Mangrove Forest Cover and Phenology with Landsat Dense Time Series in Central Queensland, Australia

Abstract

Wetlands are one of the most biologically productive ecosystems. Wetland ecosystem services, ranging from provision of food security to climate change mitigation, are enormous, far outweighing those of dryland ecosystems per hectare. However, land use change and water regulation infrastructure have reduced connectivity in many river systems and with floodplain and estuarine wetlands. Mangrove forests are critical communities for carbon uptake and storage, pollution control and detoxification, and regulation of natural hazards. Although the clearing of mangroves in Australia is strictly regulated, Great Barrier Reef catchments have suffered landscape modifications and hydrological alterations that can kill mangroves. We used remote sensing datasets to investigate land cover change and both intra- and inter-annual seasonality in mangrove forests in a large estuarine region of Central Queensland, Australia, which encompasses a national park and Ramsar Wetland, and is adjacent to the Great Barrier Reef World Heritage site. We built a time series using spectral, auxiliary, and phenology variables with Landsat surface reflectance products, accessed in Google Earth Engine. Two land cover classes were generated (mangrove versus non-mangrove) in a Random Forest classification. Mangroves decreased by 1480 hectares (−2.31%) from 2009 to 2019. The overall classification accuracies and Kappa coefficient for 2008–2010 and 2018–2020 land cover maps were 95% and 95%, respectively. Using an NDVI-based time series we examined intra- and inter-annual seasonality with linear and harmonic regression models, and second with TIMESAT metrics of mangrove forests in three sections of our study region. Our findings suggest a relationship between mangrove growth phenology along with precipitation anomalies and severe tropical cyclone occurrence over the time series. The detection of responses to extreme events is important to improve understanding of the connections between climate, extreme weather events, and biodiversity in estuarine and mangrove ecosystems.

Turning the Tide on Mapping Marginal Mangroves with Multi-Dimensional Space–Time Remote Sensing

“…This study used a range of spectral indices to model mangrove habitat, with MNDWI, followed by GCVI as the variables with the most importance for the models. GCVI represents a vegetation index and as such has been commonly used for mapping mangroves [14,27]. The MNDWI is an index more commonly used in mapping water bodies [26], however has been utilised more recently as part of other mangrove combined indices mapping [26].…”

“…Indices provide a method to extract useful information about the environment from satellite data [24]; in mangroves, relevant indices are those that provide information on green vegetation (red and near-infrared bands [25]) and water reflectance (green and middle infrared [26]). For each yearly composite, four image indices were developed that have been previously used in mapping mangroves (e.g., [1,12,15,18]): (i) NDVI-the normalised difference values for Band 5 (0.85-0.88 µm) divided by Band 4 (0.64-0.67 µm), which measures the absorbance of chlorophyll in the red band and the reflection of the mesophyll in the near-infrared band [25], (ii) MNDWI [26])-the normalised difference values for Band 3 (0.53-0.59 µm) divided by Band 6 (1.57-1.65 µm), which maximises the water reflectance with the green band and minimises noise from land using the middle infrared band [26], (iii) SR (Simple Ratio)-Band 5 (0.85-0.88 µm) divided by Band 4 (0.64-0.67 µm), which also uses the red and near-infrared bands to detect green vegetation and (iv) GCVI-Green Chlorophyll Vegetation Index ((Band 5 (0.85-0.88 µm)/bands 3 (0.53-0.59 µm))-1, which estimate the green leaf biomass [27]. The resulting bands were also masked using NASA's Shuttle Radar Topography Mission (SRTM) v3 30-metre digital elevation layer [28] and based on the NDVI and MNDWI values.…”

Vegetation-Water-Built Up Index Combined: Algorithm Indices Combination for Characterization and distribution of Mangrove Forest through Google Earth Engine

“…When viewed from its ability, the MNDWI index is an index that is formulated from water-sensitive SWIR and Green bands (Xu 2006;Chen et al 2017). Hence, these indices were often combined in mangrove monitoring or vegetation that influences water through satellite imagery (Jia et al 2019;Wang et al 2018;Chen et al 2017;Chamberlain et al 2021), aerial portrait of both UAVs and a combination of the two (Wang et al 2019). In contrast to the NDWI index, which has low sensitivity to the spectral characteristics reflected by vegetation under the influence of water (Dennison et al 2005).…”

Estimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data

“…The NDVI is one of the most commonly used vegetation indices in optical remote sensing, which is based on the intrinsic characteristics of leaf absorbance in the red region and high reflectance in the near-infrared (NIR) region [31][32][33][34]57]. It is highly sensitive to the biochemical and structural composition of the canopy and is characterized by a light computation load [6,31].…”

“…However, when spectral data observed throughout a year are calculated according to the ratio of bands, with the construction of the phenological change curve according to the period, the phenological characteristics are obvious [29,30]. Vegetation indices (VIs) with time-series information, especially the normalized difference vegetation index (NDVI) [31], have been widely used in the identification and classification of forest types and tree species [32][33][34]. Remote sensing time-series data used in previous studies have mainly been stacked by multispectral data, which are usually based on plantations with regular distributions and simple stand structures.…”