Prosopis invasion in the Northern Cape: remote sensing analysis of management action effectiveness
Date
2021-12
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Stellenbosch : Stellenbosch University
Abstract
ENGLISH ABSTRACT: Large-scale land acquisitions (LSLAs) for agricultural sector have grown significantly in the
past decade, and are mostly prevalent in developing countries. Because LSLAs are not without
negative effects on the environment and local communities, and because information about
them is scarce and difficult to obtain, systems allowing LSLAs detection, characterization and
monitoring in space and time are needed. With the increasing availability of global satellite
data products, technological development in cloud computing, image and data mining
analysis, remote sensing has evolved to an interesting tool for the detection and
characterization of changes in land use systems.
This study presents a novel approach to generically detect and characterize LSLAs at regional
spatial extents. In order to capture and analyse the full range of land use spectral and spatial
signatures related to agricultural LSLAs, this study is based on a 2-level data driven approach
(Self-Organizing Maps followed by a clustering algorithm), consisting of two phases: 1) land
use/land cover change detection at regional scale within dense temporal stacks of vegetation
indices (MODIS-NDVI, 250m) and 2), discrimination of different land use/land cover classes
using a set of spectral vegetation indices, textural features and shape metrics computed from
landscape-extracted objects (Landsat-8, 30m). Evaluation of the methodology is performed
against a ground truth database on LSLAs in Senegal.
Results obtained during this exploratory research, are promising and provide some insights
in agricultural LSLAs in the northern half of Senegal. With a very limited number of
discriminative features (consisting of two Vegetation Indices and two textural features),
detection of agricultural LSLAs is possible. Recommendations are given for enhancement of
the generalization performance of the unsupervised classifier.
AFRIKAANSE OPSOMMING: Geen Afrikaanse opsomming beskikbaar.
AFRIKAANSE OPSOMMING: Geen Afrikaanse opsomming beskikbaar.
Description
Thesis (MSc)--Stellenbosch University, 2021.
Keywords
Mesquite, Prosopis, Invasive plants, Remote sensing, Google Earth, Legumes, Plant introduction -- South Africa -- Northern Cape, Multispectral imaging, Geometry -- Data processing, Machine learning, Algorithms, UCTD