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

Abstract

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 * Corresponding author. D. N. Kuria et al. 34 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.

Combining Spectral Data and a DSM from UAS-Images for Improved Classification of Non-Submerged Aquatic Vegetation

“…A UAS-DSM derived from the infrared images was used for an initial classification which was then refined using spectral information from true-colour UAS-images, among other parameters. Kuria et al [19] analysed seasonal vegetation changes in a Tanzanian wetland, based on 0.8 m spatial resolution true-colour image data acquired with a UAS, a DSM derived from UAS-images, and commercial radar data. Thirteen land cover classes were identified, including several classes with emergent aquatic vegetation.…”

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

“…In March, the region is dry and thus backscatter contribution is increased by attenuation of low vegetation cover as this is the period just before the onset of the April rains. During the wetter season in September, soil moisture is underestimated due to influence of maturing vegetation [1]. In the presence of vegetation, the soil is sheltered and thus moisture retrieved was less than the actual soil moisture.…”

“…None of the above parameters were available for this study, thus the RGVI was used in this research to decouple the contribution from vegetation and soil. UAV photos were used to obtain this surrogate vegetation index [1]. Equation 8 shows the …”

“…However, with the increase in global population, there is growing need for increased food production. Consequently, this translates to habitation of the wetlands with increased influx occurring in the dry season when there is low water availability [1]. Soil moisture content has been reported as being an important variable with respect to influencing climate change dynamics and other hydrological applications [2] [3].…”

Definitions and Mapping of East African Wetlands: A Review

“…Some are from the project titled Agricultural Use and Vulnerability of Small Wetlands in East Africa (SWEA), which was concluded in 2013. During the first phase of this project, 51 wetlands smaller in size than 500 ha were identified within Kenya and Tanzania [111]. Mwita et al [112,113] mapped a number of these in detail by applying TerraSAR-X and RapidEye high spatial resolution remotely-sensed data.…”