Geographic information systems (GIS) tool are becoming increasingly important in conservation and natural resource management planning and implementation. The good news is that it is also getting easier to find relevant and freely available (spatial) data sets online. However, we also need to be careful in evaluating the accuracy and reliability of these data sets, as illustrated by an article I came across today. Continue reading “Importance of spatial data accuracy – of protected areas”
Just got an article out in PlosOne. Analysis were carried out and maps and figures created using a stack of open source tools, including GRASS GIS, R, and QGIS. The article addresses the question whether protected areas in Eastern Africa are representative of the diverse range of species and habitats found in the region and whether they protect those areas where biodiversity is threatened most? The paper uses a recently developed high-resolution potential natural vegetation (PNV) map for eastern Africa as a baseline to more effectively identify conservation priorities. It examines how well different potential natural vegetations (PNVs) are represented in the protected area (PA) network of eastern Africa and used a multivariate environmental similarity index to evaluate biases in PA versus PNV coverage. In addition, levels of threat to different PNVs are assessed. Results indicate substantial differences in the conservation status of PNVs and particular PNVs in which biodiversity protection and ecological functions are at risk due to human influences are revealed. The data and approach presented here provide a step forward in developing more transparent and better informed translation from global priorities to regional or national implementation in eastern Africa, and are valid for other geographic regions.
Citation: van Breugel P., R. Kindt, J.-P.B. Lillesø, M. van Breugel (2015) Environmental Gap Analysis to Prioritize Conservation Efforts in Eastern Africa. PLoS ONE 10(4): e0121444. doi:10.1371/journal.pone.0121444
ISRIC, Earth Institute, Columbia University, World Agroforestry Centre (ICRAF) and the International Center for Tropical Agriculture (CIAT) have recently released a new data set of raster layers with various predicted soil properties. This data set is referred to as the “AfSoilGrids250m” data set. It supersedes the SoilGrids1km data set and comes at a resolution of 250 meter. The AfSoilGrids250m data (GeoTIFFs) are available for download under the Attribution 4.0 International (CC BY 4.0) license. See this page for download information.
In this post I’ll show you how you can import this data set in a GRASS GIS database. Continue reading “Importing data in GRASS GIS – an example”
If you are interested in the use of GIS / spatial tools in the development and implementation of a management plan of conservation areas, have a look at Robert (GeoBob) Ford’s blog. It gives background information and ideas concerning the development of a GIS-based General Management Plan (GMP) for two national parks (Kundelungu and Upemba) in Congo. It is well written and clearly based on a lot of expertise. And for the visually inclined, there are a lot of very nice pictures too.
Data on poverty levels, important for amongst others development study, yet very difficult to get. Sure, you can get global statistics from e.g., the FAO, Worldbank, here or here. But these give you highly aggregated data (mostly by country), not the sub-national level data often needed.
Luckily for those that need sub-national level data, HarvestChoice published in 2010 global sub-national poverty maps. The maps give the distribution of various poverty indices at sub-national level. More recently, Continue reading “Poverty maps on HarvestChoice”
I am creating a series of maps based on the Global forest resources assessment 2010 (FRA). A first example is the map below with the forest and woodland cover per country as percentage of the total land area. The pie charts show the proportion of the forest-woodland area covered by forest and the portion covered by woodland per country. There are obviously other, and possible better, ways to present this information, but I wanted to try out and demonstrate the use of chart overlay in QGIS. Continue reading “Mapping Forest resource assessment 2010 data – part II”
About a year ago I wrote a post about how to make a map showing deforestation rates in Africa based on the Global Forest Assessment 2005 by the Food and Agriculture Organization of the United Nation (FAO). The main purpose was to show how this could be done in R. It has since become the most viewed post on my blog, mostly by people looking for maps on deforestation or forest cover in Africa.
Autumn last year the new Global forest resources assessment 2010 (FRA) has been published by the FAO. Time for an update Continue reading “Mapping Forest resource assessment 2010 data – part I”