Drivers of Degradation: Linking Large-scale Degradation to Human Influence in the Nigerian Guinea Savannah

2020 ◽  
Author(s):  
Ademola. A Adenle ◽  
Chinwe Ifejika Speranza
Keyword(s):  
Land Use ◽  
Population Density ◽  
Land Degradation ◽  
Large Scale ◽  
Spatial Relation ◽  
Holistic Approach ◽  
Spatial Proximity ◽  
Land Use Land Cover ◽  
Land Use Management ◽  
Human Influence

<p>The Nigerian Guinea Savannah is the largest agro-ecological belt, encompassing about 49% of Nigeria, and is one of the most diverse, fragile and threatened ecosystems in the country.  Land degradation in the zone is a serious challenge driven by deforestation, agriculture and other livelihood needs. Yet the link between land degradation and unsustainable human influence is widely acknowledged but spatially under explored. The study thus examined the spatial relation of human influence with land degradation in order to inform better land use management. We updated the Human Influence Index by combining the following spatial layers, namely: (1) distance to a major city; (2) land use/land cover; (3) human population density; (4) distance to major roads; (5) distance to railways; and (6) navigable waterways. We then overlaid the Human Influence Index with MODIS-derived land degradation status in order to explain the level of human influence on land degradation. In total, 38% of the Nigerian Guinea Savannah land area are becoming more degraded, while 14% and 48% of the remaining area show either improvement or no change, respectively. However, spatial proximity of human activities was observed to influence land degradation, but with more degradation occurring in areas of low population density. This shows that the spatial pattern of Human Influence Index data cannot completely explain land degradation in the zone. We thus present a more holistic approach to identifying human influence on land degradation in the Nigerian Guinea Savannah.  </p>

scholarly journals Estimating population and livestock density of mobile pastoralists and sedentary settlements in the south-eastern Lake Chad area

2015 ◽  
Vol 10 (1) ◽  
Author(s):  
Vreni Jean-Richard ◽  
Lisa Crump ◽  
Abbani Alhadj Abicho ◽  
Ali Abba Abakar ◽  
Abdraman Mahamat II ◽  
...  
Keyword(s):  
Land Use ◽  
Population Density ◽  
Small Ruminants ◽  
Dry Season ◽  
Land Use Management ◽  
Lake Chad ◽  
Mobile Pastoralists ◽  
Livestock Density ◽  
Density Dynamics ◽  
First Time

Mobile pastoralists provide major contributions to the gross domestic product in Chad, but little information is available regarding their demography. The Lake Chad area population is increasing, resulting in competition for scarce land and water resources. For the first time, the density of people and animals from mobile and sedentary populations was assessed using randomly defined sampling areas. Four sampling rounds were conducted over two years in the same areas to show population density dynamics. We identified 42 villages of sedentary communities in the sampling zones; 11 (in 2010) and 16 (in 2011) mobile pastoralist camps at the beginning of the dry season and 34 (in 2011) and 30 (in 2012) camps at the end of the dry season. A mean of 64.0 people per km2 (95% confidence interval, 20.3-107.8) were estimated to live in sedentary villages. In the mobile communities, we found 5.9 people per km2 at the beginning and 17.5 people per km2 at the end of the dry season. We recorded per km2 on average 21.0 cattle and 31.6 small ruminants in the sedentary villages and 66.1 cattle and 102.5 small ruminants in the mobile communities, which amounts to a mean of 86.6 tropical livestock units during the dry season. These numbers exceed, by up to five times, the published carrying capacities for similar Sahelian zones. Our results underline the need for a new institutional framework. Improved land use management must equally consider the needs of mobile communities and sedentary populations.

The Impact of Land Use/Land Cover Changes on Land Degradation Dynamics: A Mediterranean Case Study

2012 ◽  
Vol 49 (5) ◽  
pp. 980-989 ◽  
Author(s):  
S. Bajocco ◽  
A. De Angelis ◽  
L. Perini ◽  
A. Ferrara ◽  
L. Salvati
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Degradation ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
The Impact

A holistic approach towards sustainable land use management in the Hedgerow — Field crop interface

2001 ◽  
pp. 1026-1027 ◽  
Author(s):  
R. Mette ◽  
G. Uckert ◽  
B. Sattelmacher
Keyword(s):  
Land Use ◽  
Holistic Approach ◽  
Sustainable Land Use ◽  
Field Crop ◽  
Land Use Management

Tracking the Land Use/Land Cover Change in an Area with Underground Mining and Reforestation via Continuous Landsat Classification

2019 ◽  
Vol 11 (14) ◽  
pp. 1719 ◽  
Author(s):  
Jiaxin Mi ◽  
Yongjun Yang ◽  
Shaoliang Zhang ◽  
Shi An ◽  
Huping Hou ◽  
...  
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Land Cover ◽  
Large Scale ◽  
Underground Mining ◽  
Mining Area ◽  
Random Forest Classifier ◽  
Land Use Land Cover ◽  
Lulc Change ◽  
Mining Areas

Understanding the changes in a land use/land cover (LULC) is important for environmental assessment and land management. However, tracking the dynamic of LULC has proved difficult, especially in large-scale underground mining areas with extensive LULC heterogeneity and a history of multiple disturbances. Additional research related to the methods in this field is still needed. In this study, we tracked the LULC change in the Nanjiao mining area, Shanxi Province, China between 1987 and 2017 via random forest classifier and continuous Landsat imagery, where years of underground mining and reforestation projects have occurred. We applied a Savitzky–Golay filter and a normalized difference vegetation index (NDVI)-based approach to detect the temporal and spatial change, respectively. The accuracy assessment shows that the random forest classifier has a good performance in this heterogeneous area, with an accuracy ranging from 81.92% to 86.6%, which is also higher than that via support vector machine (SVM), neural network (NN), and maximum likelihood (ML) algorithm. LULC classification results reveal that cultivated forest in the mining area increased significantly after 2004, while the spatial extent of natural forest, buildings, and farmland decreased significantly after 2007. The areas where vegetation was significantly reduced were mainly because of the transformation from natural forest and shrubs into grasslands and bare lands, respectively, whereas the areas with an obvious increase in NDVI were mainly because of the conversion from grasslands and buildings into cultivated forest, especially when villages were abandoned after mining subsidence. A partial correlation analysis demonstrated that the extent of LULC change was significantly related to coal production and reforestation, which indicated the effects of underground mining and reforestation projects on LULC changes. This study suggests that continuous Landsat classification via random forest classifier could be effective in monitoring the long-term dynamics of LULC changes, and provide crucial information and data for the understanding of the driving forces of LULC change, environmental impact assessment, and ecological protection planning in large-scale mining areas.

Understanding Turning Points in Dryland Ecosystem Functioning (U-TURN)

2020 ◽  
Author(s):  
Stephanie Horion ◽  
Paulo Bernardino ◽  
Wanda De Keersmaecker ◽  
Rasmus Fensholt ◽  
Stef Lhermitte ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Extreme Events ◽  
Land Degradation ◽  
Ecosystem Functioning ◽  
Large Scale ◽  
Turning Points ◽  
Ecosystem Change ◽  
Ecosystem Development ◽  
Ecosystem Research

<p>Pressures on dryland ecosystems are ever growing. Large-scale vegetation die-offs, biodiversity loss and loss in ecosystem services are reported as a result of unsustainable land use, climate change and extreme events. Yet major uncertainties remain regarding our capability to accurately assess on-going land changes, as well as to comprehensively attribute drivers to these changes. Indeed ecosystem response to external pressures is often complex (e.g. non-linear) and non-unique (i.e. same response, different drivers). Besides critical knowledge on ecosystem stability and coping capacities to extreme events has still to be consolidated.</p><p>Recent advances in time series analysis and in the assessment of breakpoint open a new door in ecosystem research as they allow for the detection of turning points and tipping points in ecosystem development (Horion et al., 2016 and 2019). Identifying ecosystems that have significantly changed their way of functioning, i.e. that have tipped to a new functioning state, is of crucial importance for Ecology studies. These extremes cases of vegetation instability are golden mines for researches that try to understand how resilient are ecosystems to climate change and to non-sustainable use of land.</p><p>This is precisely what the U-TURN project is about:</p><ul><li><strong>Developing methods for detecting turning points in dryland ecosystem functioning</strong>; Here we defined <em>turning point</em> in ecosystem functioning as a key moment in the ecosystem development where its functioning is significantly changed or altered without implying the irreversibility of the process (Horion et al. (2016)), by opposition to the term ‘<em>tipping point</em>’ that implies irreversibility (Lenton et al. 2008).</li> <li><strong>Studying the contribution of climate and human pressure</strong> (e.g. land-use intensification, human induced land soil degradation) in pushing the ecosystem outside its safe operating space ; Here we used Earth Observation techniques coupled with Dynamic Vegetation Models to get process-based insights on the drivers of the observed changes in ecosystem functioning.</li> <li>Exploring whether <strong>early warning signal of turning points</strong> can be identified.</li> </ul><p>During our talk, we will present key methodological advances being achieved within the U-TURN project, and showcase some of our major findings in relation to abrupt changes in dryland ecosystem functioning.</p><p><strong>References:</strong></p><p>Horion, S., Ivits, E., De Keersmaecker, W., Tagesson, T., Vogt, J., & Fensholt, R. (2019). Mapping European ecosystem change types in response to land‐use change, extreme climate events, and land degradation. Land Degradation & Development, 30(8), 951-963. doi:10.1002/ldr.3282</p><p>Horion, S., Prishchepov, A. V., Verbesselt, J., de Beurs, K., Tagesson, T., & Fensholt, R. (2016). Revealing turning points in ecosystem functioning over the Northern Eurasian agricultural frontier. Global Change Biology, 22(8), 2801-2817. doi:10.1111/gcb.13267</p><p>Lenton, T. M., Held, H., Kriegler, E., Hall, J. W., Lucht, W., Rahmstorf, S., & Schellnhuber, H. J. (2008). Tipping elements in the Earth's climate system. Proc Natl Acad Sci U S A, 105(6), 1786-1793. doi:10.1073/pnas.0705414105</p><p> </p><p><strong>Project website: http://uturndryland.wixsite.com/uturn</strong></p><p>This research is funded by the Belgian Federal Science Policy Office (Grant/Award Number:SR/00/339)</p>

Level of Urbanization and Land Use/Land Cover Landscape Pattern of Relationship Analysis

2013 ◽  
Vol 316-317 ◽  
pp. 197-199 ◽  
Author(s):  
Chen Guang Xu ◽  
Amat Anwar
Keyword(s):  
Land Use ◽  
Population Density ◽  
Land Use Change ◽  
Landscape Pattern ◽  
Agricultural Land ◽  
Land Use Intensity ◽  
Land Use Land Cover ◽  
Rapid Urbanization ◽  
Relationship Analysis ◽  
The Relationship

On the basis of analysis of the development of urbanization and land use change in Zhengzhou City in2004-2010, The drive mechanism of the intensity of land use change in the research context of rapid urbanization, To explore the degree of land use change human driving factors, Drive and build a model, The results show that: Unit of agricultural land and the ratio of the output value of the land for construction as well as population density and land use intensity positive correlation, Making land use intensity tends to increase, The population density growth, Urbanization of the population and economic non-farm is the main driving factor for the intensity of land use change. And then proceed to the analysis of the spatial pattern of Zhengzhou City, Investigate the level of urbanization and land use / cover the relationship between landscape pattern.

scholarly journals The Land Use - Land Degradation Nexus in Mediterranean Landscapes – Drivers of Changes And Key Processes at Selected Natura 2000 Sites of Crete, Greece

2020 ◽  
Vol 13 (1-2) ◽  
pp. 13-24
Author(s):  
Burghard C. Meyer ◽  
Fabian Kirsten ◽  
Dietmar Sattler ◽  
Jürgen Heinrich
Keyword(s):  
Land Use ◽  
Land Degradation ◽  
Large Scale ◽  
Land Use Changes ◽  
Natura 2000 ◽  
Field Work ◽  
Research Field ◽  
Processes Of Change ◽  
Land Uses ◽  
Mediterranean Landscapes

AbstractThe land use–land degradation nexus in Cretan landscapes in regions with Natura 2000 sites was analyzed by an explorative expert driven study based on literature, field work and photo documentation methods with the aim of determining status, drivers and key processes of change. Drivers of current land use changes have been worked out by (1) general tourism developments and tourism related land uses; (2) irrigated olive yard developments; (3) fenced large-scale goat pastures and (4) large scale greenhouses. Key processes of change have been identified and qualitatively assessed for 5 regions with NATURA 2000 areas based on a non-ranked set of 11 descriptive indicators. The analysis includes the status-description and the importance assessment of land degradation processes in selected NATURA 2000 sites. Threats and pressures taken from the NATURA 2000 documentation and the land use – land degradation nexus and the analysis are a suitable basis for future land management in order to reach land degradation neutrality. The result of our analysis opens a new research field for a better integration of the normally thematically isolated analysis in geography, biology/nature conservation and agricultural policy analysis about the drivers and processes in landscape systems towards a better understanding the trends in land cover change (e.g. vegetation/soil degradation), the trends in productivity or functioning changes caused by land uses and as well for the trends in carbon stock change.

Sign in / Sign up

Export Citation Format

Share Document