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    The Map consist of 6 Local Government ares in Lagos which includes: Apapa, Ajeromi-Ifelodun, Amuwo-Odofin, Surulere, Lagos Island and Lagos Mainland). The Maps were generated using RapidEye image, a 5m resolution, using Object based image analysis.

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    Land degradation in this dataset is defined as the persistent reduction or loss of land ecosystem services, notably the primary production service. The long-term trend of inter-annual mean Normalized Difference Vegetation Index (NDVI), over the period 1982–2006, is used as a proxy for the persistent decline or improvement in the Net Primary Productivity (NPP) of land, thereby delineating land degradation hotspots. The Global Inventory Modelling and Mapping Studies (GIMMS) dataset of 64 km2-resolution of NDVI data employed has been corrected for rainfall variation effect and atmospheric fertilization effect in addition to the masking of ineligible pixels. The data is used to calculate the area of NDVI decline in km2 and in percentages for corresponding land covers. The dataset provided contains different files (GIS files, a table and a report). The report,"BIOMASS PRODUCTIVITY-BASED MAPPING OF GLOBAL LAND DEGRADATION HOTSPOTS", contains a detailed description of how the global land degradation hotspots, based on biomass productivity, were identified. A summary of the “ground truthing” methods is also presented. The GIS files map the global land degradation hotspots versus main land cover/use types. In addition, the GIS files map the areas with above ground biomass improvement. The area (km2) of long-term (1982-2006) NDVI decline (with correction of RF and AF effects and masking of saturated NDVI zone) versus main land cover/use types counted for each country are then presented in the excel table.

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    The Map shows the overlay of the number of land cover changes with NDVI decrease and increase between 2001 and 2011 referring to NDVI trends. Three classes among the trends are built. Besides a “tolerance class” meaning NDVI trends between -0.005 and +0.005 the dataset was classified into “decreasing” (NDVI Trend <-0.005) and “increasing” (NDVI trend >0.005) vegetation trends. The overlay highlights the southern part of Kenya, especially the counties Narok and Kajiado where a stable land cover and decreasing trends overlap. Within this overlap are also Kitui and Isiolo – both counties that were also highlighted in the OLS-regression output as underpredicting –, parts of Marsabit and some small areas along the coastline. Also again the northwestern area, mainly Turkana Region but also West Pokot and Baringo are expressing increasing trends and seem to be linked to a more stable land cover.

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    MODIS provides the Land Cover Type Product MCD12Q1 (Friedl et al., 2002) with 500m grid resolution which represents the same pixel size also used for the MODIS NDVI time-series analysis. Annually data provision and a matching pixel size with the MODIS NDVI data used earlier in this study were key elements for choosing this dataset. The Map shows the number of LULC changes as calculated based on the methods described in chapter II.3.3. Stable areas – where land cover changes are zero – can be identified in southern Kenya, Kajiado County in particular, but also in western Kenya north of Lake Victoria, around Lake Turkana, and in the northeastern part of Kenya bordering Ethiopia. Around 33.16% of the total land area experience zero changes from 2001-2011 while 16.11% changed once and 22.92% show two changes. Three (13.98%), four (9.53%) and five (3.42%) changes can still be observed in Map III.11 while areas experiencing more than five changes are occurring in less than 1% of the total land area. The different classes show the number of land cover changes within the observation period.

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    Alluvial gold mining generates a vast amount of extractive waste that completely covers the natural soil, destroys riparian ecosystems, and negatively impacts river beds and valleys. Since 2002, a gold mining company has striven to create agroforestry plots in the waste deposits as a post-mining management approach, where agricultural crops and livestock are combined to complement reforestation in the area. This research aims at supporting reclamation of waste deposits by providing a comprehensive understanding of processes to manage the transition of nutrient-poor and acidic deposition sites towards productive agroforestry-based systems. Major components of this research comprise (i) an analysis of environmental and social challenges of the gold mining sector in Colombia, and its potential opportunities to add value to affected communities, (ii) an assessment of management practices and decision-making processes of the farmers working on reclamation areas, (iii) an analysis of the sources of variability of waste deposits from the perspective of soil development and vegetation succession, (iv) an analysis of spatial variability of the physicochemical properties of waste deposits with a spatially explicit management scheme, and (v) an assessment of vegetation recovery in terms of biomass and plant community composition. Farmers who are currently working on areas undergoing reclamation rely mostly on their own local knowledge to respond to the challenges that the heavily disturbed conditions of the area pose to crop establishment. Therefore, increasing their awareness of the inherent heterogeneity of their fields, as well as the interdependencies between management practices and improvement of soil fertility, may increase the productivity of their farms. The analysis of sources of variability of the waste deposits generated by alluvial gold mining revealed that these deposits are primarily influenced by the parent material of the alluvial gold deposits and by the technology used for gold mining (bucket or suction dredges), which define the type of deposit formed (gravel or sand). Waste deposits can provide essential functions for rural areas such as woody biomass production and crop establishment if deposits are managed according to a specific purpose, and crop selection for each deposit is done based on physicochemical and structural soil properties. This finding is echoed by the spatial assessment of vegetation reestablishment through the combination of remote sensing with machine-learning techniques that show a high spatial variability of textural properties and nutrient contents of the deposits. A management approach is proposed with the use of delineated management zones, which can lead to an overall increased productivity by developing strategies suitable to the characteristics of each field and its potential uses.

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    The file describes tree diameters at breast height (DBH) and heights measurements of green spaces in Kumasi Ghana. The elevation and ground distance data are used to calculate the actual heights of trees. Both DBH and height data are then used to estimate tree biomass using appropriate biomass allometric equations.

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    The Upper Kharun Catchment (UKC) is one of the most important, economically sound and highly populated watersheds of Chhattisgarh state in India. It covers diverse land use types: urban, rural, agricultural, forest and industrial areas. The study area is a part of the newly formed state, which was established in 2000 and is characterized by considerable population growth and expansion of urban areas, industrialization, and irrigation areas and facilities for meeting the increasing food demand. Furthermore, the government has planned the formation of the new capital city. The planning unit is partly in the study area, and hence there is an urgent need to estimate the impact of future land use change on the water resources of UKC, and to consider whether and to which degree the intensification of irrigated agriculture is putting the groundwater resources of the UKC at risk of over-exploitation that might lead to a major water crisis in near future. Climate change is likely to severely affect the surface and groundwater resources due to changes in precipitation and evapotranspiration and their spatio-temporal distribution. The impact of future climate change may be felt more severely in the study area, which is already under stress due to the current population increase and associated demands for energy, freshwater and food. In spite of the uncertainties about the precise magnitude of climate change and its possible impacts, particularly on regional scales, measures must be taken to anticipate, mitigate and/or adapt to its adverse effects on surface and ground-water availability. There is no research documented in literature related to climate change and land use change impacts on water resources of the UKC. Hence, an attempt is made to overcome these shortcomings and to run the model SWAT with high resolution input data taking irrigation issues relevant in the UKC explicitly into account. For this purpose, the climate scenarios of the PRECIS regional climate model were bias corrected to station level, and land use maps of 1991, 2001, 2011 and 2021 were prepared with details of surface and groundwater-irrigated areas. The results of the study provide the base for framing strategies for water resource management in the study area. The main findings show that the overall rainfall trend for the UKC increased at a rate of 1.94 mm per annum at p=0.033 level of significance from 1961-2011. No statistically significant change in rainfall in the month of peak rainfall was observed. Mid July remains the period of peak rainfall over the years (1961-2011). There was no significant trend for mean annual temperature. However, slight increase in temperature was detected in specific months. The bias-corrected PRECIS RCM scenarios show an increasing trend for both mean annual rainfall and temperature (except for the q0 and q1 scenarios for the 2020s, where there is a decrease in annual rainfall compared to the baseline). The mean monthly rainfall increases for all scenarios, except for the month of June, where a significant decrease in rainfall is predicted. The main land use change pattern between 1991 and 2011 shows a significant increase in urban areas by 4.67%, decrease in wasteland by 3.76%, increase in area under two-season crops by 5.43 %, while 5.67% of the area is under more than two-season crops with paddy as a summer crop. The two and more than two-season crops are irrigated by groundwater sources. The land use scenario of 2021 shows a further increase in built-up area by 2.6% compared to 2011. Also, the groundwater-irrigated area with two-season crops is expected to increase by 24.25% and the area with more than two-season crops with summer paddy by 12.57%, which indicates an excessive increase in groundwater irrigation for some villages in the UKC and unsustainable use of the precious groundwater resources. On the UKC scale, the impact of land use change on different water balance components is small. There is a decreasing trend of annual discharge, water yield and groundwater contribution to streamflow, and an increasing trend of annual surface runoff and actual evapotranspiration over the decades. The impact on water resources is significant and clearly visible at sub-catchment level, where an increasing trend for urban areas can be observed. Based on the bias-corrected climate scenarios q0, q1 and q14, changes in the main water balance components were simulated with the SWAT model. The simulated annual discharge for the 2020s ranged between 25.9% decrease to 23.6% increase depending on the PRECIS scenario. For the 2050s, discharged ranges between 17.6% decrease to 39.4% increase, and for the 2080s an increase in the range of 16.3% to 63.7% is simulated. The annual surface runoff for the 2020s ranges between 28.8% decrease to 26.8% increase. For the 2050s, predictions vary between 17.9% decrease to 44.1% increase, whereas for the 2080s an increase in the range of 19.5% to 69.6% is expected. The annual percolation for the 2020s is estimated to range between 12.8% decrease to 8.7% increase. Predictions for the 2050s range between 10.3% decrease to 15.4% increase, and for the 2080s between 0.3% decrease and 13.7% increase. The annual groundwater contribution to streamflow for the 2020s is expected in the range of 7.0% decrease to 14.7% increase. Predictions for the 2050s range from 13.3% decrease to 64.7% increase, and for the 2080s between 10.4% decrease and 59.1% increase. Scenario Q1 shows a decrease in annual groundwater quantity in all time steps.

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    These data were gathered for the purpose of research in Work Package 4.5 within the context of the BiomassWeb Project (https://biomassweb.org/). The data are on agricultural land use, resource management practices and production in small-scale maize systems in Bolgatanga and Bongo Districts, Upper East Region, Ghana. The data were collected by Francis Molua Mwambo, using semi-structured questionnaire to interview maize farmers in 2015. The data are based on the interviewees' estimate, which were collected as feedback to the questionnaire during a field survey in the study area.