9(10), 1054; doi:10.3390/rs9101054
Received: 26 August 2017 / Revised: 25 September 2017 / Accepted: 10 October 2017 / Published: 17 October 2017
PDF Full-text (7246 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Consumer-grade cameras are being increasingly used for remote sensing applications in recent years. However, the performance of this type of cameras has not been systematically tested and well documented in the literature. The objective of this research was to evaluate the performance of
[...] Read more.
Consumer-grade cameras are being increasingly used for remote sensing applications in recent years. However, the performance of this type of cameras has not been systematically tested and well documented in the literature. The objective of this research was to evaluate the performance of original and resolution-reduced images taken from two consumer-grade cameras, a RGB camera and a modified near-infrared (NIR) camera, for crop identification and leaf area index (LAI) estimation. Airborne RGB and NIR images taken over a 6.5-square-km cropping area were mosaicked and aligned to create a four-band mosaic with a spatial resolution of 0.4 m. The spatial resolution of the mosaic was then reduced to 1, 2, 4, 10, 15 and 30 m for comparison. Six supervised classifiers were applied to the RGB images and the four-band images for crop identification, and 10 vegetation indices (VIs) derived from the images were related to ground-measured LAI. Accuracy assessment showed that maximum likelihood applied to the 0.4-m images achieved an overall accuracy of 83.3% for the RGB image and 90.4% for the four-band image. Regression analysis showed that the 10 VIs explained 58.7% to 83.1% of the variability in LAI. Moreover, spatial resolutions at 0.4, 1, 2 and 4 m achieved better classification results for both crop identification and LAI prediction than the coarser spatial resolutions at 10, 15 and 30 m. The results from this study indicate that imagery from consumer-grade cameras can be a useful data source for crop identification and canopy cover estimation. Full article
(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity)
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Open AccessFeature PaperArticle Multiscale Remote Sensing to Map the Spatial Distribution and Extent of Cropland in the Sudanian Savanna of West Africa
Remote Sens. 2017, 9(8), 839; doi:10.3390/rs9080839
Received: 11 June 2017 / Revised: 8 August 2017 / Accepted: 10 August 2017 / Published: 14 August 2017
PDF Full-text (19673 KB) | HTML Full-text | XML Full-text
Abstract
Food security is the topmost priority on the global agenda. Accurate agricultural statistics (i.e., cropped area) are essential for decision making and developing appropriate programs to achieve food security. However, derivation of these essential agricultural statistics, especially in developing countries, is fraught with
[...] Read more.
Food security is the topmost priority on the global agenda. Accurate agricultural statistics (i.e., cropped area) are essential for decision making and developing appropriate programs to achieve food security. However, derivation of these essential agricultural statistics, especially in developing countries, is fraught with many challenges including financial, logistical and human capacity limitations. This study investigated the use of fractional cover approaches in mapping cropland area in the heterogeneous landscape of West Africa. Discrete cropland areas identified from multi-temporal Landsat data were upscaled to MODIS resolution using random forest regression. Producer’s accuracy and user’s accuracy of the cropland class in the Landsat scale analysis averaged 95% and 94%, respectively, indicating good separability between crop and non-crop land. Validation of the fractional cropland cover map at MODIS resolution (MODIS_FCM) revealed an overall mean absolute error of 19%. Comparison of MODIS_FCM with the MODIS land cover product (e.g., MODIS_LCP) demonstrate the suitability of the proposed approach to cropped area estimation in smallholder dominant heterogeneous landscapes over existing global solutions. Comparison with official government statistics (i.e., cropped area) revealed variable levels of agreement and partly enormous disagreements, which clearly indicate the need to integrate remote sensing approaches and ground based surveys conducted by agricultural ministries in improving cropped area estimation. The recent availability of a wide range of open access remote sensing data is expected to expedite this integration and contribute missing information urgently required for regional assessments of food security in West Africa and beyond. Full article
(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity)
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Open AccessArticle Agricultural Expansion and Intensification in the Foothills of Mount Kenya: A Landscape Perspective
Remote Sens. 2017, 9(8), 784; doi:10.3390/rs9080784
Received: 3 July 2017 / Revised: 27 July 2017 / Accepted: 28 July 2017 / Published: 31 July 2017
PDF Full-text (16518 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This study spatially assesses, quantifies, and visualizes the agricultural expansion and land use intensification in the northwestern foothills of Mount Kenya over the last 30 years: processes triggered by population growth, and, more recently, by large-scale commercial investments. We made use of Google
[...] Read more.
This study spatially assesses, quantifies, and visualizes the agricultural expansion and land use intensification in the northwestern foothills of Mount Kenya over the last 30 years: processes triggered by population growth, and, more recently, by large-scale commercial investments. We made use of Google Earth Engine to access the USGS Landsat data archive and to generate cloud-free seasonal composites. These enabled us to accurately differentiate between rainfed and irrigated cropland, which was important for assessing agricultural intensification. We developed three land cover and land use classifications using the random forest classifier, and assessed land cover and land use change by creating cross-tabulation matrices for the intervals from 1987 to 2002, 2002 to 2016, and 1987 to 2016 and calculating the net change. We then applied a landscape mosaic approach to each classification to identify landscape types categorized by land use intensity. We discuss the impacts of landscape changes on natural habitats, biodiversity, and water. Kappa accuracies for the three classifications lay between 78.3% and 82.1%. Our study confirms that rainfed and irrigated cropland expanded at the expense of natural habitats, including protected areas. Agricultural expansion took place mainly in the 1980s and 1990s, whereas agricultural intensification largely happened after 2000. Since then, not only large-scale producers, but also many smallholders have begun to practice irrigated farming. The spatial pattern of agricultural expansion and intensification in the study area is defined by water availability. Agricultural intensification and the expansion of horticulture agribusinesses increase pressure on water. Furthermore, the observed changes have heightened pressure on pasture and idle land due to the decrease in natural and agropastoral landscapes. Conflicts between pastoralists, smallholder farmers, large-scale ranches, and wildlife might further increase, particularly during the dry seasons and in years of extreme drought. Full article
(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity)
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Open AccessArticle Identifying Irrigated Areas in the Snake River Plain, Idaho: Evaluating Performance across Composting Algorithms, Spectral Indices, and Sensors
Remote Sens. 2017, 9(6), 546; doi:10.3390/rs9060546
Received: 3 March 2017 / Revised: 22 May 2017 / Accepted: 25 May 2017 / Published: 1 June 2017
Cited by 3 | PDF Full-text (8349 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
There are pressing concerns about the interplay between agricultural productivity, water demand, and water availability in semi-arid to arid regions of the world. Currently, irrigated agriculture is the dominant water user in these regions and is estimated to consume approximately 80% of the
[...] Read more.
There are pressing concerns about the interplay between agricultural productivity, water demand, and water availability in semi-arid to arid regions of the world. Currently, irrigated agriculture is the dominant water user in these regions and is estimated to consume approximately 80% of the world’s diverted freshwater resources. We develop an improved irrigated land-use mapping algorithm that uses the seasonal maximum value of a spectral index to distinguish between irrigated and non-irrigated parcels in Idaho’s Snake River Plain. We compare this approach to two alternative algorithms that differentiate between irrigated and non-irrigated parcels using spectral index values at a single date or the area beneath spectral index trajectories for the duration of the agricultural growing season. Using six different pixel and county-scale error metrics, we evaluate the performance of these three algorithms across all possible combinations of two growing seasons (2002 and 2007), two datasets (MODIS and Landsat 5), and three spectral indices, the Normalized Difference Vegetation Index, Enhanced Vegetation Index and Normalized Difference Moisture Index (NDVI, EVI, and NDMI). We demonstrate that, on average, the seasonal-maximum algorithm yields an improvement in classification accuracy over the accepted single-date approach, and that the average improvement under this approach is a 60% reduction in county scale root mean square error (RMSE), and modest improvements of overall accuracy in the pixel scale validation. The greater accuracy of the seasonal-maximum algorithm is primarily due to its ability to correctly classify non-irrigated lands in riparian and developed areas of the study region. Full article
(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity)
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Open AccessArticle Monitoring Agricultural Expansion in Burkina Faso over 14 Years with 30 m Resolution Time Series: The Role of Population Growth and Implications for the Environment
Remote Sens. 2017, 9(2), 132; doi:10.3390/rs9020132
Received: 21 December 2016 / Revised: 25 January 2017 / Accepted: 27 January 2017 / Published: 5 February 2017
Cited by 5 | PDF Full-text (8157 KB) | HTML Full-text | XML Full-text
Abstract
Burkina Faso ranges amongst the fastest growing countries in the world with an annual population growth rate of more than three percent. This trend has consequences for food security since agricultural productivity is still on a comparatively low level in Burkina Faso. In
[...] Read more.
Burkina Faso ranges amongst the fastest growing countries in the world with an annual population growth rate of more than three percent. This trend has consequences for food security since agricultural productivity is still on a comparatively low level in Burkina Faso. In order to compensate for the low productivity, the agricultural areas are expanding quickly. The mapping and monitoring of this expansion is difficult, even on the basis of remote sensing imagery, since the extensive farming practices and frequent cloud coverage in the area make the delineation of cultivated land from other land cover and land use types a challenging task. However, as the rapidly increasing population could have considerable effects on the natural resources and on the regional development of the country, methods for improved mapping of LULCC (land use and land cover change) are needed. For this study, we applied the newly developed ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) framework to generate high temporal (8-day) and high spatial (30 m) resolution NDVI time series for all of Burkina Faso for the years 2001, 2007, and 2014. For this purpose, more than 500 Landsat scenes and 3000 MODIS scenes were processed with this automated framework. The generated ESTARFM NDVI time series enabled extraction of per-pixel phenological features that all together served as input for the delineation of agricultural areas via random forest classification at 30 m spatial resolution for entire Burkina Faso and the three years. For training and validation, a randomly sampled reference dataset was generated from Google Earth images and based on expert knowledge. The overall accuracies of 92% (2001), 91% (2007), and 91% (2014) indicate the well-functioning of the applied methodology. The results show an expansion of agricultural area of 91% between 2001 and 2014 to a total of 116,900 km². While rainfed agricultural areas account for the major part of this trend, irrigated areas and plantations also increased considerably, primarily promoted by specific development projects. This expansion goes in line with the rapid population growth in most provinces of Burkina Faso where land was still available for an expansion of agricultural area. The analysis of agricultural encroachment into protected areas and their surroundings highlights the increased human pressure on these areas and the challenges of environmental protection for the future. Full article
(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity)
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Open AccessArticle Cropping Intensity in the Aral Sea Basin and Its Dependency from the Runoff Formation 2000–2012
Remote Sens. 2016, 8(8), 630; doi:10.3390/rs8080630
Received: 23 June 2016 / Revised: 21 July 2016 / Accepted: 26 July 2016 / Published: 29 July 2016
Cited by 2 | PDF Full-text (12621 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This study is aimed at a better understanding of how upstream runoff formation affected the cropping intensity (CI: number of harvests) in the Aral Sea Basin (ASB) between 2000 and 2012. MODIS 250 m NDVI time series and knowledge-based pixel masking that included
[...] Read more.
This study is aimed at a better understanding of how upstream runoff formation affected the cropping intensity (CI: number of harvests) in the Aral Sea Basin (ASB) between 2000 and 2012. MODIS 250 m NDVI time series and knowledge-based pixel masking that included settlement layers and topography features enabled to map the irrigated cropland extent (iCE). Random forest models supported the classification of cropland vegetation phenology (CVP: winter/summer crops, double cropping, etc.). CI and the percentage of fallow cropland (PF) were derived from CVP. Spearman’s rho was selected for assessing the statistical relation of CI and PF to runoff formation in the Amu Darya and Syr Darya catchments per hydrological year. Validation in 12 reference sites using multi-annual Landsat-7 ETM+ images revealed an average overall accuracy of 0.85 for the iCE maps. MODIS maps overestimated that based on Landsat by an average factor of ~1.15 (MODIS iCE/Landsat iCE). Exceptional overestimations occurred in case of inaccurate settlement layers. The CVP and CI maps achieved overall accuracies of 0.91 and 0.96, respectively. The Amu Darya catchment disclosed significant positive (negative) relations between upstream runoff with CI (PF) and a high pressure on the river water resources in 2000–2012. Along the Syr Darya, reduced dependencies could be observed, which is potentially linked to the high number of water constructions in that catchment. Intensified double cropping after drought years occurred in Uzbekistan. However, a 10 km × 10 km grid of Spearman’s rho (CI and PF vs. upstream runoff) emphasized locations at different CI levels that are directly affected by runoff fluctuations in both river systems. The resulting maps may thus be supportive on the way to achieve long-term sustainability of crop production and to simultaneously protect the severely threatened environment in the ASB. The gained knowledge can be further used for investigating climatic impacts of irrigation in the region. Full article
(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Annual 30m cropland mapping between 2001 and 2016 using Reference Landsat time series: A case study in Xinjiang and Kazakhstan
Authors: Pengyu Hao*(1), Fabian Löw* (2)
Affiliations: (1) Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
(2) MapTailor Geospatial Consulting GbR, Bonn, Germany
Abstract: Mapping the spatial and temporal dynamics of cropland is an important prerequisite for regular crop condition monitoring and assessing the effects of agricultural activities on natural resources. Satellite earth observation is a proven means to accurately identify croplands regularly, but currently, existing maps of the annual cropland extent either have a low spatial resolution (e.g. 250 m – 1000 m), or existing high resolution maps (such as 30m) are not provided frequency – for example at a regular, annual base - to support regular monitoring. Obstacles to regularly producing cropland maps can be a lack of in situ reference data and irregular timing of the image time series, like for instance Landsat or Sentinel-2. Against this backdrop, this paper proposed a method to identify croplands using generalized training signatures, i.e. temporal features that remain stable over time, and to create binary cropland vs. non-cropland maps annually between 2001 and 2016 at 30m spatial resolution. Landsat-5, Landsat-7 and Landsat-8 images were firstly monthly composited. Next, reference Landsat time series of cropland were generated based on the monthly compositions. Finally, a novel method was proposed to identify cropland using reference time series annually, and eliminate the effect of irregular time series. This study presents a rigorous assessment of annually created cropland masks at 30m resolution in six distinct agricultural landscapes. The research questions were: (1) to evaluate the potential of monthly composited Landsat data for yearly 30m cropland identification; (2) to acquire optional cropland identification features from the monthly composition time series; and (3) to assess the accuracy of using reference image time series created in one year for cropland mapping in the other years. The performances of the method were tested in seven study regions, Bole, Manas, Luntai, Ili-Balkash of Xinjiang, China; and Almaty, Karakalpakstan, Kyzyl-Orda of Central-Asia, the applicability of the method was then evaluated with annually available reference samples.

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