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Subsistence farmers in elephant rangelands suffer food insecurity, economic losses and emotional stress from crop damage by elephants, resulting in reduced local support toward elephant conservation. To foster human-elephant co-existence in a dynamic social-ecological system, we explored the validity of Classification Tree (CT) analysis application to identify human-elephant hotspots in an effort to target approaches to reduce human-elephant conflicts. The CT analysis revealed that the most important factors affecting occurrence of crop damage were crop types, field sizes and distance to nearest road and river. From CT generated thresholds, we propose establishment of multi-criteria-based risk zones, as an alternative approach, to quickly identify and prioritize future human-elephant conflict (HEC) hotspots for improved agrarian livelihood and elephant conservation outcomes. We also explained the limitations of CT models. This study contributes to the existing theory and body of work on elephant habitat use at the human-elephant interface. [ABSTRACT FROM AUTHOR]

Goal and Objectives: This paper examined the relations between informal documentations and land tenure security on change in land use/cover. Using Chamuka land registration, the paper focuses on caused of land use/cover change and how informal documents-based land dealings leads to tenure security and a more productive land use. Methodology: The study used various methods, including 345 households' interviews, group discussions and satellite images of 2010, 2015 and 2020, to understand how land ownership in Chamuka chiefdom effect on land use. Results: The study results shows that the introduction of Land Advisory Boards (LABs), Customary Certificate of Land Occupancy (CCLO) and the land register has scored several successes (i.e., increase in land rights, ownership, low cost of land registration, tenure security and reduction in land-related conflicts. Other improvements include the practice of conservation farming, i.e. agroforestry, fire management resulting to improved crop and livestock farming. Despite these positive results, the satellite images showed decline in vegetation and forest cover due to expansion of agricultural area, buildup and decrease in water bodies. This study provides new insights into how the evolution of the traditional land tenure system, as an integral part of resource governance, its effect on land use/cover. Next to suggesting is how to strengthen informal documents, and efficiency of local land management institutions in order to reduce loss of vegetation and deforestation. [ABSTRACT FROM AUTHOR]

Pollution of aquatic ecosystems is one of the major challenges affecting many countries of the world. Heavy metal pollutants, in particular, threaten the life of aquatic organisms (fauna and flora) and, more importantly, humans who consume aquatic products as a critical source of proteins. In the present study, the concentrations of selected heavy metals (cobalt- Co, Chromium-Cr, nickel- Ni and manganese- Mn) in Limnothrissa miodon, locally known as 'Kapenta' were assessed using the Microwave Plasma Atomic Emission Spectrometer (MP AES) 4200 at the Zambia Agricultural Research Institute (ZARI). The fish was collected from Lake Kariba, Zambia, which is divided into four fisheries management strata (I, II, III, and IV). The health risks to consumers were evaluated using the Estimated Daily Intake (EDI), Target Hazard Quotient (THQ) and Hazard Index (HI). Analysis of variance (ANOVA) was used to assess the difference in the means of heavy metal concentration across the four strata for each element. The concentrations of all the heavy metal elements were within the permissible limits considered to be safe for human consumption based on the Food and Agriculture Organization (FAO) standards. However, the concentration of individual heavy metal elements varied significantly across the strata with stratum I and II showing higher levels in general except for Mn which was highest in stratum II and III compared with the other strata. Furthermore, Mn concentration was the highest in all the strata and the highest concentration was observed in the fish from stratum II. The EDIs, THQs and HIs of each heavy metal element did not show any threat to consumers of the fish from the lake. Further studies are required to better understand the potential sources of heavy metals and to regularly monitor existing activities that may elevate the concentration levels. [ABSTRACT FROM AUTHOR]

Cyclones are some of the most devastating natural disasters that cause major losses of life and property. A few months after Cyclone Idai swept through Beira, Mozambique between 4 and 20 March 2019. An analysis is presented on the impacts of this disaster by assessing the extent of the floods. This study employed Sentinel-2 imagery and remote sensing techniques including a threshold analysis and an object‑based image analysis (OBIA) approach. Water and non-water land covers were established by using a Modified Normalized Difference Water Index (MNDWI), while the classification approach assessed three land covers. These assessments were done on four dates: (1) 31 January (before), (2) 25 February 20 (before), (3) 12 March (during) and (4) 16 April (after). Accuracies ranging from 74% to 90% were attained. The OBIA approach showed that 61% of the study area was affected by floods, while the threshold approach showed that 55% of the area was flooded. Areas with low elevation and close to the coast were more effected by the floods. After the cyclone, the area under water reduced by more than 20%. The results here could be useful for developing early warning systems, risk assessment, establishing damage and developing natural hazard policies. [ABSTRACT FROM AUTHOR]

Land cover classification of Landsat images is one of the most important applications developed from Earth observation satellites. The last four decades were marked by different developments in land cover classification methods of Landsat images. This paper reviews the developments in land cover classification methods for Landsat images from the 1970s to date and highlights key ways to optimize analysis of Landsat images in order to attain the desired results. This review suggests that the development of land cover classification methods grew alongside the launches of a new series of Landsat sensors and advancements in computer science. Most classification methods were initially developed in the 1970s and 1980s; however, many advancements in specific classifiers and algorithms have occurred in the last decade. The first methods of land cover classification to be applied to Landsat images were visual analyses in the early 1970s, followed by unsupervised and supervised pixel-based classification methods using maximum likelihood, K-means and Iterative Self-Organizing Data Analysis Technique (ISODAT) classifiers. After 1980, other methods such as sub-pixel, knowledge-based, contextual-based, object-based image analysis (OBIA) and hybrid approaches became common in land cover classification. Attaining the best classification results with Landsat images demands particular attention to the specifications of each classification method such as selecting the right training samples, choosing the appropriate segmentation scale for OBIA, pre-processing calibration, choosing the right classifier and using suitable Landsat images. All these classification methods applied on Landsat images have strengths and limitations. Most studies have reported the superior performance of OBIA on different landscapes such as agricultural areas, forests, urban settlements and wetlands; however, OBIA has challenges such as selecting the optimal segmentation scale, which can result in over or under segmentation, and the low spatial resolution of Landsat images. Other classification methods have the potential to produce accurate classification results when appropriate procedures are followed. More research is needed on the application of hybrid classifiers as they are considered more complex methods for land cover classification. [ABSTRACT FROM AUTHOR]

The study was undertaken to evaluate form factors forPinus kesiyagrowing in the three Zambia Forest and Forestry Industries Corporation (ZAFFICO) plantations – the Ndola, Ichimpe and Chati groups – under different rainfall (900–1 500 mm) and soil conditions (pH 4.0–5.5). Trees were sampled from the three plantation groups and 10 sample plots were established in which 10 trees were randomly sampled for cutting. Height and diameter at breast height (dbh) were the main variables that were measured and constituted the collected data. The trees were then felled and cut into segments of 0.5 m from the bottom to the top of the tree and segment diameters over bark were measured at both ends. The form factor for each tree was then calculated by dividing the sum of the volume of the tree segments by the estimated cylindrical volume of the standing tree. The results showed significant differences (p-value<0.05) in form factors among the three plantation groups. The study further showed a significant difference in form factor when the three plantation groups were compared with a fixed form factor (0.4) used by ZAFFICO plantations, indicating that the form factors are site specific. Therefore, for the benefit of customers and plantation owners, it is important that area-specific form factors should be used for each plantation group, as these groups are affected by different factors such as soils characteristics and rainfall, which have a direct effect on the form factor. [ABSTRACT FROM PUBLISHER]