| dc.creator |
Kuria, David N. |
|
| dc.creator |
Menz, Gunter |
|
| dc.creator |
Misana, Salome |
|
| dc.creator |
Mwita, Emiliana |
|
| dc.creator |
Thamm, Hans P. |
|
| dc.creator |
Alvarez, Miguel |
|
| dc.creator |
Mogha, Neema |
|
| dc.creator |
Becker, M. |
|
| dc.creator |
Oyieke, Helida |
|
| dc.date |
2016-09-21T13:13:49Z |
|
| dc.date |
2016-09-21T13:13:49Z |
|
| dc.date |
2014-03 |
|
| dc.date.accessioned |
2018-03-27T09:09:43Z |
|
| dc.date.available |
2018-03-27T09:09:43Z |
|
| dc.identifier |
Kuria, D.N., Menz, G., Misana, S., Mwita, E., Thamm, H.P., Alvarez, M., Mogha, N., Becker, M. and Oyieke, H., 2014. Seasonal vegetation changes in the Malinda Wetland using bi-temporal, multi-sensor, very high resolution remote sensing data sets. Advances in Remote Sensing, 3(01), p.33. |
|
| dc.identifier |
http://hdl.handle.net/20.500.11810/3906 |
|
| dc.identifier |
10.4236/ars.2014.31004 |
|
| dc.identifier.uri |
http://hdl.handle.net/20.500.11810/3906 |
|
| dc.description |
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 resolu-tion remote sensing approaches overcome this limitation allowing detailed inventorying and re-search 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. |
|
| dc.language |
en |
|
| dc.subject |
Image Fusion |
|
| dc.subject |
Land Cover Classification |
|
| dc.subject |
Unmanned Aerial Vehicle |
|
| dc.subject |
Change Vector Analysis |
|
| dc.subject |
Land Cover Change |
|
| dc.subject |
Vegetation Phenology |
|
| dc.title |
Seasonal Vegetation Changes in the Malinda Wetland Using Bi-Temporal, Multi-Sensor, Very High Resolution Remote Sensing Data Sets |
|
| dc.type |
Journal Article, Peer Reviewed |
|