Integrating cross-sensor high spatial resolution satellite images to detect subtle forest vegetation change in the Purple Mountains, a national scenic spot in Nanjing, China

Zhu, Fangyan; Shen, Wenjuan; Diao, Jiaojiao; Li, Mingshi; Zheng, Guang

doi:10.1007/s11676-019-00978-x

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…The existing studies on the ecological problems of tourism destinations mainly focus on three aspects. The first is that resources are plundered in the development and operation of tourism destinations, and excessive environmental load poses a serious threat to the ecological environment [17], such as water pollution and water resource shortage [18], forest and vegetation destruction [19], soil pollution [20], climate change [21], etc. The second is the influencing factors that cause the ecological environment problems of tourism destinations [22], mainly including the construction of scenic facilities [23], social and economic factors [24], industrial structures [25], government environmental regulations [26], and tourist travel modes and behaviors [27].…”

Section: Introductionmentioning

confidence: 99%

Impact of Urbanization on Eco-Efficiency of Tourism Destinations

Zhang

Dong

et al. 2023

Sustainability

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Under the dual background of climate change and post-epidemic economic recovery, the study of the eco-efficiency of tourism destinations in the process of urbanization is critical to promoting the green and healthy development of tourism. This paper selects tourism destinations in 30 provinces of China in 2000–2019 as the research object, calculates the economic efficiency and eco-efficiency of China’s tourism destinations by constructing the Super-SBM (Slacks-Based Measure) model and visualizes the spatial distribution pattern and evolution trend of economic efficiency and eco-efficiency of China’s tourism destinations through spatial hotspot and center of gravity analysis. The coupling model is used to find the coupling relationship between the efficiency of China’s tourism destinations (economic efficiency and eco-efficiency) and urbanization. Finally, Tobit panel regression is used to find out how urbanization affects the eco-efficiency of tourism destinations. The results show that: (1) the eco-efficiency of tourism destinations in China is higher than the economic efficiency, as well as showing a downward trend. (2) The economic efficiency of tourism destinations in western China has increased while the eco-efficiency has declined. (3) China’s tourism destinations are undergoing the process of transformation and restructuring, and have not yet reached the decoupling standard. (4) On the whole, the improvement in urbanization is conducive to promoting the economic and environmentally sustainable development of tourism destinations. The main driving indicators are the living standards for urban residents, urban resources and environment, the industrial structure, and the role of the government. This study attempts to find a balance between the economic benefits and ecological impacts of tourism destinations and alleviate the real demand for the rapid economic recovery of tourism destinations in the post-epidemic era and the tension between human activities and the ecological environment. The research results are expected to provide a path for the healthy development of tourism destinations in the process of China’s new urbanization and provide a reference for tourism destinations in developing countries similar to China.

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Section: Introductionmentioning

confidence: 99%

Impact of Urbanization on Eco-Efficiency of Tourism Destinations

Zhang

Dong

et al. 2023

Sustainability

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“…The images used can be registered by sensors on-board three types of platforms: satellite, manned, and unmanned air platforms. Firstly, earth observation (EO) programs have been used in natural resource management to obtain images of medium [ 5 ] or high spatial resolution [ 6 ], offering data with different spatial, spectral, radiometric, and temporal resolution based on different technologies [ 7 ]. Furthermore, its global coverage reduces the intensity of sampling, and thus economic and temporary costs, and provides data on inaccessible or difficult-to-access areas.…”

Section: Introductionmentioning

confidence: 99%

“…Passive sensors are dependent on meteorological conditions, and there are limitations on acquiring traditional set of forest parameters obtained by the classical method, such as canopy diameter or basal areas. Nevertheless, these images have been widely used in forestry activities [ 5 , 6 , 8 ]. Manned aerial platforms allow forest inventory to be carried out on much larger areas compared to what is achievable with traditional field methods [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Individualization of Pinus radiata Canopy from 3D UAV Dense Point Clouds Using Color Vegetation Indices

Cabrera-Ariza

Lara-Gómez

Santelices-Moya

et al. 2022

Sensors

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The location of trees and the individualization of their canopies are important parameters to estimate diameter, height, and biomass, among other variables. The very high spatial resolution of UAV imagery supports these processes. A dense 3D point cloud is generated from RGB UAV images, which is used to obtain a digital elevation model (DEM). From this DEM, a canopy height model (CHM) is derived for individual tree identification. Although the results are satisfactory, the quality of this detection is reduced if the working area has a high density of vegetation. The objective of this study was to evaluate the use of color vegetation indices (CVI) in canopy individualization processes of Pinus radiata. UAV flights were carried out, and a 3D dense point cloud and an orthomosaic were obtained. Then, a CVI was applied to 3D point cloud to differentiate between vegetation and nonvegetation classes to obtain a DEM and a CHM. Subsequently, an automatic crown identification procedure was applied to the CHM. The results were evaluated by contrasting them with results of manual individual tree identification on the UAV orthomosaic and those obtained by applying a progressive triangulated irregular network to the 3D point cloud. The results obtained indicate that the color information of 3D point clouds is an alternative to support individualizing trees under conditions of high-density vegetation.

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“…changes across different sensor images has been recognized as a growing area of need. 22 The few approaches to date have either focused on a two-step process to (1) use deep learning or classification to transform disparate images to a common feature space and then (2) look for changes within that feature space; [23][24][25][26] or else have imposed signal interpolation to align channels (which is only possible between optical channels, and not extendable to crossmodality data). 27 These approaches do begin to address this challenge, but they require significant training imagery and/or a priori knowledge, which makes them very challenging to scale.…”

mentioning

confidence: 99%

Multi-sensor anomalous change detection in remote sensing imagery

Ziemann

Ren

Theiler

2021

J. Appl. Rem. Sens.

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Combining multiple satellite remote sensing sources provides a far richer, more frequent view of the earth than that of any single source; the challenge is in distilling these petabytes of heterogeneous sensor imagery into meaningful characterizations of the imaged areas. Meeting this challenge requires effective algorithms for combining multi-modal imagery over time to identify subtle but real changes among the intrinsic data variation. Here, we implement a jointdistribution framework for multi-sensor anomalous change detection (MSACD) that can effectively account for these differences in modality, and does not require any signal resampling of the pixel measurements. This flexibility enables the use of satellite imagery from different sensor platforms and modalities. We use multi-year construction of the SoFi Stadium in California as our testbed, and exploit synthetic aperture radar imagery from Sentinel-1 and multispectral imagery from both Sentinel-2 and Landsat 8. We show results for MSACD using real imagery with implanted, measurable changes, as well as real imagery with real, observable changes, including scaling our analysis over multiple years. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Integrating cross-sensor high spatial resolution satellite images to detect subtle forest vegetation change in the Purple Mountains, a national scenic spot in Nanjing, China

Cited by 5 publications

References 18 publications

Impact of Urbanization on Eco-Efficiency of Tourism Destinations

Impact of Urbanization on Eco-Efficiency of Tourism Destinations

Individualization of Pinus radiata Canopy from 3D UAV Dense Point Clouds Using Color Vegetation Indices

Multi-sensor anomalous change detection in remote sensing imagery

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