Impact of land cover changes on land surface temperature and human thermal comfort in Dhaka City of Bangladesh

Urbanization leads to the construction of various urban infrastructures in the city area for residency, transportation, industry, and other purposes, which causes major land use change. Consequently, it substantially affects Land Surface Temperature (LST) by unbalancing the surface energy budget. Higher LST in city areas decreases human thermal comfort for the city dwellers and affects the urban environment and ecosystem. Therefore, a comprehensive investigation is needed to evaluate the impact of land use change on the LST. Remote Sensing (RS) and Geographic Information System (GIS) techniques were used for the detailed investigation. RS data for the years 1993, 2007 and 2020 during summer (March–May) in Dhaka city were used to prepare land cover maps, analyze LST, generate hazard maps and relate the land cover change with LST by using GIS. The results show that the built-up area in Dhaka city increased by 67% from 1993 to 2020 by replacing lowland mainly, followed by vegetation, bare soil and water bodies. LSTs found in the study area were ranged from 23.26 to 39.94 °C, 23.69 to 43.35 °C and 24.44 to 44.58 °C for the years 1993, 2007 and 2020, respectively. The increases of spatially distributed maximum and mean LST were found 4.62 °C and 6.43 °C, respectively, for the study period of 27 years while the change in minimum LST was not substantial. LST increased by around 0.24 °C per year and human thermal discomfort shifted from moderate to strong heat stress for the total study period due to the increase of built-up and bare lands. This study also shows that normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were negatively correlated with LST while normalized difference built-up Index (NDBI) and normalized difference built-up Index (NDBAI) were positively correlated with LST. The methodology developed in this study can be adapted to other cities around the globe

The role of earth observation in an integrated deprived area mapping “system” for low-to-middle income countries

Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups

Resource assessment of deciduous forests in Bangladesh

This research makes a new assessment of both the physical and social dimensions of deciduous forest resources located in the central part of Bangladesh. Satellite remote sensing data and techniques are used to detect spatial and temporal forest change, to measure forest biophysical variables and to appraise their potential for developing model predictions based on a field survey conducted in 2003. Post classification assessment and regression analysis were the main methods in remote sensing data analysis. The study focused on a part of deciduous forest (64 sq km) located in Madhupur thana for fine-scale forest assessment. Remote sensing results suggest that only 16 percent forest left in the study area compared to 3826 hectares in 1962. The forest biophysical variables show strong association with spectral information of satellite data. For instance, an R-squared of 0.79 for predicted variable (for tree height) was achieved while regressing with field data, indicating that remote sensing methods can be efficiently used even in the tropical forests where heterogeneity is common. The second part of the thesis focuses on the underlying social factors/drivers that impacted on the forest, ranging from social dynamics such as land tenancy disputes,historical legacies and local corruption to policy failure by employing the theoretical framework of political ecology. Political ecological analysis in this research helped to evaluate the role and inter-relations of power, the ideological dilemmas and methodological disputes (i.e. the way forest problems are perceived) over forest resources in the study area. Field survey and observation was also found useful in gathering information about social variables by interviewing local inhabitants, forest officials, NGO activists, and politicians. The research employs methodologies from both science (i.e. remote sensing) and social science (i.e. political ecology) and the findings suggest that these two strands can work together for the better management (including resource assessment, monitoring and progress evaluation) of resources in Bangladesh

Detection, monitoring and management of small water bodies:: A case study of Shahjadpur Thana, Sirajgonj district, Bangladesh

Bangladesh is a low-lying flood prone deltaic plain. Excavations are needed to create raised land for safe flood-free homesteads and water bodies for irrigation, and these result in the creation of doba, pukur, dighi and jola. All of these types of small water bodies are almost equally distributed all over the country, except for the heel, which is a natural, saucer shaped depression. For every eight people there is approximately an acre of small water bodies, which range in size from 25-400 sq.m. (doba), 150-1000 sq.m. (pukur), >750 sq.m. (dighi), >2000 sq.m. (jola) and >1000 sq.m. (heel). These small water bodies are commonly used for drinking, bathing and washing, fisheries and aquaculture, duck raising, irrigation, cattle feeding and washing. Despite the importance of small water bodies to the local economy there is no up to date inventory. For this purpose, in my research I have employed integrated participatory remote sensing, GIS and socio-cultural approaches. Although these have not been used before in Bangladesh, 1 argue that they are ideal for effective resource management and sustainable development planning. This research investigated the historical development of the present spatial distribution and use patterns of SWB using Remote Sensing and GIS. This was at a regional scale in four mouzas of Shahjadpur Thana. The data sources were topographical maps, aerial photographs, satellite images, agricultural census data, in-depth questionnaire, focus group meetings and interviewing key informants. An integrated RS-GIS and social sciences methodology was employed to produce maps of change and overlays of the socio-cultural factors involved. Results show that the doba, pukur and dighi, when these are not obstructed by surrounding vegetation, can be detected easily in high resolution panchromatic CORONA satellite photography, IRS-ID Panchromatic image and aerial photography. Comparatively large pukurs, dighis and all jo las and heels are detected in all other optical sensors and the SIR-C radar imagery. Multi-temporal images are helpful for identifying the different types of small water bodies as well separating those from other seasonal large water bodies and flooded areas. It is hoped that the proposed computer assisted participatory management system, including some locally specific guidelines, may be applicable for the planning of other thanas (total 490) in Bangladesh. The proposed management system will facilitate the integration of local planning with the national level planning process, which has not been possible hitherto

Spatiotemporal variation in land use land cover in the response to local climate change using multispectral remote sensing data

Climate change is likely to have serious social, economic, and environmental impacts on farmers whose subsistence depends on nature. Land Use Land Cover (LULC) changes were examined as a significant tool for assessing changes at diverse temporal and spatial scales. Normalized Difference Vegetation Index (NDVI) has the potential ability to signify the vegetation structures of various eco-regions and provide valuable information as a remote sensing tool in studying vegetation phenology cycles. In this study, we used remote sensing and Geographical Information System (GIS) techniques with Maximum Likelihood Classification (MLC) to identify the LULC changes for 40 years in the Sahiwal District. Later, we conducted 120 questionnaires administered to local farmers which were used to correlate climate changes with NDVI. The LULC maps were prepared using MLC and training sites for the years 1981, 2001, and 2021. Regression analysis (R2) was performed to identify the relationship between temperature and vegetation cover (NDVI) in the study area. Results indicate that the build-up area was increased from 7203.76 ha (2.25%) to 31,081.3 ha (9.70%), while the vegetation area decreased by 14,427.1 ha (4.5%) from 1981 to 2021 in Sahiwal District. The mean NDVI values showed that overall NDVI values decreased from 0.24 to 0.20 from 1981 to 2021. Almost 78% of farmers stated that the climate has been changing during the last few years, 72% of farmers stated that climate change had affected agriculture, and 53% of farmers thought that rainfall intensity had also decreased. The R2 tendency showed that temperature and NDVI were negatively connected to each other. This study will integrate and apply the best and most suitable methods, tools, and approaches for equitable local adaptation and governance of agricultural systems in changing climate conditions. Therefore, this research outcome will also meaningfully help policymakers and urban planners for sustainable LULC management and strategies at the local level

Implementing an object-based multi-index protocol for mapping surface glacier facies from Chandra-Bhaga basin, Himalaya

Surface glacier facies are superficial expressions of a glacier that are distinguishable based on differing spectral and structural characteristics according to their age and inter-mixed impurities. Increasing bodies of literature suggest that the varying properties of surface glacier facies differentially influence the melt of the glacier, thus affecting the mass balance. Incorporating these variations into distributed mass balance modelling can improve the perceived accuracy of these models. However, detecting and subsequently mapping these facies with a high degree of accuracy is a necessary precursor to such complex modelling. The variations in the reflectance spectra of various glacier facies permit multiband imagery to exploit band ratios for their effective extraction. However, coarse and medium spatial resolution multispectral imagery can delimit the efficacy of band ratioing by muddling the minor spatial and spectral variations of a glacier. Very high-resolution imagery, on the other hand, creates distortions in the conventionally obtained information extracted through pixel-based classification. Therefore, robust and adaptable methods coupled with higher resolution data products are necessary to effectively map glacier facies. This study endeavours to identify and isolate glacier facies on two unnamed glaciers in the Chandra-Bhaga basin, Himalayas, using an established object-based multi-index protocol. Exploiting the very high resolution offered by WorldView-2 and its eight spectral bands, this study implements customized spectral index ratios via an object-based environment. Pixel-based supervised classification is also performed using three popular classifiers to comparatively gauge the classification accuracies. The object-based multi-index protocol delivered the highest overall accuracy of 86.67%. The Minimum Distance classifier yielded the lowest overall accuracy of 62.50%, whereas, the Mahalanobis Distance and Maximum Likelihood classifiers yielded overall accuracies of 77.50% and 70.84% respectively. The results outline the superiority of the object-based method for extraction of glacier facies. Forthcoming studies must refine the indices and test their applicability in wide ranging scenarios