The role of LAI and leaf chlorophyll on NDVI estimated by UAV in grapevine canopies

Caruso, Giovanni; Palai, Giacomo; Tozzini, Letizia; D'Onofrio, Claudio; Gucci, Riccardo

doi:10.1016/j.scienta.2023.112398

Cited by 14 publications

(2 citation statements)

References 41 publications

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“…The Normalized Difference Vegetation Index (NDVI) serves as a vital measure of vegetation density and vitality, offering crucial insights into vegetation coverage, which is a primary determinant factor influencing the stability and integrity of landscapes [58,59]. In this study, we used the mixed pixel separation method to calculate the NDVI according to the gray values B4 and B3 of the TM4 and TM3 band from the Landsat-8 data [60,61].…”

Section: Normalized Difference Vegetation Indexmentioning

confidence: 99%

Geological Disaster Susceptibility Evaluation Using Machine Learning: A Case Study of the Atal Tunnel in Tibetan Plateau

Bian,

Chen,

Liu

et al. 2024

Sustainability

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Tunnels serve as vital arteries in the realm of transportation and infrastructure, facilitating the seamless flow of movement across challenging terrains. With the increasing demand experienced by the traffic network on the Tibetan Plateau, deep-buried, lengthy tunnels have become one of the extremely important types of roads for local residents to pass through. Geological disaster susceptibility mapping by hybrid models has been proven to be an effective means to reduce the losses caused by disasters in a large area. However, there has been relatively little research conducted in tunnel areas with significant human activity. To explore the feasibility of conducting geological disaster susceptibility assessment in tunnel areas, we chose the Atal Tunnel as a study project; as a strategic passageway, this exemplifies the significant geological hurdles encountered on the Tibetan Plateau. Employing multi-source remote sensing data, we meticulously mapped the distribution of geological disasters and identified nine environmental and geological variables pivotal for susceptibility evaluation. We harnessed interpretable ensemble learning models to assess this susceptibility, comparing the efficacy of four distinct models: the weight of evidence method (WoE), the frequency ratio (FR), logistic regression (LR) and the support vector machine (SVM). The precision of our findings was rigorously tested using metrics such as the percentage of disaster area encompassed within each risk level, the Area Under the Curve (AUC) value, and the Receiver Operating Characteristic (ROC) curve, and all results were highly accurate. Notably, the WoE-LR model achieved superior performance, boasting an impressive accuracy rate of 90.7%. Through model interpretation, we discerned that the alignment of the road line is the most critical determinant in the evaluation of tunnel geological disaster susceptibility, underscoring the high precision of our model. The extension and successful application of this research in plateau areas hold profound implications for sustainable development. Moreover, the practical application of these research findings will provide a practical reference for the design and construction of projects in similar plateau areas, with positive outcomes that extend well beyond the immediate geographical area of the projects.

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Section: Normalized Difference Vegetation Indexmentioning

confidence: 99%

Geological Disaster Susceptibility Evaluation Using Machine Learning: A Case Study of the Atal Tunnel in Tibetan Plateau

Bian,

Chen,

Liu

et al. 2024

Sustainability

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“…Meanwhile, the FPAR represents the fraction of incident radiation above the crop canopy used for plant photosynthesis, reflecting the physiological activity of the host crop [28,29]. The NDVI is a classic vegetation index reflecting the comprehensive vigor and nutritional status of the crop plants [30]. Moreover, to extract the rice-planting area at a finer resolution, some moderate-resolution remote sensing data (i.e., Sentinel-1 and Sentinel-2 images) were also used in this study for rice mapping.…”

Section: Remote Sensing Datamentioning

confidence: 99%

Assessing Rice Sheath Blight Disease Habitat Suitability at a Regional Scale through Multisource Data Analysis

Zhang,

Li,

Tian

et al. 2023

Remote Sensing

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Extensive occurrence of rice sheath blight has been observed in China in recent years due to agricultural practices and climatic conditions, posing a serious threat to rice production. Assessing habitat suitability for rice sheath blight at a regional scale can provide important information for disease forecasting. In this context, the present study aims to propose a regional-scale habitat suitability evaluation method for rice sheath blight in Yangzhou city using multisource data, including remote sensing data, meteorological data, and disease survey data. By combining the epidemiological characteristics of the crop disease and the Relief-F algorithm, some habitat variables from key stages were selected. The maximum entropy (Maxent) and logistic regression models were adopted and compared in constructing the disease habitat suitability assessment model. The results from the Relief-F algorithm showed that some remote sensing variables in specific temporal phases are particularly crucial for evaluating disease habitat suitability, including the MODIS products of LAI (4–20 August), FPAR (9–25 June), NDVI (12–20 August), and LST (11–27 July). Based on these remote sensing variables and meteorological features, the Maxent model yielded better accuracy than the logistic regression model, with an area under the curve (AUC) value of 0.90, overall accuracy (OA) of 0.75, and a true skill statistics (TSS) value of 0.76. Indeed, the results of the habitat suitability assessment models were consistent with the actual distribution of the disease in the study area, suggesting promising predictive capability. Therefore, it is feasible to utilize remotely sensed and meteorological variables for assessing disease habitat suitability at a regional scale. The proposed method is expected to facilitate prevention and control practices for rice sheath blight disease.

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Assessing Maize (Zea Mays) Biomass at Different Growth Stages: A UAV-Based Multispectral Study on Nitrogen Effects

Merchan,

Calderon,

Broncano

et al. 2024

Lecture Notes in Geoinformation and Cartography

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The role of LAI and leaf chlorophyll on NDVI estimated by UAV in grapevine canopies

Cited by 14 publications

References 41 publications

Geological Disaster Susceptibility Evaluation Using Machine Learning: A Case Study of the Atal Tunnel in Tibetan Plateau

Geological Disaster Susceptibility Evaluation Using Machine Learning: A Case Study of the Atal Tunnel in Tibetan Plateau

Assessing Rice Sheath Blight Disease Habitat Suitability at a Regional Scale through Multisource Data Analysis

Assessing Maize (Zea Mays) Biomass at Different Growth Stages: A UAV-Based Multispectral Study on Nitrogen Effects

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