This research work dealt with the development of an operational methodology with appropriate technical components for monitoring and forecasting of rice crop (Boro) production in Bangladesh. Designed system explores integrated application of remote sensing (RS) sciences and Geographic Information System (GIS) technology. Terra MODIS 16-day Normalized Difference Vegetation Index (NDVI) maximum value composite (MVC) image product MOD13A1 of 500 m spatial resolution covering Bangladesh have been utilized for a period 2011–2017. Hence the district-wise sum of NDVI on pixel-by-pixel has been calculated from Jan–April during 2011–2017. Regression analysis between district-based pixel-wise summation of MODIS-NDVI and district-wise BBS (Bangladesh Bureau of Statistics) estimated Boro production revealed strong correlation (R² = 0.57–0.85) where in March most of the regression coefficient shows significant correlation due to maximize photosynthetic activities. Therefore, the highest regression coefficient value from derived set of coefficient value (BCP-Boro Crop Production Model 2) has been utilized to obtain year-wise rice productions for all the years (2011–2017). Global Positioning System (GPS)-based field verification, accuracy assessment and validation operation have been carried out at randomly selected geographical positions over the country using various statistical tools. The results demonstrate good agreement between estimated and predicted yearly Boro rice production during 2011–2017 time period with Mean Bias Error (MBE) = −29,881 to 19,431 M.Ton; Root Mean Square Error (RMSE) = 5238 to 11,852 M.Ton; Model Efficiency (ME) = (0.86–0.94); Correlation Coefficients = 0.65 to 0.87. Therefore MODIS-NDVI based regression model seems to be effective for Boro production forecasting. The system generally appears to be relatively fast, simple, reasonably accurate and suitable for nation-wide crop statistics generation and food security issues.

This research work studies the integrated application of satellite Remote Sensing (RS) and Geographic Information System (GIS) for the monitoring and forecasting of rice crop (Aman) production in Bangladesh. Normalized Difference Vegetation Index (NDVI) images of Terra MODIS products MOD13A1 (h25v06 and h26v06) with 500 m spatial resolution, composed using Maximum Value Composite (MVC) techniques, were used to cover Bangladesh for the period of 2011–2017. Country scale NDVI (district-wise summation) was calculated pixel-by-pixel to draw a regression curve while using Bangladesh Bureau of Statistics (BBS) estimations of Aman production for the months of September–November. The regression study of district-wise pixel-based summation of MODIS-NDVI and ground-based BBS-estimated Aman production shows a strong correlation (R² = 0.54–0.78); for the months of September and October, most of the regression coefficient indicates significant correlation due to maximum photosynthetic activities. Therefore, based on the highest regression coefficient value of September and October, Aman Crop Production (ACP) models were developed and the ACP Model-2 was exploited (from the derived set of coefficient values) to acquire year-wise rice production for all the years (2011–2017). The simulated ACP Model-2 demonstrates good agreement between the estimated and predicted yearly Aman rice production for the 2011–2017 time period with Mean Bias Error (MBE) = (−9435 to 23,156) M.Ton; Root Mean Square Error (RMSE) = 253–4426 M.Ton; Model Efficiency (ME) = (0.89–0.93); and, Correlation Coefficients = (0.72–0.94). Hence, the MODIS–NDVI-based regression model seems to be effective for Aman crop production forecasting in the context of food security issues in Bangladesh. The applied system is simple, rationally accurate, and fit for the generation of nationwide crop statistics.

The research results in the last two decades show the important role of pekarangan as a micro-scale landscape unit in providing space for growing various types of plants, i.e., multi-layers from the grasses to the trees, livestock, and fish. Pekarangan is defined with distinctive spatial patterns and elements as a representation of the harmonious relationship between the owner and the ecological character. Perceptions and preferences based on different social, cultural, and environmental factors shape the pekarangan pattern and function to become more diverse as a form of community local wisdom. The important role of pekarangan needs to be deeper elaborated in efforts to increase community immunity during the Covid-19 pandemic. Pekarangan has great potential to be developed as a mitigation and rehabilitation space by providing a variety of medicinal plants as well as outdoor activity spaces with a healthy environment quality. This research was designed for three years with three main approaches: (1) inductive method, which focuses on building theory from practices that have been known and carried out for generations, (2) implementation as an effort to actualize local knowledge, (3) multiplicative for the benefit of the important value of pekarangan. Studies start from basic research of pekarangan; research on the development of spatial planning and elements forming pekarangan; research on nutritional content contributed from pekarangan products. Data were obtained through pekarangan mapping, structured questionnaires at four selected locations in Cisadane, Citarum, Kali Progo, and Brantas Watersheds. The results of this research are expected to be implemented in public spaces on a meso scale and macro scales.

This study aims to set up a comprehensive approach to the Vulnerability and Impact Assessment (VIA) of river erosion and to suggest Ecosystem-based Adaptation (EbA) practices. Based on the analysis of vulnerability using the Driver-Pressure-State-Impact-Response (DPSIR) framework, this paper discusses some of the significant climatic (rainfall pattern, temperature, seasonal drift, cold wave and heat wave) and non-climatic (river erosion, repetitive death of field crops and agrochemicals) forces in the Kazipur Upazila (Sirajganj District)—a river erosion-prone area of Bangladesh. Both primary (Key Informants Interview, Household Survey, and Focus Group Discussion) and secondary (climatic, literature review) data have been used in revealing the scenario of climatic stress. The analysis revealed a slightly increasing trend of mean annual temperature, and a decreasing trend of total annual rainfall from 1981 to 2015, which have been supported by people’s perception. This study found that river erosion, the increase of temperature and the late arrival of monsoon rain, excessive monsoon rainfall, high use of agrochemicals, and flow alterations are major drivers in the riverine ecosystem. These drivers are creating pressures on agricultural land, soil fertility, water availability and livelihood patterns of affected communities. Hence, floating bed cultivation, integrated pest management, use of cover crops, reforestation, the introduction of an agro-weather forecasting system, and a new variety of flood tolerant species have been suggested as potential EbA to cope with river bank erosion and to increase the capacity of the affected ecosystem.

The changes in the Land Use Land Cover (LULC), particularly the industrialization and urbanization, significantly impact the Land Surface Temperature (LST). Therefore, this study investigates the impact of urban expansion on LST in an industrial zone of Gazipur, Bangladesh, and analyzes the relationship between LULC changes and land surface emissivity over the period of 1995 to 2024, utilizing the satellite imageries and Machine Learning (ML)-based remote sensing techniques in Google Earth Engine (GEE). The study’s results revealed significant urbanization, accompanied by a sharp increase in land surface temperature. The built-up area increased more than fourfold (4.4-fold), expanding from 60.46 km² (3.33%) in 1995 to 266.36 km² (14.66%) in 2024, while overall accuracy (OA) ranged from 0.92 to 0.98, and the kappa Coefficient ranged from 0.88 to 0.97. Changes in LULC patterns, particularly the expansion of urban areas, have estimated a mean LST rise of about 1.64 °C in summer and 2.85 °C in winter over the 30 years (1995–2024), with the highest values associated with industrialized zones, particularly in Gazipur Sadar, which has faced significant challenges in sustainable urbanization. Although the estimated LST was validated using available ground-based and MODIS-derived temperature data, uncertainty may remain due to emissivity estimation and sensor calibration. The findings from this study highlight the need for sustainable urban planning and land management practices to minimize the effects of increased LST in industrialized regions and similar areas elsewhere. In addition, this study showcases the potentiality of GEE-based semi-automatic LST monitoring techniques at a large scale.