The Experts below are selected from a list of 273 Experts worldwide ranked by ideXlab platform
Sandra Eckert - One of the best experts on this subject based on the ideXlab platform.
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Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya
Remote Sensing, 2017Co-Authors: Purity Rima, Kathrin Einzmann, Markus Immitzer, Clement Atzberger, Sandra EckertAbstract:Prosopis was introduced to Baringo, Kenya in the early 1980s for provision of fuelwood and for controlling desertification through the Fuelwood Afforestation Extension Project (FAEP). Since then, Prosopis has hybridized and spread throughout the region. Prosopis has negative ecological impacts on biodiversity and socio-economic effects on livelihoods. Vachellia tortilis, on the other hand, is the dominant indigenous tree species in Baringo and is an important natural resource, mostly preferred for wood, fodder and charcoal production. High utilization due to anthropogenic pressure is affecting the Vachellia populations, whereas the well adapted Prosopis—competing for nutrients and water—has the potential to replace the native Vachellia vegetation. It is vital that both species are mapped in detail to inform stakeholders and for designing management strategies for controlling the Prosopis invasion. For the Baringo area, few remote sensing studies have been carried out. We propose a detailed and robust object-based Random Forest (RF) classification on high spatial resolution Sentinel-2 (ten meter) and Pleiades (two meter) data to detect Prosopis and Vachellia spp. for Marigat sub-county, Baringo, Kenya. In situ reference data were collected to train a RF classifier. Classification results were validated by comparing the outputs to independent reference data of test sites from the “Woody Weeds” project and the Out-Of-Bag (OOB) confusion matrix generated in RF. Our results indicate that both datasets are suitable for object-based Prosopis and Vachellia classification. Higher accuracies were obtained by using the higher spatial resolution Pleiades data (OOB accuracy 0.83 and independent reference accuracy 0.87–0.91) compared to the Sentinel-2 data (OOB accuracy 0.79 and independent reference accuracy 0.80–0.96). We conclude that it is possible to separate Prosopis and Vachellia with good accuracy using the Random Forest classifier. Given the cost of Pleiades, the free of charge Sentinel-2 data provide a viable alternative as the increased spectral resolution compensates for the lack of spatial resolution. With global revisit times of five days from next year onwards, Sentinel-2 based classifications can probably be further improved by using temporal information in addition to the spectral signatures.
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Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya
MDPI AG, 2017Co-Authors: Purity Rima, Kathrin Einzmann, Markus Immitzer, Clement Atzberger, Sandra EckertAbstract:Prosopis was introduced to Baringo, Kenya in the early 1980s for provision of fuelwood and for controlling desertification through the Fuelwood Afforestation Extension Project (FAEP). Since then, Prosopis has hybridized and spread throughout the region. Prosopis has negative ecological impacts on biodiversity and socio-economic effects on livelihoods. Vachellia tortilis, on the other hand, is the dominant indigenous tree species in Baringo and is an important natural resource, mostly preferred for wood, fodder and charcoal production. High utilization due to anthropogenic pressure is affecting the Vachellia populations, whereas the well adapted Prosopis—competing for nutrients and water—has the potential to replace the native Vachellia vegetation. It is vital that both species are mapped in detail to inform stakeholders and for designing management strategies for controlling the Prosopis invasion. For the Baringo area, few remote sensing studies have been carried out. We propose a detailed and robust object-based Random Forest (RF) classification on high spatial resolution Sentinel-2 (ten meter) and Pléiades (two meter) data to detect Prosopis and Vachellia spp. for Marigat sub-county, Baringo, Kenya. In situ reference data were collected to train a RF classifier. Classification results were validated by comparing the outputs to independent reference data of test sites from the “Woody Weeds” project and the Out-Of-Bag (OOB) confusion matrix generated in RF. Our results indicate that both datasets are suitable for object-based Prosopis and Vachellia classification. Higher accuracies were obtained by using the higher spatial resolution Pléiades data (OOB accuracy 0.83 and independent reference accuracy 0.87–0.91) compared to the Sentinel-2 data (OOB accuracy 0.79 and independent reference accuracy 0.80–0.96). We conclude that it is possible to separate Prosopis and Vachellia with good accuracy using the Random Forest classifier. Given the cost of Pléiades, the free of charge Sentinel-2 data provide a viable alternative as the increased spectral resolution compensates for the lack of spatial resolution. With global revisit times of five days from next year onwards, Sentinel-2 based classifications can probably be further improved by using temporal information in addition to the spectral signatures
Purity Rima - One of the best experts on this subject based on the ideXlab platform.
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Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya
Remote Sensing, 2017Co-Authors: Purity Rima, Kathrin Einzmann, Markus Immitzer, Clement Atzberger, Sandra EckertAbstract:Prosopis was introduced to Baringo, Kenya in the early 1980s for provision of fuelwood and for controlling desertification through the Fuelwood Afforestation Extension Project (FAEP). Since then, Prosopis has hybridized and spread throughout the region. Prosopis has negative ecological impacts on biodiversity and socio-economic effects on livelihoods. Vachellia tortilis, on the other hand, is the dominant indigenous tree species in Baringo and is an important natural resource, mostly preferred for wood, fodder and charcoal production. High utilization due to anthropogenic pressure is affecting the Vachellia populations, whereas the well adapted Prosopis—competing for nutrients and water—has the potential to replace the native Vachellia vegetation. It is vital that both species are mapped in detail to inform stakeholders and for designing management strategies for controlling the Prosopis invasion. For the Baringo area, few remote sensing studies have been carried out. We propose a detailed and robust object-based Random Forest (RF) classification on high spatial resolution Sentinel-2 (ten meter) and Pleiades (two meter) data to detect Prosopis and Vachellia spp. for Marigat sub-county, Baringo, Kenya. In situ reference data were collected to train a RF classifier. Classification results were validated by comparing the outputs to independent reference data of test sites from the “Woody Weeds” project and the Out-Of-Bag (OOB) confusion matrix generated in RF. Our results indicate that both datasets are suitable for object-based Prosopis and Vachellia classification. Higher accuracies were obtained by using the higher spatial resolution Pleiades data (OOB accuracy 0.83 and independent reference accuracy 0.87–0.91) compared to the Sentinel-2 data (OOB accuracy 0.79 and independent reference accuracy 0.80–0.96). We conclude that it is possible to separate Prosopis and Vachellia with good accuracy using the Random Forest classifier. Given the cost of Pleiades, the free of charge Sentinel-2 data provide a viable alternative as the increased spectral resolution compensates for the lack of spatial resolution. With global revisit times of five days from next year onwards, Sentinel-2 based classifications can probably be further improved by using temporal information in addition to the spectral signatures.
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Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya
MDPI AG, 2017Co-Authors: Purity Rima, Kathrin Einzmann, Markus Immitzer, Clement Atzberger, Sandra EckertAbstract:Prosopis was introduced to Baringo, Kenya in the early 1980s for provision of fuelwood and for controlling desertification through the Fuelwood Afforestation Extension Project (FAEP). Since then, Prosopis has hybridized and spread throughout the region. Prosopis has negative ecological impacts on biodiversity and socio-economic effects on livelihoods. Vachellia tortilis, on the other hand, is the dominant indigenous tree species in Baringo and is an important natural resource, mostly preferred for wood, fodder and charcoal production. High utilization due to anthropogenic pressure is affecting the Vachellia populations, whereas the well adapted Prosopis—competing for nutrients and water—has the potential to replace the native Vachellia vegetation. It is vital that both species are mapped in detail to inform stakeholders and for designing management strategies for controlling the Prosopis invasion. For the Baringo area, few remote sensing studies have been carried out. We propose a detailed and robust object-based Random Forest (RF) classification on high spatial resolution Sentinel-2 (ten meter) and Pléiades (two meter) data to detect Prosopis and Vachellia spp. for Marigat sub-county, Baringo, Kenya. In situ reference data were collected to train a RF classifier. Classification results were validated by comparing the outputs to independent reference data of test sites from the “Woody Weeds” project and the Out-Of-Bag (OOB) confusion matrix generated in RF. Our results indicate that both datasets are suitable for object-based Prosopis and Vachellia classification. Higher accuracies were obtained by using the higher spatial resolution Pléiades data (OOB accuracy 0.83 and independent reference accuracy 0.87–0.91) compared to the Sentinel-2 data (OOB accuracy 0.79 and independent reference accuracy 0.80–0.96). We conclude that it is possible to separate Prosopis and Vachellia with good accuracy using the Random Forest classifier. Given the cost of Pléiades, the free of charge Sentinel-2 data provide a viable alternative as the increased spectral resolution compensates for the lack of spatial resolution. With global revisit times of five days from next year onwards, Sentinel-2 based classifications can probably be further improved by using temporal information in addition to the spectral signatures
Andrew F. Zull - One of the best experts on this subject based on the ideXlab platform.
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a crowding dependent population model for Woody Weeds where size does matter
Environmental Modelling and Software, 2016Co-Authors: Andrew F. Zull, Roger A. Lawes, Oscar J. CachoAbstract:Abstract Integrated weed management for Woody Weeds is difficult to implement, partly due to the unknown effects of plant size on intraspecific plant competition. Moreover, weed literature often uses density (quantity) as a measure of control efficacy; this is insufficient for Woody Weeds due to varying plant sizes within populations. Using Ziziphus mauritiana as a case study, we describe a method of simultaneously measuring plant sizes and density: crowdedness. A new deterministic crowding-dependent matrix population model was developed by grouping the population into ten life stages. Elasticity analyses and simulations showed that removing the largest plant had the greatest control efficacy on new and old infestations in riparian and upland zones; despite subsequent mass recruitments. The model also accommodated for shocks without overcompensating. The alternative measure of plant abundance developed in this paper, provides a useful tool to assist in Woody-weed control decisions and provide a better measure of weed-control efficacy.
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A crowding-dependent population model for Woody Weeds – Where size does matter
Environmental Modelling & Software, 2016Co-Authors: Andrew F. Zull, Roger A. Lawes, Oscar J. CachoAbstract:Abstract Integrated weed management for Woody Weeds is difficult to implement, partly due to the unknown effects of plant size on intraspecific plant competition. Moreover, weed literature often uses density (quantity) as a measure of control efficacy; this is insufficient for Woody Weeds due to varying plant sizes within populations. Using Ziziphus mauritiana as a case study, we describe a method of simultaneously measuring plant sizes and density: crowdedness. A new deterministic crowding-dependent matrix population model was developed by grouping the population into ten life stages. Elasticity analyses and simulations showed that removing the largest plant had the greatest control efficacy on new and old infestations in riparian and upland zones; despite subsequent mass recruitments. The model also accommodated for shocks without overcompensating. The alternative measure of plant abundance developed in this paper, provides a useful tool to assist in Woody-weed control decisions and provide a better measure of weed-control efficacy.
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Optimising the control of rangeland Woody Weeds.
2010Co-Authors: Andrew F. Zull, O. J. Cacho, R. A. Lawes, S. M. ZydenbosAbstract:There are very few studies that combine ecological population and economic optimisation models to establish integrated weed management (IWM) policies for Woody Weeds within rangeland grazing systems. This case study attempts to do so and uses a stochastic dynamic programming (SDP) model to determine the optimal weed management decisions for chinee apple (Ziziphus mauritiana) in northern Australian rangelands in order to maximise grazing profits. Model simulations were used to generate a weed management threshold frontier and decision rules, based on weed-free grazing gross margins and the cost of different control methods. The ecologicaleconomic optimising framework presented here can be used for many other long-lived plant species.
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Optimising Woody-weed control
2009Co-Authors: Andrew F. Zull, Oscar J. Cacho, Roger A. LawesAbstract:Woody Weeds pose significant threats to the 12.3 billion dollar Australian grazing industry. These Weeds reduce stocking rate, increase mustering effort, and impede cattle access to waterways. Two major concerns of Woody-weed management are the high cost of weed management with respect to grazing gross margins, and episodic seedling recruitments due to climatic conditions. This case study uses a Stochastic Dynamic Programming (SDP) model to determine the optimal weed management decisions for chinee apple (Ziziphus mauritiana) in northern Australian rangelands to maximise grazing profits. Weed management techniques investigated include: no-control, burning, poisoning, and mechanical removal (blade ploughing). The model provides clear weed management thresholds and decision rules, with respect to weed-free gross margins and weed management costs.
Wilma Matheson - One of the best experts on this subject based on the ideXlab platform.
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The use of integrated remotely sensed and GIS data to determine causes of vegetation cover change in southern Botswana
Applied Geography, 1996Co-Authors: Susan Ringrose, Cornelis Vanderpost, Wilma MathesonAbstract:Abstract The characteristics and dynamics of dry savanna vegetation cover are receiving considerable attention from the perspectives of both global change and range degradation studies. Problems include the establishment of major savanna determinants and the floristic response of vegetation cover to given stimuli. Basic work on determinants is required to assess the nature and causes of natural resource depletion, particularly in the Kalahari region. Use of image processing techniques involving the association of pixel values and field data have resulted in the development of a vegetation map indicating floristic content and structure. Results indicate that a clear distinction can be made between classes containing high proportions of taller woodland species and those that contain mainly Woody Weeds. Degraded areas with sparse vegetation cover and large areas of bare soil were also identified. The GIS technique of buffer analysis was applied to determine the extent to which herbivory (livestock) and the gathering of bush products by the local population were directly involved in the spatial distribution of savanna types. Results indicate that most of the degraded areas are within 2 km of villages and boreholes. Most of the Woody weed areas fall within a 2–4-km zone around boreholes. Spatial association indicates that uncontrolled bush product harvesting and goat grazing are primarily responsible for village-centred degradation, while cattle grazing around numerous boreholes is a primary cause of Woody weed development. These kinds of savanna adaptive responses are difficult to reverse in rural Botswana because of increasing population pressure and concomitant poverty.
Markus Immitzer - One of the best experts on this subject based on the ideXlab platform.
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Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya
Remote Sensing, 2017Co-Authors: Purity Rima, Kathrin Einzmann, Markus Immitzer, Clement Atzberger, Sandra EckertAbstract:Prosopis was introduced to Baringo, Kenya in the early 1980s for provision of fuelwood and for controlling desertification through the Fuelwood Afforestation Extension Project (FAEP). Since then, Prosopis has hybridized and spread throughout the region. Prosopis has negative ecological impacts on biodiversity and socio-economic effects on livelihoods. Vachellia tortilis, on the other hand, is the dominant indigenous tree species in Baringo and is an important natural resource, mostly preferred for wood, fodder and charcoal production. High utilization due to anthropogenic pressure is affecting the Vachellia populations, whereas the well adapted Prosopis—competing for nutrients and water—has the potential to replace the native Vachellia vegetation. It is vital that both species are mapped in detail to inform stakeholders and for designing management strategies for controlling the Prosopis invasion. For the Baringo area, few remote sensing studies have been carried out. We propose a detailed and robust object-based Random Forest (RF) classification on high spatial resolution Sentinel-2 (ten meter) and Pleiades (two meter) data to detect Prosopis and Vachellia spp. for Marigat sub-county, Baringo, Kenya. In situ reference data were collected to train a RF classifier. Classification results were validated by comparing the outputs to independent reference data of test sites from the “Woody Weeds” project and the Out-Of-Bag (OOB) confusion matrix generated in RF. Our results indicate that both datasets are suitable for object-based Prosopis and Vachellia classification. Higher accuracies were obtained by using the higher spatial resolution Pleiades data (OOB accuracy 0.83 and independent reference accuracy 0.87–0.91) compared to the Sentinel-2 data (OOB accuracy 0.79 and independent reference accuracy 0.80–0.96). We conclude that it is possible to separate Prosopis and Vachellia with good accuracy using the Random Forest classifier. Given the cost of Pleiades, the free of charge Sentinel-2 data provide a viable alternative as the increased spectral resolution compensates for the lack of spatial resolution. With global revisit times of five days from next year onwards, Sentinel-2 based classifications can probably be further improved by using temporal information in addition to the spectral signatures.
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Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya
MDPI AG, 2017Co-Authors: Purity Rima, Kathrin Einzmann, Markus Immitzer, Clement Atzberger, Sandra EckertAbstract:Prosopis was introduced to Baringo, Kenya in the early 1980s for provision of fuelwood and for controlling desertification through the Fuelwood Afforestation Extension Project (FAEP). Since then, Prosopis has hybridized and spread throughout the region. Prosopis has negative ecological impacts on biodiversity and socio-economic effects on livelihoods. Vachellia tortilis, on the other hand, is the dominant indigenous tree species in Baringo and is an important natural resource, mostly preferred for wood, fodder and charcoal production. High utilization due to anthropogenic pressure is affecting the Vachellia populations, whereas the well adapted Prosopis—competing for nutrients and water—has the potential to replace the native Vachellia vegetation. It is vital that both species are mapped in detail to inform stakeholders and for designing management strategies for controlling the Prosopis invasion. For the Baringo area, few remote sensing studies have been carried out. We propose a detailed and robust object-based Random Forest (RF) classification on high spatial resolution Sentinel-2 (ten meter) and Pléiades (two meter) data to detect Prosopis and Vachellia spp. for Marigat sub-county, Baringo, Kenya. In situ reference data were collected to train a RF classifier. Classification results were validated by comparing the outputs to independent reference data of test sites from the “Woody Weeds” project and the Out-Of-Bag (OOB) confusion matrix generated in RF. Our results indicate that both datasets are suitable for object-based Prosopis and Vachellia classification. Higher accuracies were obtained by using the higher spatial resolution Pléiades data (OOB accuracy 0.83 and independent reference accuracy 0.87–0.91) compared to the Sentinel-2 data (OOB accuracy 0.79 and independent reference accuracy 0.80–0.96). We conclude that it is possible to separate Prosopis and Vachellia with good accuracy using the Random Forest classifier. Given the cost of Pléiades, the free of charge Sentinel-2 data provide a viable alternative as the increased spectral resolution compensates for the lack of spatial resolution. With global revisit times of five days from next year onwards, Sentinel-2 based classifications can probably be further improved by using temporal information in addition to the spectral signatures
