Food security is one of the burning issues in the 21st century, as a tremendous population growth over recent decades has increased demand for food production systems. However, agricultural production is constrained by the limited availability of arable land resources, whereas a significant part of these is already degraded due to overexploitation. In order to get optimum output from the available land resources, it is of prime importance that crops are monitored, analyzed, and mapped at various stages of growth so that the areas having underdeveloped/unhealthy plants can be treated appropriately as and when required. This type of monitoring can be performed using ultra-high-resolution earth observation data like the images captured through unmanned aerial vehicles (UAVs)/drones. The objective of this research is to estimate and analyze the above-ground biomass (AGB) of the wheat crop using a consumer-grade red-green-blue (RGB) camera mounted on a drone. AGB and yield of wheat were estimated from linear regression models involving plant height obtained from crop surface models (CSMs) derived from the images captured by the drone-mounted camera. This study estimated plant height in an integrated setting of UAV-derived images with a Mid-Western Terai topographic setting (67 to 300 m amsl) of Nepal. Plant height estimated from the drone images had an error of 5% to 11.9% with respect to direct field measurement. While R² of 0.66 was found for AGB, that of 0.73 and 0.70 were found for spike and grain weights respectively. This statistical quality assurance contributes to crop yield estimation, and hence to develop efficient food security strategies using earth observation and geo-information.

Household smoke-exposure risk (SER) can be defined through the assessment of cooking fuels (smoke and no smoke-producing) and cooking places (indoor and outdoor) related information, which represent different levels of household air pollution. This study aimed to explore the prevalence and geographical variations in smoke-exposure risks (SERs) associated with indoor and outdoor cooking practices and use of smoke-producing and non-smoke-producing cooking fuels in Tanzania. We further investigated the social and spatial features associated with household SERs. We defined an indicator variable, the household SER, using country-level, cross-sectional data on cooking fuels and cooking places obtained from the 2015–2016 Tanzania Demographic and Health Survey and then estimated zone-level average SERs. We used an ordered logistic regression model to assess the social and spatial characteristics associated with household SER. We identified 76.4% of the 12,425 households that practiced indoor cooking using smoke-producing fuels as having a high SER. High-level SER was more prevalent in the Central, Southern highland, and Southwest highland zones. Overall, wealthier households, female-headed households, and households with higher education attainments were more likely to be categorized as households with very low SER. Meanwhile, households headed by older individuals and with larger family sizes were less likely to be in the very low SER category. The prevalence of high SER is a major public health concern in Tanzania. Improved cooking stoves and cleaner fuels should be adopted simultaneously to minimize the adverse effects associated with household SER.

Keywords: Mikania micrantha, Invasive Species, Chitwan National Park, Image Classification, Maxent Modeling Mikania micrantha is one of the major invasive alien plant species in tropical moist forest regions of Asia including Nepal. Recently, this weed is spreading at an alarming rate in Chitwan National Park (CNP) and threatening biodiversity. This paper aims to assess the impacts of Mikania micrantha on different land cover and to predict potential invasion sites in CNP using Maxent model. Primary data for this were presence point coordinates and perceived Mikania micrantha cover collected through systematic random sampling technique. Rapideye image, Shuttle Radar Topographic Mission data and bioclimatic variables were acquired as secondary data. Mikania micrantha distribution maps were prepared by overlaying the presence points on image classified by object based image analysis. The overall accuracy of classification was 90 % with Kappa coefficient 0.848. A table depicting the number of sample points in each land cover with respective Mikania micrantha coverage was extracted from the distribution maps to show the impact. The riverine forest was found to be the most affected land cover with 85.98 % presence points and sal forest was found to be very less affected with only 17.02 % presence points. Maxent modeling predicted the areas near the river valley as the potential invasion sites with statistically significant Area Under the Receiver Operating Curve (AUC) value of 0.969. Maximum temperature of warmest month and annual precipitation were identified as the predictor variables that contribute the most to Mikania micrantha's potential distribution.