Geospatial Digital Innovations for Smart Agriculture and Forestry

Topic Information

Dear Colleagues,

We are inviting submissions related to innovative geospatial methods and developments and their applications in smart agriculture and forestry, with a specific focus on the following key areas of interest:

• Remote Sensing: This area involves the use of non-invasive techniques such as satellite imagery, aerial photography, UAVs (unmanned aerial vehicles), and LiDAR (light detection and ranging) to capture geospatial data for various applications in agriculture and forestry.
• Developing Digital Earth Systems for Smart Agriculture and Forestry: This key area involves exploring essential algorithms, data structures, and software tools for developing Digital Earth system components for harnessing big geospatial data in smart agriculture and forestry.
• Computational Techniques in Smart Agriculture and Forestry: This involves developing and utilizing novel computational methods in Digital Earth’s optimization and simulation models to improve smart agriculture and forestry practices.
• Artificial Intelligence (AI): The focus lies in exploring machine learning, deep learning, and generative AI techniques within smart agriculture and forestry domains. Instances of application include the monitoring and detecting of plants, livestock, and farm production, as well as the synthesis of scarce and difficult-to-acquire geospatial or farm-related training data using generative AI. Moreover, this field encompasses the prediction of natural disturbances affecting both forest ecosystems and farmlands, as well as the detection of disaster damage, among other topics.
• Visualization in Digital Earth: This involves investigating and implementing advanced techniques that enable a more expressive and engaging representation of big, complex geospatial datasets. This area of interest includes exploring visualization methods such as virtual reality (VR), augmented reality (AR), physicalization, and interactive 3D modelling of the earth to present geospatial data using well-designed interaction methods. The goal is to assist various smart agriculture and forestry stakeholders in understanding the data more fully in order to make informed decisions.

Dr. Faramarz F. Samavati
Dr. Ali Mahdavi-Amiri
Topic Editors

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agriculture agriculture	3.6	3.6	2011	17.7 Days	CHF 2600	Submit
Forests forests	2.9	4.5	2010	16.9 Days	CHF 2600	Submit
ISPRS International Journal of Geo-Information ijgi	3.4	6.2	2012	35.5 Days	CHF 1700	Submit
Remote Sensing remotesensing	5.0	7.9	2009	23 Days	CHF 2700	Submit

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Published Papers (6 papers)

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All Agriculture Forests IJGI Remote Sensing

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20 pages, 6363 KiB  

Open AccessArticle

A New Dissimilarity Metric for Anomaly Detection in Management Zones Delineation Constructed from Time-Varying Satellite Images

by Roghayeh Heidari and Faramarz F. Samavati

Agriculture 2024, 14(5), 688; https://doi.org/10.3390/agriculture14050688 (registering DOI) - 27 Apr 2024

Viewed by 243

Abstract

A field’s historical performance data are used for management zone delineation in precision agriculture, but including abnormal data leads to inappropriate zones. This paper introduces a framework incorporating historical performance data and a new Zoning Dissimilarity Metric ($ZDM$) to [...] Read more.

A field’s historical performance data are used for management zone delineation in precision agriculture, but including abnormal data leads to inappropriate zones. This paper introduces a framework incorporating historical performance data and a new Zoning Dissimilarity Metric ($ZDM$) to detect abnormal zoning data automatically. The methodology identifies abnormal zoning data among the field’s performance indicators extracted from satellite images to enhance the accuracy of the delineated zones. We experimented with our framework using Sentinel-2 images on 39 fields across Canada. Our experimental results, which involve both real and synthetic data, clearly demonstrate the importance of $ZDM$ in effectively excluding abnormal data during the zone delineation process. Full article

(This article belongs to the Topic Geospatial Digital Innovations for Smart Agriculture and Forestry)

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28 pages, 15553 KiB  

Open AccessArticle

From Space to Field: Combining Satellite, UAV and Agronomic Data in an Open-Source Methodology for the Validation of NDVI Maps in Precision Viticulture

by David Govi, Salvatore Eugenio Pappalardo, Massimo De Marchi and Franco Meggio

Remote Sens. 2024, 16(5), 735; https://doi.org/10.3390/rs16050735 - 20 Feb 2024

Cited by 2 | Viewed by 579

Abstract

Recent GIS technologies are shaping the direction of Precision Agriculture and Viticulture. Sentinel-2 satellites and UAVs are key resources for multi-spectral analyses of vegetation. Despite being extensively adopted in numerous applications and scenarios, the pros and cons of both platforms are still debated. [...] Read more.

Recent GIS technologies are shaping the direction of Precision Agriculture and Viticulture. Sentinel-2 satellites and UAVs are key resources for multi-spectral analyses of vegetation. Despite being extensively adopted in numerous applications and scenarios, the pros and cons of both platforms are still debated. Researchers have currently investigated different aspects of these sources, mainly comparing different vegetation indexes and exploring potential relationships with agronomic variables. However, due to the costs and limitations of such an approach, a standardized methodology for agronomic purposes is still missing. This study aims to fill such a methodology gap by overcoming the potential flaws or shortages of previous works. To achieve this, an image acquisition campaign covering 6 months and over 17 hectares was carried out, followed by an NDVI comparison between Sentinel-2 and UAV to eventually explore relationships with agronomic variables. Comparative analyses were performed by using both classical (Ordinary Least Squares regression and Pearson Correlation) and spatial (Moran’s Index) statistical approaches: here, 90% of cases show r and MI scores above 0.6 for plain images, with these scores expectedly lowering to 72% and 52% when considering segmented images. Moreover, NDVI thematic maps were classified into clusters and validated by the Chi-squared test. Finally, the relationship and distribution of agronomic variables within NDVI and clustered maps were consistently validated through the ANOVA test. The proposed open-source pipeline allows to strengthen existing UAV and satellite applications in Precision Agriculture by integrating more agronomic variables. Full article

(This article belongs to the Topic Geospatial Digital Innovations for Smart Agriculture and Forestry)

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18 pages, 22653 KiB  

Open AccessArticle

Parallel Channel Identification and Elimination Method Based on the Spatial Position Relationship of Different Channels

by Mingwei Zhao, Xiaoxiao Ju, Ni Wang, Chun Wang, Weibo Zeng and Yan Xu

ISPRS Int. J. Geo-Inf. 2024, 13(1), 13; https://doi.org/10.3390/ijgi13010013 - 30 Dec 2023

Viewed by 1286

Abstract

Extracting a channel network based on the Digital Elevation Model (DEM) is one of the key research topics in digital terrain analysis. However, when the channel area is wide and flat, it is easy to form parallel channels, which seriously affect the accuracy [...] Read more.

Extracting a channel network based on the Digital Elevation Model (DEM) is one of the key research topics in digital terrain analysis. However, when the channel area is wide and flat, it is easy to form parallel channels, which seriously affect the accuracy of channel network extraction. To solve this problem, this study proposes a method to identify and eliminate parallel channels extracted by classical methods. First, the channel level in the study area is marked based on the flow accumulation data, and the parallel channels are then identified using the positional relationship between the different channel levels. Finally, the modification point of the identified parallel channels is determined to eliminate the parallel channels, with the help of the change relationship between the parallel channel and its upper-level channel. In this study, two watersheds in southeast China are selected as examples for method verification and analysis. Experimental results show that the parallel channel identification method proposed in this paper can accurately identify all parallel channels and eliminate the identified parallel channels one by one. The location relationship of the modified channels is consistent with the actual situation, indicating that the proposed method has good application potential in DEM-based channel extraction networks. Full article

(This article belongs to the Topic Geospatial Digital Innovations for Smart Agriculture and Forestry)

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20 pages, 21211 KiB  

Open AccessArticle

A Lightweight Forest Scene Image Dehazing Network Based on Joint Image Priors

by Xixuan Zhao, Yu Miao, Zihui Jin, Jiaming Zhang and Jiangming Kan

Forests 2023, 14(10), 2062; https://doi.org/10.3390/f14102062 - 16 Oct 2023

Cited by 1 | Viewed by 993

Abstract

Fog interference is an unfavorable issue when using vision sensors to monitor forest environmental resources. The existence of fog causes intelligent forest vision sensor equipment to fail to obtain accurate information on environmental resources. Therefore, this study proposes a lightweight forest scene image [...] Read more.

Fog interference is an unfavorable issue when using vision sensors to monitor forest environmental resources. The existence of fog causes intelligent forest vision sensor equipment to fail to obtain accurate information on environmental resources. Therefore, this study proposes a lightweight forest scene image dehazing network to remove fog interference from the vision system. To deal with the extraction of detailed forest image features, we propose utilizing joint image priors including white balance, contrast, and gamma correction feature maps as inputs of the network to strengthen the learning ability of the deep network. Focusing on reducing the computational cost of the network, four different kinds of Ghost Bottleneck blocks, which adopt an SE attention mechanism to better learn the abundant forest image features for our network, are adopted. Moreover, a lightweight upsampling module combining a bilinear interpolation method and a convolution operation is proposed, thus reducing the computing space used by the fog removal module in the intelligent equipment. In order to adapt to the unique color and texture features of forest scene images, the cost function consisting of $L_1$ loss and multi-scale structural similarity (MS-SSIM) loss is specially designed to train the proposed network. The experimental results show that our proposed method obtains more natural visual effects and better evaluation indices. The proposed network is trained both on indoor and outdoor synthetic datasets and tested on synthetic and real foggy images. The PSNR achieves an average value of 26.00 dB and SSIM achieves 0.96 on the indoor synthetic dataset, while PSNR achieves an average value of 25.58 dB and SSIM achieves 0.94 on the outdoor synthetic test images. The average processing time of our proposed dehazing network for a single foggy image with a size of 480 × 640 is 0.26 s. Full article

(This article belongs to the Topic Geospatial Digital Innovations for Smart Agriculture and Forestry)

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25 pages, 17444 KiB  

Open AccessArticle

Automatic Soil Sampling Site Selection in Management Zones Using a Multi-Objective Optimization Algorithm

by Meysam Kazemi and Faramarz F. Samavati

Agriculture 2023, 13(10), 1993; https://doi.org/10.3390/agriculture13101993 - 13 Oct 2023

Viewed by 814

Abstract

Precision agriculture hinges on accurate soil condition data obtained through soil testing across the field, which is a foundational step for subsequent processes. Soil testing is expensive, and reducing the number of samples is an important task. One viable approach is to divide [...] Read more.

Precision agriculture hinges on accurate soil condition data obtained through soil testing across the field, which is a foundational step for subsequent processes. Soil testing is expensive, and reducing the number of samples is an important task. One viable approach is to divide the farm fields into homogenous management zones that require only one soil sample. As a result, these sample points must be the best representatives of the management zones and satisfy some other geospatial conditions, such as accessibility and being away from field borders and other test points. In this paper, we introduce an algorithmic method as a framework for automatically determining locations for test points using a constrained multi-objective optimization model. Our implementation of the proposed algorithmic framework is significantly faster than the conventional GIS process. While the conventional process typically takes several days with the involvement of GIS technicians, our framework takes only 14 s for a 200-hectare field to find optimal benchmark sites. To demonstrate our framework, we use time-varying Sentinel-2 satellite imagery to delineate management zones and a digital elevation model (DEM) to avoid steep regions. We define the objectives for a representative area of a management zone. Then, our algorithm optimizes the objectives using a scalarization method while avoiding constraints. We assess our method by testing it on five fields and showing that it generates optimal results. This method is fast, repeatable, and extendable. Full article

(This article belongs to the Topic Geospatial Digital Innovations for Smart Agriculture and Forestry)

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19 pages, 7185 KiB  

Open AccessArticle

Carbon Biomass Estimation Using Vegetation Indices in Agriculture–Pasture Mosaics in the Brazilian Caatinga Dry Tropical Forest

by Vicente de Paula Sousa Júnior, Javier Sparacino, Giovana Mira de Espindola and Raimundo Jucier Sousa de Assis

ISPRS Int. J. Geo-Inf. 2023, 12(9), 354; https://doi.org/10.3390/ijgi12090354 - 27 Aug 2023

Cited by 1 | Viewed by 1881

Abstract

Remote sensing is valuable for estimating aboveground biomass (AGB) stocks. However, its application in agricultural and pasture areas is limited compared with forest areas. This study quantifies AGB in agriculture–pasture mosaics within Brazil’s Campo Maior Complex (CMC). The methodology employs remote sensing cloud [...] Read more.

Remote sensing is valuable for estimating aboveground biomass (AGB) stocks. However, its application in agricultural and pasture areas is limited compared with forest areas. This study quantifies AGB in agriculture–pasture mosaics within Brazil’s Campo Maior Complex (CMC). The methodology employs remote sensing cloud processing and utilizes an estimator to incorporate vegetation indices. The results reveal significant changes in biomass values among land use and land cover classes over the past ten years, with notable variations observed in forest plantation, pasture, sugar cane, and soybean areas. The estimated AGB values range from 0 to 20 Mg.ha⁻¹ (minimum), 53 to 419 Mg.ha⁻¹ (maximum), and 19 to 57 Mg.ha⁻¹ (mean). In Forest formation areas, AGB values range from approximately 0 to 278 Mg.ha⁻¹, with an average annual value of 56.44 Mg.ha⁻¹. This study provides valuable insights for rural landowners and government officials in managing the semiarid territory and environment. It aids in decision making regarding agricultural management, irrigation and fertilization practices, agricultural productivity, land use and land cover changes, biodiversity loss, soil degradation, conservation strategies, the identification of priority areas for environmental restoration, and the optimization of resource utilization. Full article

(This article belongs to the Topic Geospatial Digital Innovations for Smart Agriculture and Forestry)

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