Mapping Long Term Changes in Mangrove Cover and Predictions of Future Change under Different Climate Change Scenarios in the Sundarbans, Bangladesh

Ground-based readings of temperature and rainfall, satellite imagery, aerial photographs, ground verification data and Digital Elevation Model (DEM) were used in this study. Ground-based meteorological information was obtained from Bangladesh Meteorological Department (BMD) for the period 1977 to 2015 and was used to determine the trends of rainfall and temperature in this thesis. Satellite images obtained from the US Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) website (www.glovis.usgs.gov) in four time periods were analysed to assess the dynamics of mangrove population at species level. Remote sensing techniques, as a solution to lack of spatial data at a relevant scale and difficulty in accessing the mangroves for field survey and also as an alternative to the traditional methods were used in monitoring of the changes in mangrove species composition, . To identify mangrove forests, a number of satellite sensors have been used, including Landsat TM/ETM/OLI, SPOT, CBERS, SIR, ASTER, and IKONOS and Quick Bird. The use of conventional medium-resolution remote sensor data (e.g., Landsat TM, ASTER, SPOT) in the identification of different mangrove species remains a challenging task. In many developing countries, the high cost of acquiring high- resolution satellite imagery excludes its routine use. The free availability of archived images enables the development of useful techniques in its use and therefor Landsat imagery were used in this study for mangrove species classification. Satellite imagery used in this study includes: Landsat Multispectral Scanner (MSS) of 57 m resolution acquired on 1st February 1977, Landsat Thematic Mapper (TM) of 28.5 m resolution acquired on 5th February 1989, Landsat Enhanced Thematic Mapper (ETM+) of 28.5 m resolution acquired on 28th February 2000 and Landsat Operational Land Imager (OLI) of 30 m resolution acquired on 4th February 2015. To study tidal channel dynamics of the study area, aerial photographs from 1974 and 2011, and a satellite image from 2017 were used. Satellite images from 1974 with good spatial resolution of the area were not available, and therefore aerial photographs of comparatively high and fine resolution were considered adequate to obtain information on tidal channel dynamics. Although high-resolution satellite imagery was available for 2011, aerial photographs were used for this study due to their effectiveness in terms of cost and also ease of comparison with the 1974 photographs. The aerial photographs were sourced from the Survey of Bangladesh (SOB). The Sentinel-2 satellite image from 2017 was downloaded from the European Space Agency (ESA) website (https://scihub.copernicus.eu/). In this research, elevation data acts as the main parameter in the determination of the sea level rise (SLR) impacts on the spatial distribution of the future mangrove species of the Bangladesh Sundarbans. High resolution elevation data is essential for this kind of research where every centimeter counts due to the low-lying characteristics of the study area. The high resolution (less than 1m vertical error) DEM data used in this study was obtained from Water Resources Planning Organization (WRPO), Bangladesh. The elevation information used to construct the DEM was originally collected by a Finnish consulting firm known as FINNMAP in 1991 for the Bangladesh government.本研究采用地面气温与降水观测数据、卫星影像、航空摄影照片、地面验证数据以及数字高程模型（Digital Elevation Model, DEM）。1977年至2015年的地面气象信息源自孟加拉国气象局（Bangladesh Meteorological Department, BMD），用于本文分析降水与气温的变化趋势。从美国地质调查局（US Geological Survey, USGS）地球资源观测与科学中心（Center for Earth Resources Observation and Science, EROS）官网（www.glovis.usgs.gov）获取的四个时段的卫星影像，被用于解析红树林种群的物种级动态变化。遥感技术可解决相关尺度下空间数据缺失、野外调查难以抵达红树林区域的痛点，同时可作为传统监测方法的替代方案，用于监测红树林物种组成的变化。为识别红树林，研究使用了多款卫星传感器，包括Landsat TM/ETM/OLI、SPOT、CBERS、SIR、ASTER、IKONOS以及Quick Bird。但利用常规中分辨率遥感传感器数据（如Landsat TM、ASTER、SPOT）识别不同红树林物种仍具挑战性。在诸多发展中国家，高分辨率卫星影像的获取成本高昂，限制了其常规应用。而存档影像的免费可用性推动了相关应用技术的发展，因此本研究采用Landsat影像开展红树林物种分类工作。本研究使用的卫星影像包括：1977年2月1日获取的分辨率为57米的Landsat多光谱扫描仪（Multispectral Scanner, MSS）数据、1989年2月5日获取的分辨率为28.5米的Landsat专题制图仪（Thematic Mapper, TM）数据、2000年2月28日获取的分辨率为28.5米的Landsat增强型专题制图仪（Enhanced Thematic Mapper, ETM+）数据，以及2015年2月4日获取的分辨率为30米的Landsat陆地成像仪（Operational Land Imager, OLI）数据。为探究本研究区的潮汐通道动态，本研究使用了1974年与2011年的航空摄影照片，以及2017年的卫星影像。由于1974年该区域的高空间分辨率卫星影像无法获取，因此采用分辨率相对较高且精细的航空摄影照片来获取潮汐通道动态信息。尽管2011年已有高分辨率卫星影像可用，但考虑到成本效益以及便于与1974年航空照片进行对比，本研究仍采用了航空摄影照片。这些航空摄影照片源自孟加拉国测量局（Survey of Bangladesh, SOB）。2017年的Sentinel-2卫星影像则从欧洲空间局（European Space Agency, ESA）官网（https://scihub.copernicus.eu/）下载获取。本研究中，高程数据是确定海平面上升（Sea Level Rise, SLR）对孟加拉孙德尔本斯未来红树林物种空间分布影响的核心参数。由于研究区地势低洼，每厘米高程变化都会对研究结果产生显著影响，因此此类研究亟需高分辨率高程数据。本研究使用的垂直误差小于1米的高分辨率DEM数据，源自孟加拉国水资源规划组织（Water Resources Planning Organization, WRPO）。该DEM数据所用的高程信息，最初由芬兰咨询公司FINNMAP于1991年为孟加拉国政府采集。