Land use change (LUC) driven by human activities and natural factors has resulted in the global loss of native biodiversity and the alteration of ecological processes and services across different ecosystems. It is thus necessary to analyze the trends and driving factors that influence land use changes. In this study, moderate resolution Landsat images were freely downloaded from the United States Geological Survey (USGS) archives, analyzed using the random forest (RF) algorithm and mapped in ArcGIS 10.2 software to examine the LUC trends from 1990 to 2016 in the Kilombero valley floodplain (KVFP), Tanzania. Participatory rural appraisals (PRA) and household questionnaire surveys were used to assess the potential drivers of LUC. The results show that, from 1990 to 2016, the agricultural land and grassland increased by 11.3% and 13.3%, respectively, while the floodplain wetland area decreased from 4.6% in 1990 to 0.9% in 2016. Based on a questionnaire survey, the intensification of human activities was identified as the proximate driver while population growth, a growing market demand and price incentives for agricultural and forest products coupled with improved infrastructure and biophysical factors such as soil properties, climate variability and terrain characteristics were identified as the underlying drivers of LUC. However, there is interplay among these factors acting simultaneously as well as differently that influence land use changes. Based on these findings, future sustainable land management strategies should include the introduction of the alternative environmentally friendly sources of livelihood, such as beekeeping, the promotion of community participation and education on the importance of sustainable wetland management.

Land use/cover change (LUCC) attributed to natural factors and human activities has led to the loss of ecosystem services, making the quantitative valuation of ecosystem services the scientific focal pointfor sustainable development. This study assessed changes in the ecosystem services values (ESVs) due to LUCC during the period 1990–2016 in the Kilombero Valley floodplain, located in southeastern Tanzania. Moderate resolution Landsat images from 1990, 2010 and 2016 were obtained and analyzed using a random forest (RF) algorithm for classification, and ArcGIS Desktop software (version 10.2, Esri, Redlands, CA, USA) for mapping to assess the LUCC. The ESVs were estimated based on the benefit transfer approach using adopted global value coefficients and modified local value coefficients. The results revealed that the aggregated ESVs of the forests, bushlands, wetlands, and water had decreased, consequently leading to a total loss of US$ 811.5 million (26.6%) in ESVs over the past 26 years when calculated with the modified local value coefficients to US$ 3000.7 million (42.3%) when calculated with global value coefficients. Moreover, the loss in the ESV was attributed to the decreased values of water regulation, climate regulation, erosion control, nutrient cyclying, habitat/refugia, and water supply, with the exception of the values of food production and biological control, which gradually increased during the study period. This study provided minimum estimates of the ecosystem service values, which willcontribute to the formulation of policy actions and strategies for sustainable management of the Kilombero Valley floodplain and inform various stakeholders on the tradeoffs involved in the use of land resources.

Drought is a devastating recurring climate hazard, with enormous impacts on livelihoods and attainment of sustainable development goals (SDGs) in semi-arid areas. The current study was conducted to evaluate households’ livelihood vulnerability to droughts in an African semi-arid region. A total of 384 households from a drought-prone region in Northern Cameroon were randomly sampled and interviewed using a structured questionnaire. Livelihood vulnerability was evaluated from the collected households’ data using the Livelihood Vulnerability Index (LVI) and Intergovernmental Panel on Climate Change Vulnerability Index (IPCC-VI) methods. The LVI results showed that food insecurity, livelihood strategy and social networking were the major drivers of livelihood vulnerability. This was because almost all of the interviewed households relied on rain-fed agriculture and there was limited early drought warning information, which limited the achievements of some SDGs such as zero hunger, water accessibility and poverty alleviation. Adoption of drought-resilient and early maturing crops together with the use of agriculture extension officers to disseminate drought warnings are advised to reduce the effects of the livelihood vulnerability drivers and ensure sustainable development. The IPCC-VI showed that drought exposure and adaptive capacity were low while drought sensitivity was high among the surveyed households. The high sensitivity was due to food and water insecurity, while low adaptive capacity was due to a lack of formal education and a high dependency rate. Heavy investment in education and reducing the number of dependents through family planning and campaigning against polygamy could enhance households’ adaptive capacity. It was concluded that households’ livelihood vulnerability in the study area was due to high sensitivity and low adaptive capacity to drought. Drought reversed positive steps that had been taken to achieve various SDGs; however, heavy investment in other SDGs such as quality education and health could enhance households’ adaptive capacity and system sustainability in African semi-arid regions.

Crop survival and growth requires identification of correlations between appropriate suitable planting season and relevant climatic and environmental characteristics. Climatic and environmental conditions may cause water and heat stress at critical stages of crop development and thus affecting planting suitability. Consequently, this may affect crop yield and productivity. This study assesses the influence of climate and environmental variables on rain-fed sunflower planting season suitability in Tanzania. Data on rainfall, temperature, slope, elevation, soil and land use/or cover were accessed from publicly available sources using Google Earth Engine. This is a cloud-based geospatial computing platform for remote sensed datasets. Tanzania sunflower production calendar of 2022 was adopted to mark the start and end limits of planting across the country. The default climate and environmental parameters from FAO database were used. In addition, Pearson correlation was used to evaluate the relationship between rainfall, temperature over Normalized Difference Vegetation Index (NDVI) from 2000 to 2020 at five-year interval for January-April and June–September, for high and poor suitability season. The results showed that planting suitability of sunflower in Tanzania is driven more by rainfall than temperature. It was revealed that intra-annual planting suitability increases gradually from short to long- rain season and diminishes towards dry season of the year. January-April planting season window showing highest suitability (41.65%), whereas June–September indicating lowest suitability (0.05%). Though, not statistically significant, rainfall and NDVI were positively correlated with r = 0.65 and 0.75 whereas negative correlation existed between temperature and NDVI with r = -− 0.6 and − 0.77. We recommend sunflower subsector interventions that consider appropriate intra-regional and seasonal diversity as an important adaptive mechanism to ensure high sunflower yields.

Land use and land cover dynamics are pivotal to communicating land surface temperature (LST) scenarios. This study characterises the influence of biophysical variables on LSTs in the Dar es Salaam Metropolitan City (DMC). Landsat images were analysed using geographically weighted regression (GWR) and ordinary least square (OLS) models to determine biophysical variables (soil adjusted vegetation index, normalized difference built-up index, and normalised difference bareness index) and LST relationships. The GWR analysis resultsrevealed that LST had a weak to strong negative correlation with the soil adjusted vegetation index, a moderate positive correlation with normalized difference built-up index, and a low positive correlation with the normalised difference bareness index. GWR predicted LST better than OLS, with coefficient of determination -R² values of 55%, 80%, and 62% for 1995, 2009, and 2017, respectively. In addition, higher model residuals values were observed in high building density compared to low building density areas. This study provides a broad understanding of the biophysical variables’ impact on LST in DMC and provides reference for site-specific urban land-use planning and designing strategies for LST mitigation.