This paper uncovers the importance of people’s place-values on sustainable forest management, and how such values can be incorporated into forest management actions and decision-making. Specifically, it focuses on mapping economic and cultural values on forest ecosystem services; assesses how non-materials and materials benefit from forest ecosystem cause landscape fragmentation; and how this information could assist in better forest planning and management. The data were collected from ten villages surrounding the Ngezi forest reserve in Pemba, Tanzania. Data were collected through participatory mapping, field observation, and focus group discussions. A map of place-values for each respondent was transferred from paper to digital format, digitized and coded using the GIS, and analysed using kernel density. Non-spatial data were processed and integrated into GIS-based spatial analysis. The results indicate that only 12 areas were identified as very high-valued and these require careful consideration for sustainable forest planning and management. About 4 out of 6 very high-valued areas for material services are found inside the reserve. The areas outside the reserve are undervalued and not utilized effectively for material services. Contrary to cultural services, only 1 out of 6 very high-valued places is located inside the reserve. Furthermore, economic situations, together with social driving forces, have been important determinants of forest values in the areas. Therefore, placevalues issues, particularly economic development outcomes, preservation of the aesthetics and improvement of recreational amenities should be considered when examining sustainable forest resource management.

Landslides are becoming increasingly widespread, claiming tens of thousands of fatalities, hundreds of thousands of injuries, and billions of dollars in economic losses each year. Thus, studies for geographically locating landslides vulnerable areas have been increasingly relevant in recent decades. This research is aimed at integrating Geographical Information Systems (GIS) and Remote Sensing (RS) techniques to delineate Landslide Susceptibility Mapping (LSM) of Lushoto District, Tanzania. RS assisted in providing remotely datasets including; Digital Elevation Models (DEM), Landsat 8 OLI imageries, and spatially distributed landslides coordinates with the use of a handheld Global Position System (GPS) receiver while various GIS analysis techniques were used in the preparation and analysis of landslides in uencing factors hence, generating LSM index values. However, rainfall, slope's angle, elevation, soil type, lithology, proximity to roads, rivers, faults, and Normalized Difference Vegetation Index (NDVI) factors were found to have direct in uence on the occurrence of landslides. These factors were evaluated, weighted, and ranked using Analytical Hierarchy Process (AHP) technique in which 0.086 (8.6%) consistency ratio (CR) was attained (highly accepted). Findings reveal that, rainfall (29.97%), slopes' angle (21.72%), elevation (15.68%), and soil types (11.77%) were found to have high in uence on the occurrence of landslides while proximity to faults (8.35%), lithology (4.94%), proximity to roads (3.41%), rivers (2.48%) and NDVI (1.69%) had very low in uences respectively. The overall results, obtained through Weighted Linear Combination (WLC) analysis indicate that, about 97669.65 hectares (ha) of the land is under very low landslides susceptibility levels which accounts for 24.03% of the total study area. Low susceptibility levels had 123105.84 ha (30.28%) moderate landslides susceptibility areas were found to have 140264.79 ha (34.50%) while high and very high susceptibility areas were found to cover about 45423.43 ha (11.17%) and 57.78 ha (0.01%) respectively. Furthermore, 81% overall model accuracy was obtained as computed from Area under the Curve (AUC) using Receiver Operating Characteristic (ROC) Curve.

This study sought to determine daily water consumption in the three zones of Sumbawanga Urban district. The study applied the geographical information system (GIS) to map and quantify non-revenue water (NRW), and tested the use of geospatial database management strategy to reduce NRW. The study used water distribution networks of zone A (Majengo ward), zone B (Katandala and Mazwi wards), and zone C (Chanji and Kizwite wards) that are served by the Sumbawanga Urban Water Supply Authority (SUWASA). The study collected water flow and water loss data by recording and mapping the existing water networks using a GPS device. The methodology for determining the quantity of NRW abstracted from the International Water Association (IWA), and maps made through QGIS, were used to show the degree of water loss by zones. A geo-database that assists in maintenance, budgeting, planning and procurement of water assets to reduce NRW was developed using postgress and postGIS, and connected to QGIS for visualization. Experimental results and computations indicate that around 8787.6m3 , 10718.3m3 , 14637.1m3 of water are consumed monthly in zones A, B and C, respectively. Furthermore, the results show that averages of 15.15%, 40.2%, and 24.52% of water are lost as NRW in zones A, B, and C, respectively. The NRW in all the three zones were greater than the maximum NRW of 20% recommended by Energy and Water Utilities Regulatory Authority (EWURA), and key performance indicators suggested by the SUWASA. This study inform planners and decisionmakers that management strategies should employ GIS to establish optimal routes for effective and quick response to bursts before losing a lot of water, instead of using traditional auditing systems that results into high NRW levels.

This paper presents the results of an integration of Geographical Information Systems (GIS) and Remote Sensing (RS) techniques to delineate landslide susceptible areas in Lushoto district, Tanzania. To achieve this, the study has examined the distribution of landslide events and identified susceptible areas in the district. The study collected data through a handheld Global Positioning System (GPS), open-source databases and on-screen digitization. Analytical Hierarchy Process (AHP) technique was used to evaluate factors influencing landslides and Quantum GIS software was used to analyse landslides data through multi criteria technique to generate landslide susceptible areas. The study reveals that past landslides are more concentrated in the southern habitable areas of Lushoto district in which mudflow and rock falls are more dominant. The findings further expose that rainfall (29.97%) and slopes (21.72%), are the factors that have a higher influence on the occurrence of landslides while proximity to rivers (2.48%) and NDVI (1.69%) have very low influences. Further, the findings reveal that about 45% of the total area falls under moderate to very high landslides susceptible areas. This study concludes that a large area of Lushoto district’s southern part is at risk of being battered by landslides resulting from the influence of rainfall and slopes. As such the study recommends that governmental and non-governmental organizations should intervene through the formulation of policies against human activities that induce landslides in susceptible areas and to use these geospatial results to officially demarcate these areas to minimize fatalities and other economic and environmental impacts.

Landslides are becoming increasingly widespread, claiming tens of thousands of fatalities, hundreds of thousands of injuries, and billions of dollars in economic losses each year. Thus, studies for geographically locating landslides vulnerable areas have been increasingly relevant in recent decades. This research is aimed at integrating Geographical Information Systems (GIS) and Remote Sensing (RS) techniques to delineate Landslide Susceptibility Mapping (LSM) of Lushoto District, Tanzania. RS assisted in providing remotely datasets including; Digital Elevation Models (DEM), Landsat 8 OLI imageries, and spatially distributed landslides coordinates with the use of a handheld Global Position System (GPS) receiver while various GIS analysis techniques were used in the preparation and analysis of landslides influencing factors hence, generating LSM index values. However, rainfall, slope’s angle, elevation, soil type, lithology, proximity to roads, rivers, faults, and Normalized Difference Vegetation Index (NDVI) factors were found to have direct influence on the occurrence of landslides. These factors were evaluated, weighted, and ranked using Analytical Hierarchy Process (AHP) technique in which 0.086 (8.6%) consistency ratio (CR) was attained (highly accepted). Findings reveal that, rainfall (29.97%), slopes’ angle (21.72%), elevation (15.68%), and soil types (11.77%) were found to have high influence on the occurrence of landslides while proximity to faults (8.35%), lithology (4.94%), proximity to roads (3.41%), rivers (2.48%) and NDVI (1.69%) had very low influences respectively. The overall results, obtained through Weighted Linear Combination (WLC) analysis indicate that, about 97669.65 hectares (ha) of the land is under very low landslides susceptibility levels which accounts for 24.03% of the total study area. Low susceptibility levels had 123105.84 ha (30.28%) moderate landslides susceptibility areas were found to have 140264.79 ha (34.50%) while high and very high susceptibility areas were found to cover about 45423.43 ha (11.17%) and 57.78 ha (0.01%) respectively. Furthermore, 81% overall model accuracy was obtained as computed from Area under the Curve (AUC) using Receiver Operating Characteristic (ROC) Curve.