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	16.6 Days	CHF 2600	Submit
Forests forests	2.9	4.5	2010	19 Days	CHF 2600	Submit
ISPRS International Journal of Geo-Information ijgi	3.4	6.2	2012	35.2 Days	CHF 1700	Submit
Remote Sensing remotesensing	5.0	7.9	2009	21.1 Days	CHF 2700	Submit

Published Papers (3 papers)

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Journals

All Agriculture Forests IJGI Remote Sensing

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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 657

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 471

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

Viewed by 1307

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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