Landscape configuration and composition trends of indigenous forest using multi- and hyperspectral imagery : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Geography at Massey University, Manawatu, New Zealand
Loading...
Date
2023
DOI
Open Access Location
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Massey University
Rights
The Author
Abstract
Historical land use in New Zealand led to the widespread destruction of indigenous forests, creating a complex landscape matrix within which forest fragments now survive. Legislative changes have effectively halted large-scale deforestation, yet small-scale fragmentation continues to occur. This negatively effects numerous ecological processes vital in maintaining healthy forest structure and function. To monitor fragmentation, state of the environment reports often quantify area and percentage of landscape change. While these metrics are important, they are poorly suited to quantifying the spatial configuration and distribution of forest fragments. To quantify the spatial arrangement of indigenous forest remnants, two case studies were undertaken at different spatial and temporal scales. The first employed low-resolution (246 m²), multispectral derived imagery from the Climate Change Initiative - Land Cover (CCI-LC) dataset to quantify regional fragmentation over a 28-year period. Landscape analysis was performed with FRAGSTATS and revealed both spatial and temporal variability in the arrangement of forest patches. This was evidenced by periods of fragmented growth and decline, along with periods of infilling in some regions. Despite several regions recording a net increase in forest area, the overall trend was towards greater disaggregation and fragmentation. However, it is essential to exercise caution when interpreting these findings as the coarse resolution of the CCI-LC dataset may not adequately describe fragmentation at the regional level. The second case study employed high-resolution (1 m²), hyperspectral imagery to quantify forest fragmentation on a rural property. Imagery was captured with the AISA FENIX hyperspectral camera and atmospherically corrected using a pixel-wise radiative transfer model. Land cover was classified with a one-dimensional convolutional neural network and landscape configuration was assessed with FRAGSTATS. The results were compared to the medium-resolution (1 ha nominal mapping unit) Landcover Database (LCDB) and the low-resolution CCI-LC. Greater accuracy in both land cover classification and definition were achieved with the hyperspectral imagery. Edge-perimeter and connectivity metrics were also substantially improved. Management strategies seeking to reduce fragmentation should consider the use of high-resolution, hyperspectral imagery in conjunction with landscape metrics to improve classification accuracy and precision.
Description
Figure 2 is reproduced under a Creative Commons Attribution 3.0 Unported (CC BY 3.0) license.