Geospatial Technologies for Crops and Soils: An Overview

Mitran, Tarik; Meena, Ram Swaroop; Chakraborty, Abhishek

doi:10.1007/978-981-15-6864-0_1

Cited by 12 publications

(11 citation statements)

References 166 publications

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“…Preservation of olive groves as an element of Mediterranean identity [6] and a strategic economic resource [7] depends on sustainable environmental management, based on the improvement of the competitiveness of the agricultural sector and on precision agricultural methodology and technology [8,9]. The development of geospatial technologies has enabled precise mapping and inventorying of olives [10,11]. The application of unmanned aerial vehicles (UAV) in aerial photogrammetry is widely accepted as a reliable and accurate remote sensing method for environmental protection and mapping of vegetation species [10,12,13].…”

Section: Introductionmentioning

confidence: 99%

Comparative Assessment of Pixel and Object-Based Approaches for Mapping of Olive Tree Crowns Based on UAV Multispectral Imagery

et al. 2022

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Pixel-based (PB) and geographic-object-based (GEOBIA) classification approaches allow the extraction of different objects from multispectral images (MS). The primary goal of this research was the analysis of UAV imagery applicability and accuracy assessment of MLC and SVM classification algorithms within PB and GEOBIA classification approaches. The secondary goal was to use different accuracy assessment metrics to determine which of the two tested classification algorithms (SVM and MLC) most reliably distinguishes olive tree crowns and which approach is more accurate (PB or GEOBIA). The third goal was to add false polygon samples for Correctness (COR), Completeness (COM) and Overall Quality (OQ) metrics and use them to calculate the Total Accuracy (TA). The methodology can be divided into six steps, from data acquisition to selection of the best classification algorithm after accuracy assessment. High-quality DOP (digital orthophoto) and UAVMS were generated. A new accuracy metric, called Total Accuracy (TA), combined both false and true positive polygon samples, thus providing a more comprehensive insight into the assessed classification accuracy. The SVM (GEOBIA) was the most reliable classification algorithm for extracting olive tree crowns from UAVMS imagery. The assessment carried out indicated that application of GEOBIA-SVM achieved a TACOR of 0.527, TACOM of 0.811, TAOQ of 0.745, Overall Accuracy (OA) of 0.926 or 0.980 and Area Under Curve (AUC) value of 0.904 or 0.929. The calculated accuracy metrics confirmed that the GEOBIA approach (SVM and MLC) achieved more accurate olive tree crown extraction than the PB approach (SVM and MLC) if applied to classifying VHR UAVMS imagery. The SVM classification algorithm extracted olive tree crowns more accurately than MLC in both approaches. However, the accuracy assessment has proven that PB classification algorithms can also achieve satisfactory accuracy.

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Section: Introductionmentioning

confidence: 99%

Comparative Assessment of Pixel and Object-Based Approaches for Mapping of Olive Tree Crowns Based on UAV Multispectral Imagery

et al. 2022

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“…𝑃𝑜𝑠 𝑡 𝑃𝑜𝑠 𝑡 1 𝑉 𝑡 (10) where 𝐵𝑒𝑠𝑡_𝑝𝑜𝑠, 𝑅, and 𝑟𝑎𝑛𝑑 represent the current optimal position within the global search space, the influence of map and compass, and a random number, respectively. The blue circle positioned at the center in Figure 4b signifies the iterative destination of the pigeon flock, indicating that the pigeons within will promptly reach their intended destination.…”

Section: Pigeon-inspired Optimization Iteration Processmentioning

confidence: 99%

“…Spectral information in Forests 2024, 15, 456 2 of 21 the red and near-infrared bands of optical data is highly sensitive to AGB, which has led numerous studies to utilize vegetation and biophysical parameters for AGB estimation [7,8]. Additionally, SAR can offer valuable insights into dielectric properties and vertical structure by employing appropriate polarization [9][10][11]. Recently, multi-source data have become a trend in AGB inversion, and previous studies have integrated optical data with SAR data to achieve precise results [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework for Forest Above-Ground Biomass Inversion Using Multi-Source Remote Sensing and Deep Learning

Zhang,

Zhou,

Zhang

et al. 2024

Forests

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The estimation of forest above-ground biomass (AGB) can be significantly improved by leveraging remote sensing (RS) and deep learning (DL) techniques. In this process, it is crucial to obtain appropriate RS features and develop a suitable model. However, traditional methods such as random forest (RF) feature selection often fail to adequately consider the complex relationships within high-dimensional RS feature spaces. Moreover, challenges related to parameter selection and overfitting inherent in DL models may compromise the accuracy of AGB estimation. Therefore, this study proposes a novel framework based on freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical data. Firstly, we designed new indices through the formula analogous with vegetation index calculation to integrate multidimensional spectral and structural information. Then, leveraging the simplicity of computational principles, a pigeon-inspired optimization algorithm (PIO) was introduced into a bi-directional long short-term memory neural network (PIO-BiLSTM), which achieved the set objective function through repeated iteration and validation to obtain the optimal model parameters. Finally, to verify the framework’s effect, we conducted experiments in two different tree species and compared another seven classical optimization algorithms and machine learning models. The results indicated that the new indices significantly improved the inversion accuracy of all models in both categories, and the PIO-BiLSTM model achieved the highest accuracy (Category-1: R2 = 0.8055, MAE = 8.8475 Mg·ha−1, RMSE = 12.2876 Mg·ha−1, relative RMSE = 18.1715%; Category-2: R2 = 0.7956, MAE = 1.7103 Mg·ha−1, RMSE = 2.2887 Mg·ha−1, relative RMSE = 9.3000%). Compared with existing methods, the proposed framework greatly reduced the labor costs in parameter selection, and its potential uncertainty also decreased by up to 9.0%. Furthermore, the proposed method has a strong generalization ability and is independent of tree species, indicating its great potential for future forest AGB inversion in wider regions with diverse forest types.

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“…Assessments of land degradation, desertification, drought risk, and soil erosion could be performed using DSM for designing suitable region-specific action plans (Arrouays et al, 2014). DSM can also be targeted for supervising vast areas of problematic soils of the country including acid soils, salt-affected soils, waterlogged areas, and degraded lands (Mitran et al, 2021).…”

Section: Dsm Applicationsmentioning

confidence: 99%

Identifying opportunities to improve digital soil mapping in India: A systematic review

2022

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Geospatial Technologies for Crops and Soils: An Overview

Cited by 12 publications

References 166 publications

Comparative Assessment of Pixel and Object-Based Approaches for Mapping of Olive Tree Crowns Based on UAV Multispectral Imagery

Comparative Assessment of Pixel and Object-Based Approaches for Mapping of Olive Tree Crowns Based on UAV Multispectral Imagery

A Novel Framework for Forest Above-Ground Biomass Inversion Using Multi-Source Remote Sensing and Deep Learning

Identifying opportunities to improve digital soil mapping in India: A systematic review

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