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Monitoring Land-Use Change in Nakuru (Kenya) Using Multi-Sensor Satellite Data

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Advances in Remote Sensing 2012年第3期1卷 74-84页

作者： Kenneth Mubea gunter menz Center for Remote Sensing of Land surfaces (ZFL) University of Bonn Bonn Germany Remote Sensing Research Group (RSRG) University of Bonn Bonn Germany

Recently land-use change has been the main concern for worldwide environment change and is being used by city and regional planners to design sustainable cities. Nakuru in the central Rift Valley of Kenya has undergone rapid urban growth in last decade. This paper focused on urban growth using multi-sensor satellite imageries and explored the potential benefits of combining data from optical sensors (Landsat, Worldview-2) with Radar sensor data from Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data for urban land-use mapping. Landsat has sufficient spectral bands allowing for better delineation of urban green and impervious surface, Worldview-2 has a higher spatial resolution and facilitates urban growth mapping while PALSAR has higher temporal resolution compared to other operational sensors and has the capability of penetrating clouds irrespective of weather conditions and time of day, a condition prevalent in Nakuru, because it lies in a tropical area. Several classical and modern classifiers namely maximum likelihood (ML) and support vector machine (SVM) were applied for image classification and their performance assessed. The land-use data of the years 1986, 2000 and 2010 were compiled and analyzed using post classification comparison (PCC). The value of combining multi-temporal Landsat imagery and PALSAR was explored and achieved in this research. Our research illustrated that SVM algorithm yielded better results compared to ML. The integration of Landsat and ALOS PALSAR gave good results compared to when ALOS PAL- SAR was classified alone. 19.70 km2 of land changed to urban land-use from non-urban land-use between the years 2000 to 2010 indicating rapid urban growth has taken place. Land-use information is useful for the comprehensive land-use planning and an integrated management of resources to ensure sustainability of land and to achieve social Eq- uity, economic efficiency and environmental sustainabi

关键词： Land-Use Monitoring Nakuru Urban Growth Multi-Sensors Satellite Data Maximum Likelihood Support Vector Machine Post Classification Comparison Sustainability

Detection of Small Wetlands with Multi Sensor Data in East Africa

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Advances in Remote Sensing 2012年第3期1卷 64-73页

作者： Emiliana Mwita gunter menz Salome Misana Pamela Nienkemper Center for Development Research (ZEF) Bonn Germany Department of Geography Dares Salaam University College of Education Dares es Salaam Tanzania Department of Geography Remote Sensing Research Group University of Bonn Bonn Germany Department of Geography University of Dar es Salaam Dares es Salaam Tanzania

The dynamic nature and inaccessibility of wetland ecosystems restricts in situ data collection and promote the use of various remote sensing platforms. This is because of their ability to record large areas in comparatively short time periods and map physically unreachable areas. Sensors in the optical and microwave range of the electromagnetic spectrum play a critical role in wetlands detection and delineation, as they complement each other in data collection. This study examined the potential of optical and microwave remote sensing in detecting the diversity of small wetlands ( ha) in the semi-arid and sub humid parts of Laikipia and Pangani plains and the humid parts of Mt. Kenya and Usambara highlands in Kenya and Tanzania, respectively. An intensive field survey was conducted to supplement the remotely sensed data. Decision tree, supervised and unsupervised classification techniques, facilitated the detection of floodplains and inland valley wetlands within the study sites. The results reveal that although optical and microwave data work effectively in the detection of wetlands the latter would be more effective in larger wetlands than those in the scope of this study.

关键词： Wetlands Delineation Microwave Optical Kenya Tanzania

Separability of Dominant Crop Cultures in Southern Germany Using TerraSAR-X Data

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Advances in Remote Sensing 2015年第2期4卷 97-107页

作者： Kuria Thiong’o René Pasternak Alfred Kleusberg Frank Thonfeld gunter menz Department of Geography Bonn University Bonn Germany Institute of Navigation Stuttgart University Stuttgart Germany Center for Remote Sensing of Land Surfaces Bonn University Bonn Germany

The research aims at differentiating dominant crop cultures in two test sites of Baden-Wuert- temberg, Southern Germany by creating crop signatures from radar backscatter values. It seeks to establish whether the crop signatures collected in one test site are comparable or transferable to another test site. The two test sites are located in different agro-ecological zones as described in the climate maps of the “Klimaatlas Baden-Wuerttemberg”. TerraSAR-X images (VV polarization) for the months of July and August 2010 were overlaid with crop fields’ ground truth data. As pre-processing steps, radiometric correction was carried out on the images in order to normalize the topographical effects. Classification of the crops was performed on a field scale, according to the mean and standard deviation of their backscatter values. From the results, potatoes could be uniquely differentiated from the cereals in the two different test sites for both the months of July and August 2010. Cereals (rapes, maize, barley, wheat and oats) had comparable backscatter values and their differentiation varied from one test site to another. The results’ accuracy obtained with a maximum kappa coefficient of 0.82 agrees with results of a similar research carried out in North East Germany.

关键词： TerraSAR-X Radar Backscatter Crop Signatures Crop Differentiation

Influence of Vegetation Cover on the Oh Soil Moisture Retrieval Model: A Case Study of the Malinda Wetland, Tanzania

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Advances in Remote Sensing 2016年第1期5卷 28-42页

作者： Fridah Kirimi David N. Kuria Frank Thonfeld Esther Amler Kenneth Mubea Salome Misana gunter menz Department of Geography University of Bonn Bonn Germany Institute of Geomatics GIS & Remote Sensing Dedan Kimathi University of Technology Nyeri Kenya Department of Geography University of Dar es Salaam Dar es Salaam Tanzania

Soil moisture is an important parameter that drives agriculture, climate and hydrological systems. In addition, retrieval of soil moisture is important in the analysis as well as its influence on these systems. Radar imagery is best suited for this retrieval due to its all-weather capability and independence from solar irradiation. Soil moisture retrieval was done for the Malinda Wetland, Tanzania, during two time periods, March and September 2013. The aim of this paper was to analyze soil moisture retrieval performance when vegetation contribution is taken into account. Backscatter values were obtained from TerraSAR-X Spotlight mode imagery taken in March and September 2013. The backscatter values recorded by SAR imagery are influenced by vegetation, soil roughness and soil moisture. Thus, in order to obtain the backscatter due to soil moisture, the roughness and vegetation contribution are determined and decoupled from total backscatter. The roughness parameters were obtained from a Digital Surface Model (DSM) from Unmanned Aerial Vehicle (UAV) photographs whereas the vegetation parameter was obtained by inverting the Water Cloud Model (WCM). Lastly, soil moisture was retrieved using the Oh Model. The coefficient of correlation between the observed and retrieved was 0.39 for the month of March and 0.65 in the month of August. When the vegetation contribution was considered, the r2 for March was 0.64 and that in August was 0.74. The results revealed that accounting for vegetation improved soil moisture retrieval.

关键词： Surface Soil Moisture Oh Model Water Cloud Model Wetland TerraSAR-X

Seasonal Vegetation Changes in the Malinda Wetland Using Bi-Temporal, Multi-Sensor, Very High Resolution Remote Sensing Data Sets

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Advances in Remote Sensing 2014年第1期3卷 33-48页

作者： David N. Kuria gunter menz Salome Misana Emiliana Mwita Hans-Peter Thamm Miguel Alvarez Neema Mogha Mathias Becker Helida Oyieke Department of Geography Dar es Salaam University College of Technology Dar es Salaam Tanzania Department of Geography Free University Berlin Malteserstr Germany Department of Geography University of Bonn Meckenheimer Allee Bonn Germany Department of Geography University of Dar es Salaam Dar es Salaam Tanzania Institute of Crop Science and Resource Conservation University of Bonn Katzenburgweg Germany Institute of Geomatics GIS & Remote Sensing Dedan Kimathi University of Technology Nyeri Kenya National Museums of Kenya Nairobi Kenya

Small wetlands in East Africa have grown in prominence driven by the unreliable and diminished rains and the increasing population pressure. Due to their size (less than 500 Ha), these wetlands have not been studied extensively using satellite remote sensing approaches. High spatial resolution remote sensing approaches overcome this limitation allowing detailed inventorying and research on such small wetlands. For understanding the seasonal variations in land cover within the Malinda Wetland in Tanzania (350 Ha), two periods were considered, May 2012 coinciding with the wet period (rainy season) and August 2012 coinciding with a fairly rain depressed period (substantially dry but generally cooler season). The wetland was studied using very high spatial resolution orthophotos derived from Unmanned Aerial Vehicle (UAV) photography fused with TerraSAR-X Spotlight mode dual polarized radar data. Using these fused datasets, five main classes were identified that were used to firstly delineate seasonal changes in land use activities and secondly used in determining phenology changes. Combining fuzzy maximum likelihood classification, knowledge classifier and Change Vector Analysis (CVA), land cover classification was undertaken for both seasons. From the results, manifold anthropogenic activities are taking place between the seasons as evidenced by the high conversion rates (63.01 Ha). The phenological change was also highest within the human influence class due to the growing process of cropped land (26.60 Ha). Much of the changes in both cover and phenology are occurring in the mid upper portion of the wetland, attributed to the presence of springs in this portion of the wetland along the banks of River Mkomazi. There is thus seasonality in the observed anthropogenic influence between the wetland and its periphery.

关键词： Image Fusion Land Cover Classification Unmanned Aerial Vehicle Change Vector Analysis Land Cover Change Vegetation Phenology

Monitoring Land-Use Change in Nakuru (Kenya) Using Multi-Sensor Satellite Data

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Detection of Small Wetlands with Multi Sensor Data in East Africa

Advances in Remote Sensing（遥感技术进展） 2012年第3期

作者： Kenneth Mubea gunter menz

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