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Application of Remote Sensing in Agroforestry II

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 14097

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Dr. Emanuel Peres

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Department of Engineering, School of Sciences and Technology, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: remote sensing; precision agriculture; in-field data processing; remote monitoring; UAV; UAS; precision forestry; sensors and data processing; human–computer interfaces; augmented reality; virtual reality; embedded systems
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Dr. Joaquim João Sousa

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Engineering Department, School of Science and Technology, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: UAV; image processing algorithms (RGB, NIR, multi- and hyperspectral, thermal and LiDAR sensors); InSAR; precision agriculture; precision forestry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Technological development, integration, and adoption in both agriculture and forestry management practices continue to grow. The need to increase yield and quality and introduce sustainable practices while simultaneously reducing disease incidence and minimizing chemical inputs requires careful and detailed management. Knowledge with the highest detail level possible about context, culture, and environmental parameters that can influence both agriculture and forests’ high variabilities is needed to improve management practices.

Remote sensing enables the acquisition of diverse data with variable levels of detail, both in agriculture and in forestry. Indeed, the use of satellites, manned aircrafts, and unmanned aerial vehicles, equipped with different types of sensors (e.g., RGB, NIR, LiDAR, multi- and hyperspectral and thermal) has been gaining special attention in their different applications in agriculture and forests.

Moreover, the need for systems that are able to deal with the massive amounts of data being generated by remote sensing is also emerging. They must be capable of aggregating and extracting useful and intelligible information to stakeholders, preferably in a (semi)automatic way, through the application of deep learning.

This forthcoming 2nd Volume of the Special Issue on “Application of Remote Sensing in Agroforestry” (https://www.mdpi.com/journal/remotesensing/special_issues/remote_sensing_agroforestry) aims to collect new developments, methodologies, algorithms, best practices, and applications in remote sensing. We welcome submissions that provide the community with the most recent advancements on all aspects of remote sensing.

Dr. Emanuel Peres
Dr. Joaquim João Sousa
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 submissions that pass pre-check are 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 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 2700 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

deep learning in remote sensing
decision-support systems
forecasting models (e.g., yield, diseases)
management systems
box-to-box approaches in precision agriculture and precision forestry
automatic yield/diseases mapping
data visualization

Related Special Issue

Application of Remote Sensing in Agroforestry (Third Edition) in Remote Sensing

Published Papers (7 papers)

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Research

23 pages, 7488 KiB

Open AccessArticle

Spatiotemporal Variation and Stability of Rice Planting Using Landsat–MODIS Fusion Images from 1990 to 2020

by Luguang Jiang, Ye Liu and Si Wu

Remote Sens. 2023, 15(19), 4814; https://doi.org/10.3390/rs15194814 - 03 Oct 2023

Viewed by 822

Abstract

Dongting Lake Plain is a historic foundation for China’s commodity grain production. We used Landsat images to interpret the rice planting pattern from 1990 to 2020 based on the vegetation index curve and crop time window differences. The research aims included the spatiotemporal change in the rice planting area and the multiple cropping index, the transformation properties between single-crop and double-crop, and influence factors of rice cultivation. The findings indicated that the rice planting area has increased by 23.64% over the past 30 years. However, the multiple cropping index decreased by 17.39%. The area of single-crop rice increased by 2.6 times, while the area of double-crop rice decreased by 23.19%, which indicated that the planting intensity of rice has decreased. The area where rice has been steadily planted for 30 years is approximately 5600 km², accounting for 87% of all rice planting land in this study area. The transformation from double-crop rice to single-crop rice was the most obvious characteristic of internal changes. The marginal benefits of current agricultural policies have decreased. This research may provide a theoretical basis for the refined management of rice and improve agricultural policies. More clouds in the remote sensing image limited the time resolution. Future research may further explore the comprehensive influencing factors. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Agroforestry II)

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29 pages, 25998 KiB

Open AccessArticle

Use of Remotely Sensed Data for the Evaluation of Inter-Row Cover Intensity in Vineyards

by Francesco Palazzi, Marcella Biddoccu, Enrico Corrado Borgogno Mondino and Eugenio Cavallo

Remote Sens. 2023, 15(1), 41; https://doi.org/10.3390/rs15010041 - 22 Dec 2022

Cited by 3 | Viewed by 1752

Abstract

Information on vegetation cover and soil management is used in hydrological and soil erosion modeling, but in most cases, reference values are used solely based on land use classification without considering the actual spatial and temporal variation adopted at the field scale. This work focused on the adoption of satellite optical data from the Copernicus Sentinel-2 (S2) mission to evaluate both spatial and temporal variations of vineyard ground cover. First, on a wider scale, fields were mapped by photointerpretation, and a cluster analysis was carried out. Results suggest that vineyards can be classified according to different inter-row soil management, with the best results obtained using NDVI and NDWI. A pilot area in the municipality of Carpeneto, in the wine-growing area of Alto Monferrato, was also analyzed due to the availability of reference data on inter-row vegetation cover from experimental plots. Those are set on sloping areas and present different inter-row soil managements (conventional tillage—CT, and permanent grass cover—GC). Time series of different vegetation indices (VIs) have been obtained, and both S2 native bands and the derived VIs were evaluated to assess their capability of describing the vineyard’s inter-row coverage growth trends at plot level for the agrarian year 2017–2018. Results suggest that a seasonality effect may be involved in the choice of the most suitable band or index that better describes soil coverage development at a given moment of the year. Further studies on open-source remotely sensed (RS) data could provide specific inputs for applications in erosion risk management and crop modeling. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Agroforestry II)

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25 pages, 2879 KiB

Open AccessArticle

Using Apparent Electrical Conductivity to Delineate Field Variation in an Agroforestry System in the Ozark Highlands

by Shane Ylagan, Kristofor R. Brye, Amanda J. Ashworth, Phillip R. Owens, Harrison Smith and Aurelie M. Poncet

Remote Sens. 2022, 14(22), 5777; https://doi.org/10.3390/rs14225777 - 16 Nov 2022

Cited by 3 | Viewed by 1421

Abstract

Greater adoption and better management of spatially complex, conservation systems such as agroforestry (AF) are dependent on determining methods suitable for delineating in-field variability. However, no work has been conducted using repeated electromagnetic induction (EMI) or apparent electrical conductivity (EC_a) surveys in AF systems within the Ozark Highlands of northwest Arkansas. As a result, objectives were to (i) evaluate spatiotemporal EC_a variability; (ii) identify EC_a-derived soil management zones (SMZs); (iii) establish correlations among EC_a survey data and in situ, soil-sensor volumetric water content, sentential site soil-sample EC, and gravimetric water content and pH; and (iv) determine the optimum frequency at which EC_a surveys could be conducted to capture temporal changes in field variability. Monthly EC_a surveys were conducted between August 2020 and July 2021 at a 4.25 ha AF site in Fayetteville, Arkansas. The overall mean perpendicular geometry (PRP) and horizontal coplanar geometry (HCP) EC_a ranged from 1.8 to 18.0 and 3.1 to 25.8 mS m⁻¹, respectively, and the overall mean HCP EC_a was 67% greater than the mean PRP EC_a. The largest measured EC_a values occurred within the local drainage way or areas of potential groundwater movement, and the smallest measured EC_a values occurred within areas with decreased effective soil depth and increased coarse fragments. The PRP and HCP mean EC_a, standard deviation (SD), and coefficient of variation (CV) were unaffected (p > 0.05) by either the weather or growing/non-growing season. K-means clustering delineated three precision SMZs that were reflective of areas with similar EC_a and EC_a variability. Results from this study provided valuable information regarding the application of EC_a surveys to quantify small-scale changes in soil properties and delineate SMZs in highly variable AF systems. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Agroforestry II)

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17 pages, 3506 KiB

Open AccessArticle

Transfer-Learning-Based Approach for Yield Prediction of Winter Wheat from Planet Data and SAFY Model

by Yu Zhao, Shaoyu Han, Yang Meng, Haikuan Feng, Zhenhai Li, Jingli Chen, Xiaoyu Song, Yan Zhu and Guijun Yang

Remote Sens. 2022, 14(21), 5474; https://doi.org/10.3390/rs14215474 - 31 Oct 2022

Cited by 10 | Viewed by 1888

Abstract

Crop production is one of the major interactions between humans and the natural environment, in the process, carbon is translocated cyclically inside the ecosystem. Data assimilation algorithm has advantages in mechanism and robustness in yield estimation, however, the computational efficiency is still a major obstacle for widespread application. To address the issue, a novel hybrid method based on the combination of the Crop Biomass Algorithm of Wheat (CBA-Wheat) to the Simple Algorithm For Yield (SAFY) model and the transfer learning method was proposed in this paper, which enables winter wheat yield estimation with acceptable accuracy and calculation efficiency. The transfer learning techniques learn the knowledge from the SAFY model and then use the knowledge to predict wheat yield. The main results showed that: (1) The comparison using CBA-Wheat between measured AGB and predicted AGB all reveal a good correlation with R² of 0.83 and RMSE of 1.91 t ha⁻¹, respectively; (2) The performance of yield prediction was as follows: transfer learning method (R² of 0.64, RMSE of 1.05 t ha⁻¹) and data assimilation (R² of 0.64, RMSE of 1.01 t ha⁻¹). At the farm scale, the two yield estimation models are still similar in performance with RMSE of 1.33 t ha⁻¹ for data assimilation and 1.13 t ha⁻¹ for transfer learning; (3) The time consumption of transfer learning with complete simulation data set is significantly lower than that of the other two yield estimation tests. The number of pixels to be simulated was about 16,000, and the computational efficiency of the data assimilation algorithm and transfer learning without complete simulation datasets. The transfer learning model shows great potential in improving the efficiency of production estimation. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Agroforestry II)

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14 pages, 2248 KiB

Open AccessArticle

Potential of ALOS2 Polarimetric Imagery to Support Management of Poplar Plantations in Northern Italy

by Gaia Vaglio Laurin, Walter Mattioli, Simone Innocenti, Emanuela Lombardo, Riccardo Valentini and Nicola Puletti

Remote Sens. 2022, 14(20), 5202; https://doi.org/10.3390/rs14205202 - 18 Oct 2022

Cited by 1 | Viewed by 1967

Abstract

Poplar is one of the most widespread fast-growing forest species. In Northern Italy, plantations are characterized by large interannual fluctuations, requiring frequent monitoring to inform on wood supply and to manage the stands. The use of radar satellite data is proving useful for forest monitoring, being weather independent and sensitive to the changes in forest canopy structure, but it has been scarcely tested in the case of poplar. Here, L-band ALOS2 (Advanced Land Observing Satellite-2) dual-pol data were tested to detect clear-cut plantations in consecutive years. ALOS2 quad-pol data were used to discriminate among different age classes, a much complex task than detecting poplar plantations extent. Results from different machine learning algorithms indicate that with dual-pol data, poplar forest can be discriminated from clear-cut areas with 80% overall accuracy, similar to what is usually obtained with optical data. With quad-pol data, four age classes were classified with moderate overall accuracy (73%) based on polarimetric decompositions, three 3 age classes with higher accuracy (87%) based on HV band. Sources of error are represented by poplar areas of intermediate age when stems, branches and leaves were not developed enough to detect by scattering mechanisms. This study demonstrates the feasibility of monitoring poplar plantations with satellite radar, which represents a growing source of information thanks to already-planned future satellite missions. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Agroforestry II)

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12 pages, 2403 KiB

Open AccessArticle

Estimating the Legacy Effect of Post-Cutting Shelterbelt on Crop Yield Using Google Earth and Sentinel-2 Data

by Yage Liu, Huidong Li, Minchao Wu, Anzhi Wang, Jiabing Wu and Dexin Guan

Remote Sens. 2022, 14(19), 5005; https://doi.org/10.3390/rs14195005 - 08 Oct 2022

Cited by 4 | Viewed by 1493

Abstract

Shelterbelts (or windbreaks) can effectively improve the microclimate and soil conditions of adjacent farmland and thus increase crop yield. However, the individual contribution of these two factors to yield changes is still unclear since the short-term effect from the microclimate and the accumulated effect from the soil jointly affect crop yield. The latter (soil effect) is supposed to remain after shelterbelt-cutting, thus inducing a post-cutting legacy effect on yield, which can be used to decompose the shelterbelt-induced yield increase. Here, we develop an innovative framework to investigate the legacy effect of post-cutting shelterbelt on corn yield by combining Google Earth and Sentinel-2 data in Northeastern China. Using this framework, for the first time, we decompose the shelterbelt-induced yield increase effect into microclimate and soil effects by comparing the yield profiles before and after shelterbelt-cutting. We find that on average, the intensity of the legacy effect, namely the crop yield increment of post-cutting shelterbelts, is 0.98 ± 0.03%. The legacy effect varies depending on the shelterbelt–farmland relative location and shelterbelt density. The leeward side of the shelterbelt-adjacent farmland has a more remarkable legacy effect compared to the windward side. Shelterbelts with medium–high density have the largest legacy effect (1.94 ± 0.05%). Overall, the legacy effect accounts for 47% of the yield increment of the shelterbelt before cutting, implying that the soil effect is almost equally important for increasing crop yield compared to the microclimate effect. Our findings deepen the understanding of the mechanism of shelterbelt-induced yield increase effects and can help to guide shelterbelt management. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Agroforestry II)

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26 pages, 7481 KiB

Open AccessArticle

An Unsupervised Canopy-to-Root Pathing (UCRP) Tree Segmentation Algorithm for Automatic Forest Mapping

by Joshua Carpenter, Jinha Jung, Sungchan Oh, Brady Hardiman and Songlin Fei

Remote Sens. 2022, 14(17), 4274; https://doi.org/10.3390/rs14174274 - 30 Aug 2022

Cited by 2 | Viewed by 3914

Abstract

Terrestrial laser scanners, unmanned aerial LiDAR, and unmanned aerial photogrammetry are increasingly becoming the go-to methods for forest analysis and mapping. The three-dimensionality of the point clouds generated by these technologies is ideal for capturing the structural features of trees such as trunk diameter, canopy volume, and biomass. A prerequisite for extracting these features from point clouds is tree segmentation. This paper introduces an unsupervised method for segmenting individual trees from point clouds. Our novel, canopy-to-root, least-cost routing method segments trees in a single routine, accomplishing stem location and tree segmentation simultaneously without needing prior knowledge of tree stem locations. Testing on benchmark terrestrial-laser-scanned datasets shows that we achieve state-of-the-art performances in individual tree segmentation and stem-mapping accuracy on boreal and temperate hardwood forests regardless of forest complexity. To support mapping at scale, we test on unmanned aerial photogrammetric and LiDAR point clouds and achieve similar results. The proposed algorithm’s independence from a specific data modality, along with its robust performance in simple and complex forest environments and accurate segmentation results, make it a promising step towards achieving reliable stem-mapping capabilities and, ultimately, towards building automatic forest inventory procedures. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Agroforestry II)

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