Mapping and assessment of wetland conditions by using remote sensing images and POI data

Yang, Zili; Bai, Junwu; Zhang, Weiwei

doi:10.1016/j.ecolind.2021.107485

Cited by 19 publications

(6 citation statements)

References 54 publications

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“…POIs (points of interest) are selected to interpret the intensity of human and economic activity. Compared to other factors, such as population density, distance from the city center, building density, and gross domestic product, POIs are comprehensive spatial data and do not involve the issue of statistical caliber, which fits with the purpose of this study to identify the main factor of the spatial distribution of ancient trees by a geoinformatics-based approach [43,44]. The quantification standard of each influencing factor is shown in Table 1.…”

Section: Indicator System and Data Sourcementioning

confidence: 85%

What Drives the Spatial Variation of Interregional Ancient Trees? A Geoinformatics–Based Approach in Henan, Central China

Hou,

Ren,

Wang

et al. 2024

Forests

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In the context of ecological civilization construction in China, interregional ancient trees are important natural and cultural resources, and it is vital that they are effectively protected and utilized according to their spatial distribution and regional conditions in order to provide a balance between local economic development and the ecological environment. This article analyzes the spatial heterogeneity of 26,842 ancient trees and explores the underlying natural and human factors by using geoinformatics–based techniques (i.e., the nearest neighbor index, kernel density, spatial autocorrelation, and the geographically weighted regression model) in Henan Province, Central China. The research findings conclude that: (1) The interregional ancient trees show positive spatial autocorrelation, and the aggregation effect in Western Henan is the most significant. (2) The growing environment and elevation exert a more obvious influence on the spatial pattern of interregional ancient trees. (3) The main influencing factors per region are different, for example, historical culture in Western Henan, water source and elevation in Southern Henan, and the growing environment in Eastern Henan, while ancient trees in Northern Henan have a weak relationship with each factor. This research facilitates political strategy making for sustainable development and the protection of interregional ancient trees.

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Section: Indicator System and Data Sourcementioning

confidence: 85%

What Drives the Spatial Variation of Interregional Ancient Trees? A Geoinformatics–Based Approach in Henan, Central China

Hou,

Ren,

Wang

et al. 2024

Forests

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“…Wetlands have significant impacts on a variety of plant and animal species, ecosystem dynamics, the energy exchange between the atmosphere and soil, and terrestrial biodiversity [1]. Despite their ecological significance, wetlands have been degraded as a result of agricultural activities, construction projects, population growth, and natural processes such as global warming, precipitation reduction, and coastal destruction [2,3].…”

Section: Introductionmentioning

confidence: 99%

Wetland Mapping in Great Lakes Using Sentinel-1/2 Time-Series Imagery and DEM Data in Google Earth Engine

et al. 2023

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The Great Lakes (GL) wetlands support a variety of rare and endangered animal and plant species. Thus, wetlands in this region should be mapped and monitored using advanced and reliable techniques. In this study, a wetland map of the GL was produced using Sentinel-1/2 datasets within the Google Earth Engine (GEE) cloud computing platform. To this end, an object-based supervised machine learning (ML) classification workflow is proposed. The proposed method contains two main classification steps. In the first step, several non-wetland classes (e.g., Barren, Cropland, and Open Water), which are more distinguishable using radar and optical Remote Sensing (RS) observations, were identified and masked using a trained Random Forest (RF) model. In the second step, wetland classes, including Fen, Bog, Swamp, and Marsh, along with two non-wetland classes of Forest and Grassland/Shrubland were identified. Using the proposed method, the GL were classified with an overall accuracy of 93.6% and a Kappa coefficient of 0.90. Additionally, the results showed that the proposed method was able to classify the wetland classes with an overall accuracy of 87% and a Kappa coefficient of 0.91. Non-wetland classes were also identified more accurately than wetlands (overall accuracy = 96.62% and Kappa coefficient = 0.95).

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“…The use of satellites for water quality monitoring has been widely used in oceans, inland lakes, and rivers [3], but few studies for monitoring wetland water quality have been reported. Therefore, the wetland remote sensing always focuses on ecosystem monitoring, soil matters and boundary changes [4]- [7]. The multi-spectral sensor of the satellite is sensitive to chlorophyll and suspended particulate matter, and these two substances can indicate water quality and have some correlation with water quality indicators such as nitrogen and phosphorus, so it can be indirectly used to reverse the concentration of nitrogen and phosphorus [8].…”

Section: Introductionmentioning

confidence: 99%

Comparing the machine learning and empirical algorithms on remote sensing estimates water quality parameters of constructed wetland

Qian

Zhao²,

Wang³

et al. 2023

International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022)

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The water quality parameters (WQPs) at the constructed wetland of the lakeside zone are fluctuating. In order to accurately estimate the water purification efficiency of the lakeside belt, the remote sensing technology by satellite has a great advantage to complete such a task. In this study, back-propagation neural network and polynomial regression are compared in remote sensing estimate Total Nitrogen (TN), Total Phosphorus (TP), Nitrate Nitrogen (NN), and Ammonia Nitrogen (AN) concentrations in constructed wetland water quality. The result shows that the BP neural network algorithms outperformed the polynomial regression algorithms in the estimate AN and TN. However, the polynomial regression algorithms have achieved better performance in the estimate NN and TP. Moreover, the best algorithms produce about 60% of rRMSE in all WQPs in this study. As to mean normalized bias (MNB) result, the overall estimate by the BP neural network algorithms is lower than the measured data. In addition to TP, the empirical model is the opposite. This study could provide some reference for the remote sensing estimate of the water purification efficiency in constructed wetlands. Furthermore, BP neural network performance is more stable than the polynomial regression.

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Mapping and assessment of wetland conditions by using remote sensing images and POI data

Cited by 19 publications

References 54 publications

What Drives the Spatial Variation of Interregional Ancient Trees? A Geoinformatics–Based Approach in Henan, Central China

What Drives the Spatial Variation of Interregional Ancient Trees? A Geoinformatics–Based Approach in Henan, Central China

Wetland Mapping in Great Lakes Using Sentinel-1/2 Time-Series Imagery and DEM Data in Google Earth Engine

Comparing the machine learning and empirical algorithms on remote sensing estimates water quality parameters of constructed wetland

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