scholarly journals Improved Spectral Angle Mapper applications for mangrove classification using SPOT5 imagery

2019 ◽  
Author(s):  
Xiu Su ◽  
Xiang Wang ◽  
Jianhua Zhao ◽  
Ke Cao ◽  
Jianchao Fan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Spectral Feature ◽  
Mangrove Ecosystem ◽  
Training Sample ◽  
Mangrove Species ◽  
Spectral Angle Mapper ◽  
Structure Information ◽  
Mangrove Ecosystems ◽  
Spectral Angle

Abstract. The traditional Spectral Angle Mapper (SAM) is an image classification method that uses image endmember spectra. Image spatial structure information may be neglected, especially in mangrove classification research where there is greater spectral similarity between species. This study combined object-oriented classification to improve the accuracy of the method in mangrove ecosystems. A mangrove area in Guangxi's coastal zone was chosen as the study site, and spectral feature analysis and ground investigations were carried out, combining pixel purification, training sample set optimization, and watershed image segmentation algorithm to improve the SAM. The improved SAM was used to classify SPOT5 remote sensing image data for a mangrove ecosystem and then classification accuracy was assessed. The results showed that the improved SAM had better classification accuracy for SPOT5 imagery. Accuracy for each mangrove species was greater than 80 % and overall accuracy was greater than 90 %, which showed that SAM was applicable for mangrove remote sensing. This application potential for classification and information extraction lays the foundation for commercialized remote sensing monitoring of mangrove ecosystems.

scholarly journals Regeneration capacity and threats to mangrove areas on the southern coast of Oriental Mindoro, Philippines: Implications to mangrove ecosystem rehabilitation

2020 ◽  
Vol 21 (8) ◽  
Author(s):  
AARON FROILAN RAGANAS ◽  
ANNALEE S. HADSALL ◽  
NELSON M. PAMPOLINA ◽  
STEFAN HOTES ◽  
DAMASA B. MAGCALE-MACANDOG
Keyword(s):  
Dominant Species ◽  
Avicennia Marina ◽  
Mangrove Ecosystem ◽  
Regeneration Capacity ◽  
Southern Coast ◽  
Mangrove Species ◽  
Rhizophora Apiculata ◽  
Mangrove Ecosystems ◽  
Sonneratia Alba ◽  
Ecosystem Rehabilitation

Abstract. Raganas AFM, Hadsall AS, Pampolina NM, Hotes S, Magcale-Macandog DB. 2020. Regeneration capacity and threats to mangrove areas on the southern coast of Oriental Mindoro, Philippines: Implications to mangrove ecosystem rehabilitation. Biodiversitas 21: 3625-3636. Regeneration capacity is important as it determines the fate of an ecosystem. This study assessed six mangrove areas in the southern coast of Oriental Mindoro, Philippines to evaluate their regeneration capacity status. Four mangrove ecotypes were delineated namely seaward, middle, landward and riverine zones at each mangrove ecosystem, where dominant mangrove species were identified and selected for regeneration capacity study. Three subplots measuring 1 x 1 m2 were laid within the five 10 x 10 m2 survey plots established per zone. The juveniles were counted and categorized according to their height classes, using linear regeneration sampling method; where: RCI (≤40 cm) considered seedlings; RCII (41-150 cm) as saplings; and RCIII (151-≤300 cm) as small trees. Potential threats both anthropogenic and natural were determined through key informant interviews. Seven dominant species were identified across ecotypes in all mangrove sites, namely Avicennia marina, Avicennia rumphiana, Ceriops decandra, Rhizophora apiculata, Rhizophora mucronata, Sonneratia alba, and Xylocarpus granatum. RCI (seedlings) is the most abundant across mangrove sites irrespective of the dominant species. Fishpond operation within the mangrove stand is considered a major threat to the juveniles and most mangrove ecosystems. Therefore, protection and constant monitoring of these mangrove ecosystems are necessary to ensure regeneration success in the future.

scholarly journals Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges

2019 ◽  
Vol 11 (3) ◽  
pp. 230 ◽  
Author(s):  
Tien Pham ◽  
Naoto Yokoya ◽  
Dieu Bui ◽  
Kunihiko Yoshino ◽  
Daniel Friess
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Mangrove Ecosystem ◽  
Carbon Stocks ◽  
Machine Learning Techniques ◽  
Species Structure ◽  
Mangrove Species ◽  
Mitigation And Adaptation ◽  
Sensing Applications ◽  
Wide Range

The mangrove ecosystem plays a vital role in the global carbon cycle, by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, mangroves have been lost worldwide, resulting in substantial carbon stock losses. Additionally, some aspects of the mangrove ecosystem remain poorly characterized compared to other forest ecosystems due to practical difficulties in measuring and monitoring mangrove biomass and their carbon stocks. Without a quantitative method for effectively monitoring biophysical parameters and carbon stocks in mangroves, robust policies and actions for sustainably conserving mangroves in the context of climate change mitigation and adaptation are more difficult. In this context, remote sensing provides an important tool for monitoring mangroves and identifying attributes such as species, biomass, and carbon stocks. A wide range of studies is based on optical imagery (aerial photography, multispectral, and hyperspectral) and synthetic aperture radar (SAR) data. Remote sensing approaches have been proven effective for mapping mangrove species, estimating their biomass, and assessing changes in their extent. This review provides an overview of the techniques that are currently being used to map various attributes of mangroves, summarizes the studies that have been undertaken since 2010 on a variety of remote sensing applications for monitoring mangroves, and addresses the limitations of these studies. We see several key future directions for the potential use of remote sensing techniques combined with machine learning techniques for mapping mangrove areas and species, and evaluating their biomass and carbon stocks.

scholarly journals Mangrove management strategy to support fisheries in Mangunharjo Village, Semarang City

2020 ◽  
Vol 202 ◽  
pp. 06016
Author(s):  
Irene Natalia Siahaan ◽  
Jafron Wasiq ◽  
Kismartini
Keyword(s):  
Research Method ◽  
Mangrove Ecosystem ◽  
Mangrove Species ◽  
Economic Resource ◽  
Descriptive Research ◽  
Urban Village ◽  
Mangrove Ecosystems ◽  
The People ◽  
Data Collection Technique ◽  
Development Resource

Mangrove ecosystems have unique characteristics and forms and have functions and benefits as a development resource both as an economic resource and an ecological resource that has long been felt by the people who live around the coastal area. In the last few years, mangrove ecosystems in Mangunharjo Urban Village have been continuously under pressure due to human activities. The main factors causing mangrove damage, namely: (1) Pollution, (2) Conversion of mangrove ecosystems into ponds and (3) Excessive logging. Mangunharjo Village has brackish water fishery potential by having a pond area of ± 10.45 hectares. The research method used in this research is to use a descriptive research method. The data collection technique used is the study of literature. The results showed that the condition of mangrove ecosystems in Mangunharjo Subdistrict decreased from 1990 to 1995 by 50%, but began to increase again in 2002 to 2015 by 18.42%. Mangrove species found in this study were Rhizophora sp, Avicennia sp, Xylocarpus sp and Bruguiera sp. Mangunharjo mangrove ecosystem has the highest density of mangrove species, namely Avicennia sp. As for the results of the analysis of the extent of mangrove ecosystems on the coast of Mangunharjo with the results of fisheries production, it shows that during the period before abrasion the farmer's income was IDR 1,000,000.00 / day these conditions continue to decline to IDR 100,000.00 to IDR 30,000.00 / day until early in 2000.

scholarly journals Asbestos—Cement Roofing Identification Using Remote Sensing and Convolutional Neural Networks (CNNs)

2020 ◽  
Vol 12 (3) ◽  
pp. 408
Author(s):  
Małgorzata Krówczyńska ◽  
Edwin Raczko ◽  
Natalia Staniszewska ◽  
Ewa Wilk
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Spectral Feature ◽  
Hyperspectral Data ◽  
Aerial Photographs ◽  
Support Vector ◽  
Spectral Angle Mapper ◽  
Asbestos Cement ◽  
Feature Fitting

Due to the pathogenic nature of asbestos, a statutory ban on asbestos-containing products has been in place in Poland since 1997. In order to protect human health and the environment, it is crucial to estimate the quantity of asbestos–cement products in use. It has been evaluated that about 90% of them are roof coverings. Different methods are used to estimate the amount of asbestos–cement products, such as the use of indicators, field inventory, remote sensing data, and multi- and hyperspectral images; the latter are used for relatively small areas. Other methods are sought for the reliable estimation of the quantity of asbestos-containing products, as well as their spatial distribution. The objective of this paper is to present the use of convolutional neural networks for the identification of asbestos–cement roofing on aerial photographs in natural color (RGB) and color infrared (CIR) compositions. The study was conducted for the Chęciny commune. Aerial photographs, each with the spatial resolution of 25 cm in RGB and CIR compositions, were used, and field studies were conducted to verify data and to develop a database for Convolutional Neural Networks (CNNs) training. Network training was carried out using the TensorFlow and R-Keras libraries in the R programming environment. The classification was carried out using a convolutional neural network consisting of two convolutional blocks, a spatial dropout layer, and two blocks of fully connected perceptrons. Asbestos–cement roofing products were classified with the producer’s accuracy of 89% and overall accuracy of 87% and 89%, depending on the image composition used. Attempts have been made at the identification of asbestos–cement roofing. They focus primarily on the use of hyperspectral data and multispectral imagery. The following classification algorithms were usually employed: Spectral Angle Mapper, Support Vector Machine, object classification, Spectral Feature Fitting, and decision trees. Previous studies undertaken by other researchers showed that low spectral resolution only allowed for a rough classification of roofing materials. The use of one coherent method would allow data comparison between regions. Determining the amount of asbestos–cement products in use is important for assessing environmental exposure to asbestos fibres, determining patterns of disease, and ultimately modelling potential solutions to counteract threats.

scholarly journals The Tradeoff Analysis for Remote Sensing Image Fusion Using Expanded Spectral Angle Mapper

Sensors ◽  
2008 ◽  
Vol 8 (1) ◽  
pp. 520-528 ◽  
Author(s):  
Shaohui Chen ◽  
Hongbo Su ◽  
Renhua Zhang ◽  
Jing Tian ◽  
Lihu Yang
Keyword(s):  
Remote Sensing ◽  
Image Fusion ◽  
Remote Sensing Image ◽  
Spectral Angle Mapper ◽  
Tradeoff Analysis ◽  
Remote Sensing Image Fusion ◽  
Spectral Angle

scholarly journals SUSTAINABILITY ANALYSIS OF MANGROVE ECOSYSTEMSIN SUNGAI BELA VILLAGE, INDRAGIRI HILIR REGENCY

2019 ◽  
Vol 46 (3) ◽  
pp. 46
Author(s):  
Mohd Riza Fahlifi ◽  
Deni Efizon ◽  
Adriman Adriman
Keyword(s):  
Social Economic ◽  
Mangrove Ecosystem ◽  
Cultural Dimensions ◽  
Survey Method ◽  
Mangrove Species ◽  
Dimensional Scaling ◽  
Mangrove Ecosystems ◽  
Index Value ◽  
Ecological Dimension

This study aims to determine the level of sustainability of mangrove ecosystems in Sungai Bela Village. The method used in this study is a survey method with data analysis using the Multi Dimensional Scaling (MDS). The results showed that the index value of the sustainability of mangrove ecosystems showed an ecological dimension(61.42) with RMS (2.28), social, economic and cultural dimensions (50.91) with RMS (2.51), legal and institutional dimensions (61.91) with RMS (1.79).Several factors that affect the sustainability of mangrove ecosystems such as:(1) mangrove species diversity;(2) density of mangrove ecosystems;(3) content of sediment organic matter;(4) marketing of fishery products;(5) mangrove dependence on livelihoods;(6) level of community knowledge;(7) the role of community leaders;(8) level of community compliance and (9) community participation.It can be concluded that the mangrove ecosystem in Sungai Bela Village with a sufficiently continuous status.

scholarly journals Environmental and Education Trials for Mangrove Ecosystem Rehabilitation in China

2021 ◽  
Author(s):  
Ruili Li ◽  
Minwei Chai ◽  
Xiaoxue Shen ◽  
Cong Shi ◽  
Guoyu Qiu ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Metal Pollution ◽  
Heavy Metal Pollution ◽  
Southern China ◽  
Mangrove Ecosystem ◽  
Mangrove Species ◽  
The Public ◽  
Mangrove Ecosystems ◽  
Representative Species ◽  
Mangrove Wetlands

Based on Chinese ecological policy, we have been studying mangrove ecosystems in southern China, especially from the perspective of pollutants deposition in mangrove wetlands, physiological ecology of mangrove species on the impact of heavy metal pollution and seeking ecosystem restoration. For these, we explored in three aspects: 1) pollutants distribution and ecological risk in main distribution of mangrove, China, 2) eco-statistics and microbial analyses of mangrove ecosystems (including shellfish) in representative locations where mangrove plants are well developed, especially in Shenzhen, a rapid developing economic city in Guangdong Province, 3) ecophysiological experiments on a representative species of mangrove for evaluating combination effects of major nutrient elements and heavy metal pollution on growth and physiological responses of the seedlings. Based on the results, we proposed how to rehabilitate mangrove ecosystem in China under rapidly changing environmental conditions, with a view to our future survival and to provide nature-based solution as well as the public with more ecosystem services.

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