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

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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) | Viewed by 21326

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Prof. Dr. Paolo Tarolli

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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
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Prof. Dr. Antony G Brown

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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

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Keywords

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

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Published Papers (4 papers)

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Research

18 pages, 8636 KiB

Open AccessArticle

Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping—A Case Study of the Loess Plateau, China

by Hu Ding, Jiaming Na, Shangjing Jiang, Jie Zhu, Kai Liu, Yingchun Fu and Fayuan Li

Remote Sens. 2021, 13(5), 1021; https://doi.org/10.3390/rs13051021 - 8 Mar 2021

Cited by 20 | Viewed by 3367

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. 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

(This article belongs to the Special Issue Landscape, Agriculture, and Society: Multiplatform Big Data Analysis for Monitoring and Sustainable Management of Agricultural Landscapes)

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25 pages, 9359 KiB

Open AccessArticle

Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands

by Xueqin Jiang, Shenghui Fang, Xia Huang, Yanghua Liu and Linlin Guo

Remote Sens. 2021, 13(4), 579; https://doi.org/10.3390/rs13040579 - 6 Feb 2021

Cited by 38 | Viewed by 4455

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 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

(This article belongs to the Special Issue Landscape, Agriculture, and Society: Multiplatform Big Data Analysis for Monitoring and Sustainable Management of Agricultural Landscapes)

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29 pages, 5221 KiB

Open AccessFeature PaperArticle

Multiplatform-SfM and TLS Data Fusion for Monitoring Agricultural Terraces in Complex Topographic and Landcover Conditions

by Sara Cucchiaro, Daniel J. Fallu, He Zhang, Kevin Walsh, Kristof Van Oost, Antony G. Brown and Paolo Tarolli

Remote Sens. 2020, 12(12), 1946; https://doi.org/10.3390/rs12121946 - 17 Jun 2020

Cited by 52 | Viewed by 7959

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 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

(This article belongs to the Special Issue Landscape, Agriculture, and Society: Multiplatform Big Data Analysis for Monitoring and Sustainable Management of Agricultural Landscapes)

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20 pages, 13609 KiB

Open AccessArticle

Quantitative Analysis of Anthropogenic Morphologies Based on Multi-Temporal High-Resolution Topography

by Jie Xiang, Shi Li, Keyan Xiao, Jianping Chen, Giulia Sofia and Paolo Tarolli

Remote Sens. 2019, 11(12), 1493; https://doi.org/10.3390/rs11121493 - 24 Jun 2019

Cited by 15 | Viewed by 4480

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 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

(This article belongs to the Special Issue Landscape, Agriculture, and Society: Multiplatform Big Data Analysis for Monitoring and Sustainable Management of Agricultural Landscapes)

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