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Special Issue "Landscape, Agriculture, and Society: Multiplatform Big Data Analysis for Monitoring and Sustainable Management of Agricultural Landscapes"

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: closed (31 July 2021).

Special Issue Editors

Prof. Dr. Paolo Tarolli
E-Mail Website1 Website2
Guest Editor
Department of Land, Environment, Agriculture and Forestry, University of Padova, viale dell’Università 16, 35020 Legnaro (PD), Italy
Interests: digital terrain analysis; earth surface processes analysis; natural hazards; geomorphometry; lidar; structure from motion; Anthropocene
Special Issues and Collections in MDPI journals
Prof. Dr. Antony G Brown
E-Mail Website
Guest Editor
Natural Sciences, Tromsø University Museum, UiT - The Arctic University, Kvaløyvegen 30, 9013 Tromsø, Norway
Interests: geography; geoarchaeology; quaternary science; Anthropocene

Special Issue Information

A big challenge in remote sensing today is to study landscape evolution using innovative techniques that allow analyzing and following an increasingly at-risk and ever-changing environment. However, such surveys under topographically complex conditions (vegetation, steep slopes, surface roughness) present several problems. These can be solved through accurate survey planning, merging of different techniques, and data post-processing that considers different topographical features. Typically large datasets include satellite remote sensing, airborne and terrestrial laser scanning, and also geophysical datasets. A further challenge is being able to follow land degradation phenomena at the process time, detect morphological changes with a high level of detail, and then translate these procedures to the landscape scale, finding effective solutions to these problems. A certainly interesting environment to develop, test, and implement new solutions can be agricultural landscapes, where the anthropic evolution has always tried, since ancient times, to control hydro-erosive processes that range from micro-erosion to mass movements and therefore improve cultivation. In this kind of environment, it is possible to assess different survey methodologies analyzing agricultural structures such as terraces, roads, and other human infrastructure that over time have certainly had an impact on the natural landscape. A challenge may be to identify the best techniques that allow reaching a high level of detail to capture the anthropogenic feature related to agricultural activities, understand the structure, and where possible detect and model macro and micro-erosive processes, finding effective solutions to mitigate land degradation phenomena in an agricultural context, where the anthropic factor dominates adding new variables.

Prof. Dr. Paolo Tarolli
Prof. Dr. Antony G Brown
Guest Editors

Manuscript Submission Information

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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 2400 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

  • agriculture
  • agricultural heritage
  • anthropogenic landscape
  • remote sensing
  • structure from motion
  • laser scanner
  • geoarchaeology
  • geomorphology

Published Papers (4 papers)

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Research

Article
Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping—A Case Study of the Loess Plateau, China
Remote Sens. 2021, 13(5), 1021; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13051021 - 08 Mar 2021
Cited by 1 | Viewed by 622
Abstract
Artificial terraces are of great importance for agricultural production and soil and water conservation. Automatic high-accuracy mapping of artificial terraces is the basis of monitoring and related studies. Previous research achieved artificial terrace mapping based on high-resolution digital elevation models (DEMs) or imagery. [...] Read more.
Artificial terraces are of great importance for agricultural production and soil and water conservation. Automatic high-accuracy mapping of artificial terraces is the basis of monitoring and related studies. Previous research achieved artificial terrace mapping based on high-resolution digital elevation models (DEMs) or imagery. As a result of the importance of the contextual information for terrace mapping, object-based image analysis (OBIA) combined with machine learning (ML) technologies are widely used. However, the selection of an appropriate classifier is of great importance for the terrace mapping task. In this study, the performance of an integrated framework using OBIA and ML for terrace mapping was tested. A catchment, Zhifanggou, in the Loess Plateau, China, was used as the study area. First, optimized image segmentation was conducted. Then, features from the DEMs and imagery were extracted, and the correlations between the features were analyzed and ranked for classification. Finally, three different commonly-used ML classifiers, namely, extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor (KNN), were used for terrace mapping. The comparison with the ground truth, as delineated by field survey, indicated that random forest performed best, with a 95.60% overall accuracy (followed by 94.16% and 92.33% for XGBoost and KNN, respectively). The influence of class imbalance and feature selection is discussed. This work provides a credible framework for mapping artificial terraces. Full article
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Article
Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands
Remote Sens. 2021, 13(4), 579; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040579 - 06 Feb 2021
Cited by 3 | Viewed by 792
Abstract
Accurate rice mapping and growth monitoring are of great significance for ensuring food security and agricultural sustainable development. Remote sensing (RS), as an efficient observation technology, is expected to be useful for rice mapping and growth monitoring. Due to the fragmented distribution of [...] Read more.
Accurate rice mapping and growth monitoring are of great significance for ensuring food security and agricultural sustainable development. Remote sensing (RS), as an efficient observation technology, is expected to be useful for rice mapping and growth monitoring. Due to the fragmented distribution of paddy fields and the undulating terrain in Southern China, it is very difficult in rice mapping. Moreover, there are many crops with the same growth period as rice, resulting in low accuracy of rice mapping. We proposed a red-edge decision tree (REDT) method based on the combination of time series GF-6 images and red-edge bands to solve this problem. The red-edge integral and red-edge vegetation index integral were computed by using two red-edge bands derived from GF-6 images to construct the REDT. Meanwhile, the conventional method based on time series normalized difference vegetation index (NDVI), normalized difference water index (NDWI), enhanced vegetation index (EVI) (NNE) was employed to compare the effectiveness of rice mapping. The results indicated that the overall accuracy and Kappa coefficient of REDT ranged from 91%–94% and 0.82–0.87, improving about 7% and 0.15 compared with the NNE method. This proved that the proposed technology was able to efficiently solve the problem of rice mapping on a large scale and regions with fragmented landscapes. Additionally, two red-edge bands of GF-6 images were applied to monitor rice growth. It concluded that the two red-edge bands played different roles in rice growth monitoring. The red-edge bands of GF-6 images were superior in rice mapping and growth monitoring. Further study needs to develop more vegetation indices (VIs) related to the red-edge to make the best use of red-edge characteristics in precision agriculture. Full article
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Article
Multiplatform-SfM and TLS Data Fusion for Monitoring Agricultural Terraces in Complex Topographic and Landcover Conditions
Remote Sens. 2020, 12(12), 1946; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12121946 - 17 Jun 2020
Cited by 17 | Viewed by 2645
Abstract
Agricultural terraced landscapes, which are important historical heritage sites (e.g., UNESCO or Globally Important Agricultural Heritage Systems (GIAHS) sites) are under threat from increased soil degradation due to climate change and land abandonment. Remote sensing can assist in the assessment and monitoring of [...] Read more.
Agricultural terraced landscapes, which are important historical heritage sites (e.g., UNESCO or Globally Important Agricultural Heritage Systems (GIAHS) sites) are under threat from increased soil degradation due to climate change and land abandonment. Remote sensing can assist in the assessment and monitoring of such cultural ecosystem services. However, due to the limitations imposed by rugged topography and the occurrence of vegetation, the application of a single high-resolution topography (HRT) technique is challenging in these particular agricultural environments. Therefore, data fusion of HRT techniques (terrestrial laser scanning (TLS) and aerial/terrestrial structure from motion (SfM)) was tested for the first time in this context (terraces), to the best of our knowledge, to overcome specific detection problems such as the complex topographic and landcover conditions of the terrace systems. SfM–TLS data fusion methodology was trialed in order to produce very high-resolution digital terrain models (DTMs) of two agricultural terrace areas, both characterized by the presence of vegetation that covers parts of the subvertical surfaces, complex morphology, and inaccessible areas. In the unreachable areas, it was necessary to find effective solutions to carry out HRT surveys; therefore, we tested the direct georeferencing (DG) method, exploiting onboard multifrequency GNSS receivers for unmanned aerial vehicles (UAVs) and postprocessing kinematic (PPK) data. The results showed that the fusion of data based on different methods and acquisition platforms is required to obtain accurate DTMs that reflect the real surface roughness of terrace systems without gaps in data. Moreover, in inaccessible or hazardous terrains, a combination of direct and indirect georeferencing was a useful solution to reduce the substantial inconvenience and cost of ground control point (GCP) placement. We show that in order to obtain a precise data fusion in these complex conditions, it is essential to utilize a complete and specific workflow. This workflow must incorporate all data merging issues and landcover condition problems, encompassing the survey planning step, the coregistration process, and the error analysis of the outputs. The high-resolution DTMs realized can provide a starting point for land degradation process assessment of these agriculture environments and supplies useful information to stakeholders for better management and protection of such important heritage landscapes. Full article
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Article
Quantitative Analysis of Anthropogenic Morphologies Based on Multi-Temporal High-Resolution Topography
Remote Sens. 2019, 11(12), 1493; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11121493 - 24 Jun 2019
Cited by 10 | Viewed by 1731
Abstract
Human activities have reshaped the geomorphology of landscapes and created vast anthropogenic geomorphic features, which have distinct characteristics compared with landforms produced by natural processes. High-resolution topography from LiDAR has opened avenues for the analysis of anthropogenic geomorphic signatures, providing new opportunities for [...] Read more.
Human activities have reshaped the geomorphology of landscapes and created vast anthropogenic geomorphic features, which have distinct characteristics compared with landforms produced by natural processes. High-resolution topography from LiDAR has opened avenues for the analysis of anthropogenic geomorphic signatures, providing new opportunities for a better understanding of Earth surface processes and landforms. However, quantitative identification and monitoring of such anthropogenic signature still represent a challenge for the Earth science community. The purpose of this contribution is to explore a method for monitoring geomorphic changes and identifying the driving forces of such changes. The study was carried out on the Eibar watershed in Spain. The proposed method is able to quantitatively detect anthropogenic geomorphic changes based on multi-temporal LiDAR topography, and it is based on a combination of two techniques: the DEM of Difference (DoD) and the Slope Local Length of Auto-correlation (SLLAC). First, we tested the capability of the SLLAC and derived parameters to distinguish different types of anthropogenic geomorphologies in 5 study case at a small scale. Second, we calculated the DoD to quantify the geomorphic changes between 2008 and 2016. Based on the proposed approach, we classified the whole basin into three categories of geomorphic changes (natural, urban or mosaic areas). The urban area had the most clustered and largest geomorphic changes, followed by the mosaic area and the natural area. This research might help to identify and monitoring anthropogenic geomorphic changes over large areas, to schedule sustainable environmental planning, and to mitigate the consequences of anthropogenic alteration. Full article
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