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

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.

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

Remote Sensing ◽

10.3390/rs12010197 ◽

2020 ◽

Vol 12 (1) ◽

pp. 197

Author(s):

Debbie Chamberlain ◽

Stuart Phinn ◽

Hugh Possingham

Keyword(s):

Remote Sensing ◽

Time Series ◽

Land Cover ◽

Great Barrier Reef ◽

National Park ◽

Landsat 8 ◽

Barrier Reef ◽

Change Analysis ◽

Estuarine Wetland ◽

Open Forest

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.

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Land Cover-Specific Local Incidence Angle Correction: A Method for Time-Series Analysis of Forest Ecosystems

Remote Sensing ◽

10.3390/rs13091743 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1743

Author(s):

Daniel Paluba ◽

Josef Laštovička ◽

Antonios Mouratidis ◽

Přemysl Štych

Keyword(s):

Time Series ◽

Land Cover ◽

Forest Type ◽

Incidence Angle ◽

Correction Method ◽

Google Earth ◽

Forest Change ◽

Corine Land Cover ◽

Angle Correction ◽

Wide Range

This study deals with a local incidence angle correction method, i.e., the land cover-specific local incidence angle correction (LC-SLIAC), based on the linear relationship between the backscatter values and the local incidence angle (LIA) for a given land cover type in the monitored area. Using the combination of CORINE Land Cover and Hansen et al.’s Global Forest Change databases, a wide range of different LIAs for a specific forest type can be generated for each scene. The algorithm was developed and tested in the cloud-based platform Google Earth Engine (GEE) using Sentinel-1 open access data, Shuttle Radar Topography Mission (SRTM) digital elevation model, and CORINE Land Cover and Hansen et al.’s Global Forest Change databases. The developed method was created primarily for time-series analyses of forests in mountainous areas. LC-SLIAC was tested in 16 study areas over several protected areas in Central Europe. The results after correction by LC-SLIAC showed a reduction of variance and range of backscatter values. Statistically significant reduction in variance (of more than 40%) was achieved in areas with LIA range >50° and LIA interquartile range (IQR) >12°, while in areas with low LIA range and LIA IQR, the decrease in variance was very low and statistically not significant. Six case studies with different LIA ranges were further analyzed in pre- and post-correction time series. Time-series after the correction showed a reduced fluctuation of backscatter values caused by different LIAs in each acquisition path. This reduction was statistically significant (with up to 95% reduction of variance) in areas with a difference in LIA greater than or equal to 27°. LC-SLIAC is freely available on GitHub and GEE, making the method accessible to the wide remote sensing community.

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Socio-environmental land cover time series analysis of mining landscapes using Google Earth Engine and web-based mapping

Remote Sensing Applications Society and Environment ◽

10.1016/j.rsase.2020.100458 ◽

2020 ◽

pp. 100458

Author(s):

Michelle Li Ern Ang ◽

Dirk Arts ◽

Danielle Crawford ◽

Bonifacio V. Labatos ◽

Khanh Duc Ngo ◽

...

Keyword(s):

Time Series ◽

Land Cover ◽

Time Series Analysis ◽

Google Earth ◽

Cover Time ◽

Web Based ◽

Series Analysis ◽

Google Earth Engine

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Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine

Remote Sensing ◽

10.3390/rs11243023 ◽

2019 ◽

Vol 11 (24) ◽

pp. 3023 ◽

Cited By ~ 9

Author(s):

Shuai Xie ◽

Liangyun Liu ◽

Xiao Zhang ◽

Jiangning Yang ◽

Xidong Chen ◽

...

Keyword(s):

Time Series ◽

Land Cover ◽

Vegetation Index ◽

Time Series Data ◽

Land Cover Classification ◽

Google Earth ◽

Thematic Mapper ◽

Series Data ◽

Land Cover Mapping ◽

Google Earth Engine

The Google Earth Engine (GEE) has emerged as an essential cloud-based platform for land-cover classification as it provides massive amounts of multi-source satellite data and high-performance computation service. This paper proposed an automatic land-cover classification method using time-series Landsat data on the GEE cloud-based platform. The Moderate Resolution Imaging Spectroradiometer (MODIS) land-cover products (MCD12Q1.006) with the International Geosphere–Biosphere Program (IGBP) classification scheme were used to provide accurate training samples using the rules of pixel filtering and spectral filtering, which resulted in an overall accuracy (OA) of 99.2%. Two types of spectral–temporal features (percentile composited features and median composited monthly features) generated from all available Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data from the year 2010 ± 1 were used as input features to a Random Forest (RF) classifier for land-cover classification. The results showed that the monthly features outperformed the percentile features, giving an average OA of 80% against 77%. In addition, the monthly features composited using the median outperformed those composited using the maximum Normalized Difference Vegetation Index (NDVI) with an average OA of 80% against 78%. Therefore, the proposed method is able to generate accurate land-cover mapping automatically based on the GEE cloud-based platform, which is promising for regional and global land-cover mapping.

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Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition

Remote Sensing ◽

10.3390/rs12152411 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2411 ◽

Cited By ~ 2

Author(s):

Thanh Noi Phan ◽

Verena Kuch ◽

Lukas W. Lehnert

Keyword(s):

Time Series ◽

Random Forest ◽

Land Cover ◽

Land Cover Classification ◽

High Accuracy ◽

Google Earth ◽

Temporal Aggregation ◽

Google Earth Engine ◽

Time Series Images ◽

Land Cover Maps

Land cover information plays a vital role in many aspects of life, from scientific and economic to political. Accurate information about land cover affects the accuracy of all subsequent applications, therefore accurate and timely land cover information is in high demand. In land cover classification studies over the past decade, higher accuracies were produced when using time series satellite images than when using single date images. Recently, the availability of the Google Earth Engine (GEE), a cloud-based computing platform, has gained the attention of remote sensing based applications where temporal aggregation methods derived from time series images are widely applied (i.e., the use the metrics such as mean or median), instead of time series images. In GEE, many studies simply select as many images as possible to fill gaps without concerning how different year/season images might affect the classification accuracy. This study aims to analyze the effect of different composition methods, as well as different input images, on the classification results. We use Landsat 8 surface reflectance (L8sr) data with eight different combination strategies to produce and evaluate land cover maps for a study area in Mongolia. We implemented the experiment on the GEE platform with a widely applied algorithm, the Random Forest (RF) classifier. Our results show that all the eight datasets produced moderately to highly accurate land cover maps, with overall accuracy over 84.31%. Among the eight datasets, two time series datasets of summer scenes (images from 1 June to 30 September) produced the highest accuracy (89.80% and 89.70%), followed by the median composite of the same input images (88.74%). The difference between these three classifications was not significant based on the McNemar test (p > 0.05). However, significant difference (p < 0.05) was observed for all other pairs involving one of these three datasets. The results indicate that temporal aggregation (e.g., median) is a promising method, which not only significantly reduces data volume (resulting in an easier and faster analysis) but also produces an equally high accuracy as time series data. The spatial consistency among the classification results was relatively low compared to the general high accuracy, showing that the selection of the dataset used in any classification on GEE is an important and crucial step, because the input images for the composition play an essential role in land cover classification, particularly with snowy, cloudy and expansive areas like Mongolia.

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Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine

Remote Sensing ◽

10.3390/rs12172735 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2735 ◽

Cited By ~ 16

Author(s):

Carlos M. Souza ◽

Julia Z. Shimbo ◽

Marcos R. Rosa ◽

Leandro L. Parente ◽

Ane A. Alencar ◽

...

Keyword(s):

Land Use ◽

Time Series ◽

Land Cover ◽

Time Series Data ◽

Google Earth ◽

Series Data ◽

Data Series ◽

Forest Conversion ◽

Pixel Resolution ◽

Historical Land Use

Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.

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Agricultural Expansion in Mato Grosso from 1986–2000: A Bayesian Time Series Approach to Tracking Past Land Cover Change

Remote Sensing ◽

10.3390/rs12040688 ◽

2020 ◽

Vol 12 (4) ◽

pp. 688 ◽

Cited By ~ 3

Author(s):

Jacky Lee ◽

Jeffrey A. Cardille ◽

Michael T. Coe

Keyword(s):

Remote Sensing ◽

Time Series ◽

Land Cover ◽

Spatial Resolution ◽

Processing Speed ◽

Remote Sensing Data ◽

Google Earth ◽

Wide Audience ◽

Data Set ◽

Mato Grosso

Landsat 5 has produced imagery for decades that can now be viewed and manipulated in Google Earth Engine, but a general, automated way of producing a coherent time series from these images—particularly over cloudy areas in the distant past—is elusive. Here, we create a land use and land cover (LULC) time series for part of tropical Mato Grosso, Brazil, using the Bayesian Updating of Land Cover: Unsupervised (BULC-U) technique. The algorithm built backward in time from the GlobCover 2009 data set, a multi-category global LULC data set at 300 m resolution for the year 2009, combining it with Landsat time series imagery to create a land cover time series for the period 1986–2000. Despite the substantial LULC differences between the 1990s and 2009 in this area, much of the landscape remained the same: we asked whether we could harness those similarities and differences to recreate an accurate version of the earlier LULC. The GlobCover basis and the Landsat-5 images shared neither a common spatial resolution nor time frame, But BULC-U successfully combined the labels from the coarser classification with the spatial detail of Landsat. The result was an accurate fine-scale time series that quantified the expansion of deforestation in the study area, which more than doubled in size during this time. Earth Engine directly enabled the fusion of these different data sets held in its catalog: its flexible treatment of spatial resolution, rapid prototyping, and overall processing speed permitted the development and testing of this study. Many would-be users of remote sensing data are currently limited by the need to have highly specialized knowledge to create classifications of older data. The approach shown here presents fewer obstacles to participation and allows a wide audience to create their own time series of past decades. By leveraging both the varied data catalog and the processing speed of Earth Engine, this research can contribute to the rapid advances underway in multi-temporal image classification techniques. Given Earth Engine’s power and deep catalog, this research further opens up remote sensing to a rapidly growing community of researchers and managers who need to understand the long-term dynamics of terrestrial systems.

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Analysis of the mixture toxicity burden in 17 rivers in north eastern Australia – implications for the Great Barrier Reef.

10.7287/peerj.preprints.27046v1 ◽

2018 ◽

Author(s):

Francis Spilsbury ◽

Michael St.J Warne ◽

Thomas Backhaus

Keyword(s):

Water Quality ◽

Great Barrier Reef ◽

Mixture Toxicity ◽

World Heritage Site ◽

Barrier Reef ◽

Combined Toxicity ◽

Guideline Values ◽

Eastern Australia ◽

North Eastern ◽

The Individual

The Great Barrier Reef (GBR) is a protected ecosystem, listed as a UNESCO World Heritage site since 1981. It runs for approximately 3000km along the coastline in north-eastern Australia. A total of thirty-five major river basins discharge to the GBR and many transport large loads of pesticides, suspended sediment, nutrients from agricultural land. Over the past 6 years an extensive program has been conducted by the Queensland Government to monitor concentrations of 51 pesticides and their breakdown products in 17 rivers that discharge to the GBR. To explore the potential impact that the pesticides pose to the riverine environments and to the GBR we analysed the risk posed by the individual pesticides and their mixtures. Australia currently does not have water quality guidelines for 17 of the 38 pesticides detected. For those, we calculated ecotoxicity thresholds using a simplified version of the Australian methodology for determining water quality guideline values, based on species-sensitivity distributions. In all rivers, multiple pesticides were routinely detected at concentrations greater than their level of reporting. All rivers had at least one sample where the combined toxicity was greater than 1 toxic unit (TU), i.e. exposure situations where the total pesticide concentration exceeded acceptable levels. In a number of rivers more than 50% of samples had a combined toxicity greater than 1 TU. Average TU’s per river ranged from 13.47 to 0.10, with substantial fluctuations over the seasons but without clear trends between years. The patterns indicate that specific events such as severity of wet/dry seasons and cyclone events impact the combined toxicity found. We also found land use patterns affected the combined toxicity in the river ecosystems. In each of the rivers, 90% of the expected mixture toxicity was caused by only between 2 and 6 pesticides, although the individual pesticides that dominated the combined toxicity differed between rivers.

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Automated High-Resolution Time Series Mapping of Mangrove Forests Damaged by Hurricane Irma in Southwest Florida

Remote Sensing ◽

10.3390/rs12111740 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1740

Author(s):

Matthew J. McCarthy ◽

Brita Jessen ◽

Michael J. Barry ◽

Marissa Figueroa ◽

Jessica McIntosh ◽

...

Keyword(s):

Time Series ◽

High Resolution ◽

Land Cover ◽

Satellite Imagery ◽

Large Scale ◽

Detection Methods ◽

Mangrove Forests ◽

Wetland Ecosystems ◽

Southwest Florida ◽

Hurricane Irma

In September of 2017, Hurricane Irma made landfall within the Rookery Bay National Estuarine Research Reserve of southwest Florida (USA) as a category 3 storm with winds in excess of 200 km h−1. We mapped the extent of the hurricane’s impact on coastal land cover with a seasonal time series of satellite imagery. Very high-resolution (i.e., <5 m pixel) satellite imagery has proven effective to map wetland ecosystems, but challenges in data acquisition and storage, algorithm training, and image processing have prevented large-scale and time-series mapping of these data. We describe our approach to address these issues to evaluate Rookery Bay ecosystem damage and recovery using 91 WorldView-2 satellite images collected between 2010 and 2018 mapped using automated techniques and validated with a field campaign. Land cover was classified seasonally at 2 m resolution (i.e., healthy mangrove, degraded mangrove, upland, soil, and water) with an overall accuracy of 82%. Digital change detection methods show that hurricane-related degradation was 17% of mangrove forest (~5 km2). Approximately 35% (1.7 km2) of this loss recovered one year after Hurricane Irma. The approach completed the mapping approximately 200 times faster than existing methods, illustrating the ease with which regional high-resolution mapping may be accomplished efficiently.

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Incorporating the Plant Phenological Trajectory into Mangrove Species Mapping with Dense Time Series Sentinel-2 Imagery and the Google Earth Engine Platform

Remote Sensing ◽

10.3390/rs11212479 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2479 ◽

Cited By ~ 5

Author(s):

Huiying Li ◽

Mingming Jia ◽

Rong Zhang ◽

Yongxing Ren ◽

Xin Wen

Keyword(s):

Time Series ◽

Vegetation Index ◽

Google Earth ◽

Late Winter ◽

Mangrove Forests ◽

Mangrove Species ◽

Ndvi Time Series ◽

Google Earth Engine ◽

Species Mapping ◽

Sentinel 2

Information on mangrove species composition and distribution is key to studying functions of mangrove ecosystems and securing sustainable mangrove conservation. Even though remote sensing technology is developing rapidly currently, mapping mangrove forests at the species level based on freely accessible images is still a great challenge. This study built a Sentinel-2 normalized difference vegetation index (NDVI) time series (from 2017-01-01 to 2018-12-31) to represent phenological trajectories of mangrove species and then demonstrated the feasibility of phenology-based mangrove species classification using the random forest algorithm in the Google Earth Engine platform. It was found that (i) in Zhangjiang estuary, the phenological trajectories (NDVI time series) of different mangrove species have great differences; (ii) the overall accuracy and Kappa confidence of the classification map is 84% and 0.84, respectively; and (iii) Months in late winter and early spring play critical roles in mangrove species mapping. This is the first study to use phonological signatures in discriminating mangrove species. The methodology presented can be used as a practical guideline for the mapping of mangrove or other vegetation species in other regions. However, future work should pay attention to various phenological trajectories of mangrove species in different locations.

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