Tree Health Condition in Urban Green Areas Assessed through Crown Indicators and Vegetation Indices

Morales-Gallegos, Luis Manuel; Martínez-Trinidad, Tomás; Hernández-de la Rosa, Patricia; Gómez-Guerrero, Armando; Alvarado-Rosales, Dionicio; Saavedra-Romero, Luz de Lourdes

doi:10.3390/f14081673

Cited by 6 publications

(4 citation statements)

References 46 publications

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“…There are five commonly used visible-light vegetation indices: the red-to-green ratio index (RGRI) [44], excess green index (EXG) [45,46], visible light band difference vegetation index (VDVI) [47][48][49], normalized green-red difference index (NGRDI) [50], and normalized green-blue difference index (NGBDI) [51]. Among them, the VDVI has the highest overall accuracy rate of 97.47% [40], which indicates that the visible-wave vegetation index can be used to calculate vegetation coverage and has the highest applicability.…”

Section: Discussionmentioning

confidence: 99%

Research on Evaluating the Characteristics of the Rural Landscape of Zhanqi Village, Chengdu, China, Based on Oblique Aerial Photography by Unmanned Aerial Vehicles

Zhu,

Li,

Luo

et al. 2024

Sustainability

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To achieve the transition of rural areas from traditional to modern, the visualization of rural landscape data and feature evaluations are essential. Landscape character assessment (LCA) is a well-established tool that was developed to assess and understand rural landscape features. In recent years, drones have become increasingly attractive for various applications and services due to their low costs and relative ease of operation. Unlike most previous studies that relied solely on drone-based remote sensing or visual esthetic evaluations, this study proposes an innovative assessment method based on landscape characteristic assessment (LCA) and oblique drone photography technology, supported by specific data and survey results. These include various landscape metrics, such as the Shannon diversity index (SHDI), Shannon evenness index (SHEI), vegetation coverage, landscape character zoning, and delineations of various ecologically sensitive areas. This method was applied to study Zhanqi Village in Chengdu, Sichuan Province, China and revealed some unique characteristics of this village. By categorizing and describing the landscape features, the study makes judgments and decisions about them. This is a beneficial attempt to apply the scientific methods of landscape assessments to the production management of aerial drone surveys. This method provides a comprehensive framework for evaluating rural landscape features and demonstrates that the combination of LCA and oblique drone photography technology is feasible for rural landscape research. Additionally, this study emphasizes the need for further research to explore the potential application of this method in continuously evolving urban and rural environments in the future.

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Section: Discussionmentioning

confidence: 99%

Research on Evaluating the Characteristics of the Rural Landscape of Zhanqi Village, Chengdu, China, Based on Oblique Aerial Photography by Unmanned Aerial Vehicles

Zhu,

Li,

Luo

et al. 2024

Sustainability

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“…This study takes its annual median composite and generates the MedianGRVI index that is given below. MedianGRVI = [(Green − NIR)/(Green + NIR)] Median (7) In research by [27], the GRVI index was used as a phenological indicator on the basis of multiyear observations of phenology and spectral reflection for the ecosystem in Japan. The findings represent the relationship between seasonal vegetation changes and GRVI, having high temporal resolution.…”

Section: Mediangrvimentioning

confidence: 99%

Optical–SAR Data Fusion Based on Simple Layer Stacking and the XGBoost Algorithm to Extract Urban Impervious Surfaces in Global Alpha Cities

Ahmad,

Shao,

Javed

et al. 2024

Remote Sensing

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This study proposes a fusion approach to enhancing urban remote sensing applications by integrating SAR (Sentinel-1) and optical (Landsat-8) satellite datasets. The fusion technique combines feature-based fusion and simple layer stacking (SLS) to improve the accuracy of urban impervious surface (UIS) extraction. SAR textures and modified indices are used for feature extraction, and classification is performed using the XGBoost machine learning algorithm in Python and Google Earth Engine. The study focuses on four global cities (New York, Paris, Tokyo, and London) with heterogeneous climatic zones and urban dynamics. The proposed method showed significant results. The accuracy assessment using random validation points shows an overall accuracy of 86% for UIS classification with the SLS method, outperforming single-data classification. The proposed approach achieves higher accuracy (86%) compared to three global products (ESA, ESRI, and Dynamic World). New York exhibits the highest overall accuracy at 88%. This fusion approach with the XGBoost classifier holds potential for new applications and insights into UIS mapping, with implications for environmental factors such as land surface temperature, the urban heat island effect, and urban pluvial flooding.

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“…According to some studies, plants are crucial for maintaining a healthy urban environment, as plant roots and leaf litter have a significant impact on soil properties [13,14]. Moreover, vegetation type is considered as a determinant of soil quality as it makes a noticeable difference to soil properties.…”

Section: Introductionmentioning

confidence: 99%

Soil Properties and Bacterial Communities in Relation to Vegetation Types and Park Ages in Yancheng, China

Yang,

Wu,

Wang

et al. 2024

Agronomy

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Urban parks are considered one of the most significant ecosystems when looking at urban green spaces, but ecological functions and the type of recreation space created can change depending on the park’s age and its vegetation type. Therefore, the effects of the vegetation types present and urban park ages on soil properties and bacterial communities were tested in Yancheng, as it is a typical rapidly urbanizing city in China, and one of the most densely populated metropolises among the central cities of the Yangtze River Delta region. We found that the soil properties and bacterial community composition vary depending on vegetation type and park age. In addition, the pH value of soil planted with Cynodon dactylon is higher, and the available phosphorus concentrations in the old parks are at the highest levels, which are 1.20–2.66 times higher than in the middle-aged and young parks’ soil. Gammaproteobacteria, Alphaproteobacteria, Acidobacteria_6, and Deltaproteobacteria are the predominant bacteria phyla in urban park soil. A higher level of bacterial operational taxonomic units (OTUs) are found in Metasequoia glyptostroboides soil (5479, 69.7%) and middle-aged park soil (5670, 72.2%). Saprospirae, Chloracidobacteria, and Alphaproteobacteria are negatively correlated with pH to a significant extent. Additionally, pH, available potassium, and soil organic carbon were positively correlated with saccharase activity. Available phosphorus and nitrogen are related to soil community composition. These results indicate that both park age and vegetation type contribute to the differences in soil pH, available phosphorus, soil organic carbon, available potassium, available nitrogen, alkaline phosphatase, and soil bacterial composition within urban parks in Yancheng.

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Tree Health Condition in Urban Green Areas Assessed through Crown Indicators and Vegetation Indices

Cited by 6 publications

References 46 publications

Research on Evaluating the Characteristics of the Rural Landscape of Zhanqi Village, Chengdu, China, Based on Oblique Aerial Photography by Unmanned Aerial Vehicles

Research on Evaluating the Characteristics of the Rural Landscape of Zhanqi Village, Chengdu, China, Based on Oblique Aerial Photography by Unmanned Aerial Vehicles

Optical–SAR Data Fusion Based on Simple Layer Stacking and the XGBoost Algorithm to Extract Urban Impervious Surfaces in Global Alpha Cities

Soil Properties and Bacterial Communities in Relation to Vegetation Types and Park Ages in Yancheng, China

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