Other health projects focus on detecting and mapping physical damage of vegetation by pests such as the
Gonipterus scutellatus weevil and mopane worms using remotely sensed data.
Vegetation Mapping
In Southern Africa, natural forest and rangeland resources form an important resource base for food and medicinal products that form part of people’s subsistence as well as their economic base and well-being. Recent studies has however showed that the long term sustainability of the natural forest in Southern Africa and its resources is under threat because of lack of information on the distribution of the species. Mapping tree species in the natural forest of Southern Africa is therefore extremely important for forest management purposes. Earth observation and GIS provides a cost-effective tool to help forest managers better understand forest characteristics, such as forest area, locations, species, even down to the level of characterizing individual trees and health status.
Recent advancements in remotely sensed technology, such as hyper spectral and new multispectral sensors such as Worldview with strategically positioned bands have encouraged the development of more robust approaches towards forest species mapping including the discrimination of alien invasive plants within commercial forest ecosystems. This research group has conducted a number of vegetation mapping projects using such tools, ranging from mopane discrimination, brackern fern mapping, rangeland grass species mapping to commercial pine varieties mapping.
Assessing Distribution and Quality of Vegetation Species |
|
Global climate change is expected to cause shifts in the percentage cover and abundance of grass species following C3 and C4 photosynthetic pathways. These two groups differ in a number of physiological, structural and biochemical properties. Remote sensing high spectral and spatial resolution systems have been explored to detect, classify and map C3 and C4 grasses. This study also investigated an innovative method to address the challenges associated with spectral dimensionality and the related multicollinearity in the spectral response of vegetation. It involves developing a user-defined inter-band correlation filter function to resample hyperspectral data. The utility of the new resampling technique was assessed for discriminating C3 and C4 grasses and for predicting their nutrient content (nitrogen, protein, moisture, and fibre) using field spectroscopy and Worldview-2 images in the Cathedral Peak region of the Drakensberg Mountains |