scholarly journals INVASION OF SAVANNAS BY PROSOPIS TREES IN EASTERN AFRICA: EXPLORING THEIR IMPACTS ON LULC DYNAMICS, LIVELIHOODS AND IMPLICATIONS ON SOIL ORGANIC CARBON STOCKS

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 335-340
Author(s):  
P. R. Mbaabu ◽  
U. Schaffner ◽  
S. Eckert
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Land Cover ◽  
Management Strategies ◽  
Integrated Approach ◽  
Eastern Africa ◽  
Lulc Changes ◽  
Management Interventions ◽  
Trade Offs ◽  
Landsat Satellite

Abstract. Trees of the genus Prosopis from the Americas, were introduced in Eastern Africa in the 1970s to mitigate land degradation and its associated disservices. However, over time these trees have spread and invaded valuable grasslands and croplands and consequently led to significant land use and land cover (LULC) changes and livelihood stress. Early detection of invasive species is essential for formulating effective management strategies to prevent further spread into non-invaded lands and for monitoring the outcome of management interventions. We mapped the spatio-temporal evolution and dynamics of Prosopis invasion, its impacts on LULC and livelihoods in Baringo, Kenya by applying a Random Forest classifier on Landsat satellite data over seven-year intervals from 1988 – 2016. We then linked the LULC changes to soil organic carbon (SOC) stocks that we had measured for the different LULCs and also to socio-economic data on annual costs of clearing Prosopis from farmlands. By 2016, Prosopis had invaded 18,792 ha of land, spreading at a rate of 640 ha/yr, while all other land uses and land cover declined, each by over 40% of its original coverage in 1988. Through LULC specific SOC measurements, and relating the changes to annual costs of clearing Prosopis, we found that Prosopis removal and restoration to grassland is more effective for climate change mitigation compared to Prosopis “cultivation” while also avoiding trade-offs with other ecosystem services and livelihoods. Therefore, future management of this species in Kenya and Eastern Africa should be based on a more collaborative and integrated approach.

scholarly journals Sensitivity analysis of the GEMS soil organic carbon model to land cover land use classification uncertainties under different climate scenarios in senegal

2012 ◽  
Vol 9 (2) ◽  
pp. 631-648 ◽  
Author(s):  
A. M. Dieye ◽  
D. P. Roy ◽  
N. P. Hanan ◽  
S. Liu ◽  
M. Hansen ◽  
...  
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Land Cover ◽  
Satellite Data ◽  
Climate Change Scenarios ◽  
Land Use Classification ◽  
Biogeochemical Models ◽  
Classification Errors ◽  
Landsat Satellite

Abstract. Spatially explicit land cover land use (LCLU) change information is needed to drive biogeochemical models that simulate soil organic carbon (SOC) dynamics. Such information is increasingly being mapped using remotely sensed satellite data with classification schemes and uncertainties constrained by the sensing system, classification algorithms and land cover schemes. In this study, automated LCLU classification of multi-temporal Landsat satellite data were used to assess the sensitivity of SOC modeled by the Global Ensemble Biogeochemical Modeling System (GEMS). The GEMS was run for an area of 1560 km2 in Senegal under three climate change scenarios with LCLU maps generated using different Landsat classification approaches. This research provides a method to estimate the variability of SOC, specifically the SOC uncertainty due to satellite classification errors, which we show is dependent not only on the LCLU classification errors but also on where the LCLU classes occur relative to the other GEMS model inputs.

scholarly journals Sensitivity analysis of the GEMS soil organic carbon model to land cover land use classification uncertainties under different climate scenarios in Senegal

2011 ◽  
Vol 8 (4) ◽  
pp. 6589-6635 ◽  
Author(s):  
A. M. Dieye ◽  
D. P. Roy ◽  
N. P. Hanan ◽  
S. Liu ◽  
M. Hansen ◽  
...  
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Land Cover ◽  
Satellite Data ◽  
Climate Change Scenarios ◽  
Land Use Classification ◽  
Biogeochemical Models ◽  
Classification Errors ◽  
Landsat Satellite

Abstract. Spatially explicit land cover land use (LCLU) change information is needed to drive biogeochemical models that simulate soil organic carbon (SOC) dynamics. Such information is increasingly being mapped using remotely sensed satellite data with classification schemes and uncertainties constrained by the sensing system, classification algorithms and land cover schemes. In this study, automated LCLU classification of multi-temporal Landsat satellite data were used to assess the sensitivity of SOC modeled by the Global Ensemble Biogeochemical Modeling System (GEMS). The GEMS was run for an area of 1560 km2 in Senegal under three climate change scenarios with LCLU maps generated using different Landsat classification approaches. This research provides a method to estimate the variability of SOC, specifically the SOC uncertainty due to satellite classification errors, which we show is dependent not only on the LCLU classification errors but also on where the LCLU classes occur relative to the other GEMS model inputs.

Modelling long-term soil organic carbon dynamics under the impact of land cover change and soil redistribution

CATENA ◽  
2017 ◽  
Vol 151 ◽  
pp. 63-73 ◽  
Author(s):  
Samuel Bouchoms ◽  
Zhengang Wang ◽  
Veerle Vanacker ◽  
Sebastian Doetterl ◽  
Kristof Van Oost
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Land Cover ◽  
Land Cover Change ◽  
Carbon Dynamics ◽  
Soil Redistribution ◽  
The Impact ◽  
Soil Organic Carbon Dynamics

scholarly journals Molecular trade-offs in soil organic carbon composition at continental scale

2020 ◽  
Vol 13 (10) ◽  
pp. 687-692 ◽  
Author(s):  
Steven J. Hall ◽  
Chenglong Ye ◽  
Samantha R. Weintraub ◽  
William C. Hockaday
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Continental Scale ◽  
Trade Offs ◽  
Carbon Composition

scholarly journals DYNAMIC OF LAND DEGRADATION NEUTRALITY BASELINE INDCATORS IN THE REPUBLIC OF MACEDONIA

2019 ◽  
Vol 40 (1) ◽  
pp. 39
Author(s):  
Dusko Mukaetov Mukaetov ◽  
Ivan Blinkov ◽  
Hristina Poposka
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Land Cover ◽  
Land Degradation ◽  
Natural Capital ◽  
Data Sets ◽  
Land Productivity ◽  
Status And Trends ◽  
Global Data Sets ◽  
Global Data

Land degradation neutrality (LDN) is defined as a "state whereby the amount and quality of land resources nec-essary to support ecosystem functions and services and enhance food security remain stable or increase within specified temporal and spatial scales and ecosystems". The baseline is expressed as the initial (t0) estimated value of each of the three indicators, used as proxies of land-based natural capital and the ecosystem services that flow from that land base: land cover/land use change, land productivity status and trends, soil organic carbon status and trends. The baseline of LDN was calculated with estimation of the average values across the 10 years baseline period of the following indica-tors: Land Cover/Land Cover change (LC/LCC), Land Productivity Dynamics (LPD) and Soil Organic Carbon (SOC). Three tier approaches for computation of the selected indicators were used: Tier 1: Global/regional Earth observation, geospatial information and modelling; Tier 2: National statistics (only for LC/LCC) and Tier 3: Field survey. Most sig-nificant changes in LC for the period 2000/2012 are in the categories of Forest land and Shrubs/grasslands. According the global data sets used for analysis of LPD, the total affected area with depletion of Land productivity for the period 2000/2010 is identified on a only 2.35 % of the country territory. The available global data sets gives a model SOC lev-els for the period 2000/2010. According these data, the total loss of SOC in our country is estimated on 3951 t.

scholarly journals Spatial Evolution of Prosopis Invasion and its Effects on LULC and Livelihoods in Baringo, Kenya

2019 ◽  
Vol 11 (10) ◽  
pp. 1217 ◽  
Author(s):  
Purity Rima Mbaabu ◽  
Wai-Tim Ng ◽  
Urs Schaffner ◽  
Maina Gichaba ◽  
Daniel Olago ◽  
...  
Keyword(s):  
Integrated Approach ◽  
Well Being ◽  
Time Interval ◽  
Wet Season ◽  
Alien Plant ◽  
Random Forest Classification ◽  
Lulc Changes ◽  
Forest Classification ◽  
Landsat Satellite ◽  
Gains And Losses

Woody alien plant species have been deliberately introduced globally in many arid and semi-arid regions, as they can provide services and goods to the rural poor. However, some of these alien trees and shrubs have become invasive over time, with important impacts on biodiversity, ecosystem services, and human well-being. Prosopis was introduced in Baringo County, Kenya, in the 1980s, but since then, it has spread rapidly from the original plantations to new areas. To assess land-use and land-cover (LULC) changes and dynamics in Baringo, we used a combination of dry and wet season Landsat satellite data acquired over a seven-year time interval between 1988–2016, and performed a supervised Random Forest classification. For each time interval, we calculated the extent of Prosopis invasion, rates of spread, gains and losses of specific LULC classes, and the relative importance of Prosopis invasion on LULC changes. The overall accuracy and kappa coefficients of the LULC classifications ranged between 98.1–98.5% and 0.93–0.96, respectively. We found that Prosopis coverage increased from 882 ha in 1988 to 18,792 ha in 2016. The highest negative changes in LULC classes were found for grasslands (−6252 ha; −86%), irrigated cropland (−849 ha; −57%), Vachellia tortilis-dominated vegetation (−3602 ha; −42%), and rainfed cropland (−1432 ha; −37%). Prosopis invasion alone directly accounted for over 30% of these negative changes, suggesting that Prosopis invasion is a key driver of the observed LULC changes in Baringo County. Although the management of Prosopis by utilization has been promoted in Baringo for 10–15 years, the spread of Prosopis has not stopped or slowed down. This suggests that Prosopis management in Baringo and other invaded areas in East Africa needs to be based on a more integrated approach.

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