Geospatial Digital Innovations for Smart Agriculture and Forestry

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agriculture agriculture	3.3	4.9	2011	20.2 Days	CHF 2600
Forests forests	2.4	4.4	2010	16.9 Days	CHF 2600
ISPRS International Journal of Geo-Information ijgi	2.8	6.9	2012	36.2 Days	CHF 1700
Remote Sensing remotesensing	4.2	8.3	2009	24.7 Days	CHF 2700

23 pages, 6342 KiB

Open AccessReview

Scoping the Field: Recent Advances in Optical Remote Sensing for Precision Viticulture

by Kyle Loggenberg, Albert Strever and Zahn Münch

ISPRS Int. J. Geo-Inf. 2024, 13(11), 385; https://doi.org/10.3390/ijgi13110385 - 31 Oct 2024

Viewed by 636

The use of passive optical remote sensing (RS) has a rich history in precision viticulture (PV), with the use of RS technologies being employed in a myriad of PV applications. The present work undertakes a scoping review to examine past and current trends [...] Read more.

The use of passive optical remote sensing (RS) has a rich history in precision viticulture (PV), with the use of RS technologies being employed in a myriad of PV applications. The present work undertakes a scoping review to examine past and current trends in the use of RS in grapevine production. It aims to identify literature gaps and new research opportunities. The Scopus database facilitated the search for relevant articles published between 2014 and 2023 using a search string of keywords. A total of 640 articles were produced by the Scopus search. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting framework, the 640 articles were reviewed based on predefined inclusion and exclusion criteria, resulting in 388 articles being deemed eligible for further data extraction. Four research questions were defined to guide the data extraction process, and a coding scheme was implemented to address these questions. The scoping review found Italy and the United States to be leading contributors to the research field, with vineyard mapping, yield estimation, and grapevine water status being the most extensively studied RS–PV applications. However, the use of RS to map vineyard soil properties and grapevine cultivars remains underexplored, presenting promising avenues for future research. Full article

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

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31 pages, 8230 KiB

Open AccessArticle

Using Knowledge Graphs to Analyze the Characteristics and Trends of Forest Carbon Storage Research at the Global Scale

by Jie Li, Jinliang Wang, Suling He, Chenli Liu and Lanfang Liu

ISPRS Int. J. Geo-Inf. 2024, 13(7), 234; https://doi.org/10.3390/ijgi13070234 - 1 Jul 2024

Viewed by 1290

Abstract

Research on forest carbon storage (FCS) is crucial for the sustainable development of human society given the context of global climate change. Previous FCS studies formed the science base of the FCS field but lacked a macrolevel knowledge summary. This study combined the [...] Read more.

Research on forest carbon storage (FCS) is crucial for the sustainable development of human society given the context of global climate change. Previous FCS studies formed the science base of the FCS field but lacked a macrolevel knowledge summary. This study combined the scientometric mapping tool VOSviewer and multiple statistical models to conduct a comprehensive knowledge graph mining and analysis of global FCS papers (covering 101 countries, 1712 institutions, 5435 authors, and 276 journals) in the Web of Science database as of 2022, focusing on revealing the macro spatiotemporal pattern, multidimensional research status, and topic evolution process of FCS research at the global scale, so as to grasp the status of global FCS research more clearly and comprehensively, thereby facilitating the future decision-making and practice of researchers. The results showed the following: (1) In the past three decades, the number of FCS papers indicated an increasing trend, with a growth rate of 4.66/yr, particularly significant after 2010. These papers were mainly from Europe, the Americas, and Asia, while there was a huge gap between Africa, Oceania, and the above regions. (2) For the research status at the national, institutional, scholar, and journal levels, the USA, with 331 FCS papers and 18,653 total citations, was the most active and influential country in global FCS research; the United States Forest Service topped the influential ranking with 4115 citations; Grant M. Domke and Jerome Chave were the most active and influential FCS researchers globally, respectively. China’s activity (237 papers) and influence (5403 citations) ranked second, and the Chinese Academy of Sciences was the most active research institution in the world. Currently, FCS research is published in a growing number of journals, among which Forest Ecology and Management ranked first in the number of papers (154 papers) and citations (6374 citations). (3) In recent years, the keyword frequency of monitoring methods, driving factors, and reasonable management for FCS has increased rapidly, and many new related keywords have emerged, which means that researchers are not only focusing on the estimation and monitoring of FCS but also increasingly concerned about its driving mechanism and sustainable development. Full article

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

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20 pages, 14379 KiB

Open AccessArticle

Integrating Climate and Satellite Data for Multi-Temporal Pre-Harvest Prediction of Head Rice Yield in Australia

by Allister Clarke, Darren Yates, Christopher Blanchard, Md. Zahidul Islam, Russell Ford, Sabih-Ur Rehman and Robert Paul Walsh

Remote Sens. 2024, 16(10), 1815; https://doi.org/10.3390/rs16101815 - 20 May 2024

Viewed by 1326

Abstract

Precise and prompt predictions of crop yields are crucial for optimising farm management, post-harvest operations, and marketing strategies within the agricultural sector. While various machine learning approaches have been employed to forecast crop yield, their application to grain quality, particularly head rice yield [...] Read more.

Precise and prompt predictions of crop yields are crucial for optimising farm management, post-harvest operations, and marketing strategies within the agricultural sector. While various machine learning approaches have been employed to forecast crop yield, their application to grain quality, particularly head rice yield (HRY), is less explored. This research collated crop-level HRY data across four seasons (2017/18–2020/21) from Australia’s rice-growing region. Models were developed using the XGBoost algorithm trained at varying time steps up to 16 weeks pre-harvest. The study compared the accuracy of models trained on datasets with climate data alone or paired with vegetative indices using two- and four-week aggregations. The results suggest that model accuracy increases as the harvest date approaches. The dataset combining climate and vegetative indices aggregated over two weeks surpassed industry benchmarks early in the season, achieving the highest accuracy two weeks before harvest (LCCC = 0.65; RMSE = 6.43). The analysis revealed that HRY correlates strongly with agroclimatic conditions nearer harvest, with the significance of vegetative indices-based features increasing as the season progresses. These features, indicative of crop and grain maturity, could aid growers in determining optimal harvest timing. This investigation offers valuable insights into grain quality forecasting, presenting a model adaptable to other regions with accessible climate and satellite data, consequently enhancing farm- and industry-level decision-making. Full article

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

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20 pages, 6362 KiB

Open AccessArticle

by Roghayeh Heidari and Faramarz F. Samavati

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

Viewed by 1177

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 (

Z D M

) 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 (

Z D M

) 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

Z D M

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 1483

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 1715

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 1396

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

Cited by 2 | Viewed by 1404

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 2913

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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