Erosion and accretion monitoring in mangrove forests using remote sensing and Digital Shoreline Analysis System (DSAS)(Case study: Hara Biosphere reserve)

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MAFI GHOLAMI DAVOOD; BAHARLOUII MASOUMEH; MAHMOUDI BEYTOLLAH

Title

Erosion and accretion monitoring in mangrove forests using remote sensing and Digital Shoreline Analysis System (DSAS)(Case study: Hara Biosphere reserve)

Author(s)

MAFI GHOLAMI DAVOOD | BAHARLOUII MASOUMEH | MAHMOUDI BEYTOLLAH | Issue Writer Certificate

Keywords

Erosion and accretionQ1

Rate of changes of mangroves boundariesQ1

Satellite imagesQ2

Abstract

Loss of a wide range of goods and services provided by ecosystems as well as unsustainability of mangrove-dependent human communities are direct results of the destruction and loss of mangrove ecosystems. This shows the importance of presenting effective planning and management strategies for the conservation and restoration of mangrove ecosystems that it has become one of the main objectives of the decision makers and managers of natural resources. One way to achieve the above objective is to assess mangrove shoreline changes over time, which can be used as one of the best indicators to assess responsiveness of mangroves to sediment morphological and dynamic changes of coastal areas and to assess the vulnerability of these ecosystems to climatic stresses. This shows the importance of assessing the rate of progression and regression and/or erosion and sedimentation to assist the planning and implementing the conservation actions and restoration of mangrove forests of the country. Therefore, the aim of this study is to monitor mangrove's shorelines changes for a period of 30 years in Hara biosphere reserve in Hormozgan province, Iran. The Landsat images of the years 1986, 2000 and 2016 were used to analyze the rate of progression and regression of mangrove forests of Khamir site during a period of 30 years. Since cloud cover reduces the image quality and causes error in detecting phenomena of the images, thus, by examining a large number of images in the archive of Landsat satellite, images without cloud cover were used. Also to determine the exact boundaries of the Mangrove forests, those images in which the sea level was at low tide, were used in this study. Geometric correction was the first step for image analysis. Although Landsat C images are characterized by good geometric precision, in order to achieve maximum possible accuracy, and recorded a total of 128 ground control points using GPS that has a good distribution of the surface area, Were also detected in the image, Landsat C images of 2014 with a root mean square error lower than one pixel (in this study, RMS = 0. 143) were georeferenced with the use of IDRISI software, as well as by recording totally 128 ground control points with a good distribution over the region. Finally, the corrected images of Landsat C were used for geometric correction of Landsat TM images in 1986, 1998 and also the image of Landsat MSS in 1973. RMS value of no one of the Landsat TM and MSS images in any of corrections was higher than 0. 18. All images were geo-referenced to UTMWGS-1984 Zone 40N projection and datum. In general, according to the resolution of the images used, the closed-canopy edge of the boundary of the Mangrove forests was considered as off-shore (marine) boundary and single trees and seedlings in the area beyond the edge were excluded from the analysis of site boundaries. In order to separate mangroves from surrounded water and land in coastal areas and to draw the final borders of the sites, NDVI vegetation index was used. After preparing NDVI and to achieve maximum accuracy in determining the boundaries of mangroves, the off-shore (marine) border of Mangroves was manually digitized using precise visual interpretation on a scale of 1: 10, 000 and by help of expertise of the team leading the project. The off-shore border of the mangrove sites were identified in images of 1973, 1986, 1998 and 2014. Finally, the accuracy of digitalized boundaries were evaluated and approved by putting them on Landsat images. Ground validation was performed in 2012, 2013 and 2016. Accordingly, a total number of 620 ground control points determining the off-shore boundaries of Mangrove sites were entered into the GIS and then were compared to boundaries extracted from the images. Also, the location of villages and human settlements in the vicinity of Mangroves was recorded. All recorded points were entered into ArcGIS 10 software for analysis. Also, Social surveys were conducted by performing face to face interviews with families living in villages adjacent to mangroves and experts from the Department of Natural Resources of the province. Given that the purpose of the interview was to achieve the respondent views on how the position and size of Mangroves have been changed over time, so those people were interviewed who have the highest history of residence (residence time more than 30 years) in the area. Accordingly, 25 people aged from 50 to 65 years were interviewed face to face. Views recorded were used to analyze the results. As stated above, determining the progression and regression rate of mangroves is based on measuring changes of mangrove boundary position relative to a baseline over time, and transects depicted perpendicular to baselines show these changes over time. In this study, for the selected sites, a number of 1684 transects with a distance of 30 meters from each other, were drawn using DSAS software. In this study, by considering the general direction of each site, and also using the mapped buffer for Mangrove boundaries in images of 1998, applied baseline were drawn manually and transects were drawn perpendicular to the baseline. Overall, calculation of the rate of erosion and sedimentation, or the progression and regression can be done using various statistical methods including end point rate (EPR), average of rates (AOR), minimum description length (MDL), by jackknifing (JK), linear regression rate (LRR), reweighted weighted least squares (WLS) least absolute deviation (LAD) and weighted least absolute deviation (WLAD). Among these methods, LRR statistical method has had the highest usage because of assessing changes in coastlines and border of ecosystems at different times (more than 2 periods). In this method, the average rates of progression and regression of mangroves are estimated using the position of mangroves’ border-lines and the baseline and fitting the regression line of least squares relative to the position of the border-lines. Analysis of changes rate of progression and regression showed that of 1684 drawn transects for analyzing the rate change of mangrove boundaries in coastal part of habitat, a number of 875 transects had negative LRR values and 809 transects represented positive LRR values. The highest number of transects with negative IRR values occurred in the western part of the habitat (Khamir site) (53% of the negative transects) so that the mean change rate of mangroves’ boundaries at these sites was equal to 0. 26 myr-1. In an eastward move and by approaching to the Kal river estuary, Mardo habitat witch was located in Mardo Island, showed an average rate of boundaries changes 0. 74 myr-1 or progression towards the sea. Based on these results, the average value of boundaries changes in the coastal part of habitat was equal to 0. 50 myr-1. According to obtained results, from 2571 drawn transects for analyzing the rate change of mangroves’ boundaries in Island part of habitat, a number of 1734 transects (67% of the negative transects) had negative LRR values and 837 transects represented positive LRR values. The average value of boundaries changes in the Island part of habitat was equal to 0. 73 myr-1. This shows progression or sedimentation in mangroves. The results also showed that the minimum and maximum value of LRR was equal to-12. 77myr-1 and 12. 98myr-1, respectively. The obtained results showed that the progression or sedimentation rate in the island part of Hara biosphere was more than coastal part of this habitat. As well, the average value of mangroves’ boundaries changes for Hara biosphere reserve was equal to 0. 62myr-1 which indicates the dominance of sedimentation process over erosion in this habitat.

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APA: Copy

MAFI GHOLAMI, DAVOOD, BAHARLOUII, MASOUMEH, & MAHMOUDI, BEYTOLLAH. (2018). Erosion and accretion monitoring in mangrove forests using remote sensing and Digital Shoreline Analysis System (DSAS)(Case study: Hara Biosphere reserve). JOURNAL OF ENVIRONMENTAL STUDIES, 43(4 ), 633-646. SID. https://sid.ir/paper/2978/en

Vancouver: Copy

MAFI GHOLAMI DAVOOD, BAHARLOUII MASOUMEH, MAHMOUDI BEYTOLLAH. Erosion and accretion monitoring in mangrove forests using remote sensing and Digital Shoreline Analysis System (DSAS)(Case study: Hara Biosphere reserve). JOURNAL OF ENVIRONMENTAL STUDIES[Internet]. 2018;43(4 ):633-646. Available from: https://sid.ir/paper/2978/en

IEEE: Copy

DAVOOD MAFI GHOLAMI, MASOUMEH BAHARLOUII, and BEYTOLLAH MAHMOUDI, “Erosion and accretion monitoring in mangrove forests using remote sensing and Digital Shoreline Analysis System (DSAS)(Case study: Hara Biosphere reserve),” JOURNAL OF ENVIRONMENTAL STUDIES, vol. 43, no. 4 , pp. 633–646, 2018, [Online]. Available: https://sid.ir/paper/2978/en

Scientific Information Database

ISSN: 2588-4824

Journal Paper

Erosion and accretion monitoring in mangrove forests using remote sensing and Digital Shoreline Analysis System (DSAS)(Case study: Hara Biosphere reserve)