Remote Sensing of Mangroves and Estuarine Communities in Central Queensland, Australia

Abstract

Great Barrier Reef catchments are under pressure from the effects of climate change, landscape modifications, and hydrology alterations. With the use of remote sensing datasets covering large areas, conventional methods of change detection can expose broad transitions, whereas workflows that excerpt data for time-series trends divulge more subtle transformations of land cover modification. Here, we combine both these approaches to investigate change and trends in a large estuarine region of Central Queensland, Australia, that encompasses a national park and is adjacent to the Great Barrier Reef World Heritage site. Nine information classes were compiled in a maximum likelihood post classification change analysis in 2004–2017. Mangroves decreased (1146 hectares), as was the case with estuarine wetland (1495 hectares), and saltmarsh grass (1546 hectares). The overall classification accuracies and Kappa coefficient for 2004, 2006, 2009, 2013, 2015, and 2017 land cover maps were 85%, 88%, 88%, 89%, 81%, and 92%, respectively. The cumulative area of open forest, estuarine wetland, and saltmarsh grass (1628 hectares) was converted to pasture in a thematic change analysis showing the “from–to” change. We generated linear regression relationships to examine trends in pixel values across the time series. Our findings from a trend analysis showed a decreasing trend (p value range = 0.001–0.099) in the vegetation extent of open forest, fringing mangroves, estuarine wetlands, saltmarsh grass, and grazing areas, but this was inconsistent across the study site. Similar to reports from tropical regions elsewhere, saltmarsh grass is poorly represented in the national park. A severe tropical cyclone preceding the capture of the 2017 Landsat 8 Operational Land Imager (OLI) image was likely the main driver for reduced areas of shoreline and stream vegetation. Our research contributes to the body of knowledge on coastal ecosystem dynamics to enable planning to achieve more effective conservation outcomes.

Impacts of the Tropical Cyclone Idai in Mozambique: A Multi-Temporal Landsat Satellite Imagery Analysis

“…Although this was not a ground data-based study because of the well-known difficulties of field research (e.g., time-consuming, limited availability of human and logistic resources), our results are realistic and they were satisfactorily verified with the local damage reports [12]. In fact, Landsat image analyses provided reliable results, and have been widely and successfully used to map LULC damages from cyclones [16,24,27,77]. This study can be employed as a future reference in related studies.…”

Determining Changes in Mangrove Cover Using Remote Sensing with Landsat Images: a Review

“…Another alternative for the limitation described is that optical remotely sensed data such as Landsat and Sentinel provide time series images that provide more options for avoiding these effects, such as their temporal resolution which allows selecting images in seasons where tide levels are similar (Chamberlain et al, 2020) (Das & Dhorde, 2021).…”

Recent Advancement in Mangrove Forests Mapping and Monitoring of the World Using Earth Observation Satellite Data

“…Chamberlain et al [12] combined remote sensing change analysis approach and conventional method of change detection to detect subtle transformations of land cover modification in a large estuarine region of Queensland, Australia. Landsat satellite data acquired in 2004, 2006, 2009, 2013, 2015 and 2017 were used for the classification and change analysis.…”