Land Cover Mapping using Digital Earth Australia

This study establishes the use of the Earth Observation Data for Ecosystem Monitoring (EODESM) to generate land cover and change classifications based on the United Nations Food and Agriculture Organisation (FAO) Land Cover Classification System (LCCS) and environmental variables (EVs) available within, or accessible from, Geoscience Australia’s (GA) Digital Earth Australia (DEA). Classifications representing the LCCS Level 3 taxonomy (8 categories representing semi-(natural) and/or cultivated/managed vegetation or natural or artificial bare or water bodies) were generated for two time periods and across four test sites located in the Australian states of Queensland and New South Wales. This was achieved by progressively and hierarchically combining existing time-static layers relating to (a) the extent of artificial surfaces (urban, water) and agriculture and (b) annual summaries of EVs relating to the extent of vegetation (fractional cover) and water (hydroperiod, intertidal area, mangroves) generated through DEA. More detailed classifications that integrated information on, for example, forest structure (based on vegetation cover (%) and height (m); time-static for 2009) and hydroperiod (months), were subsequently produced for each time-step. The overall accuracies of the land cover classifications were dependent upon those reported for the individual input layers, with these ranging from 80% (for cultivated, urban and artificial water) to over 95% (for hydroperiod and fractional cover). The changes identified include mangrove dieback in the southeastern Gulf of Carpentaria and reduced dam water levels and an associated expansion of vegetation in Lake Ross, Burdekin. The extent of detected changes corresponded with those observed using time-series of RapidEye data (2014 to 2016; for the Gulf of Carpentaria) and Google Earth imagery (2009–2016 for Lake Ross). This use case demonstrates the capacity and a conceptual framework to implement EODESM within DEA and provides countries using the Open Data Cube (ODC) environment with the opportunity to routinely generate land cover maps from Landsat or Sentinel-1/2 data, at least annually, using a consistent and internationally recognised taxonomy.

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An Operational Analysis Ready Radar Backscatter Dataset for the African Continent

Remote Sensing ◽

10.3390/rs14020351 ◽

2022 ◽

Vol 14 (2) ◽

pp. 351

Author(s):

Fang Yuan ◽

Marko Repse ◽

Alex Leith ◽

Ake Rosenqvist ◽

Grega Milcinski ◽

...

Keyword(s):

Open Data ◽

Data Cube ◽

Observation Data ◽

Cloud Infrastructure ◽

Resource Monitoring ◽

Radar Backscatter ◽

Digital Earth ◽

Continental Scale ◽

Wide Range ◽

Natural Resource Monitoring

Digital Earth Africa is now providing an operational Sentinel-1 normalized radar backscatter dataset for Africa. This is the first free and open continental scale analysis ready data of this kind that has been developed to be compliant with the CEOS Analysis Ready Data for Land (CARD4L) specification for normalized radar backscatter (NRB) products. Partnership with Sinergise, a European geospatial company and Earth observation data provider, has ensured this dataset is produced efficiently in the cloud infrastructure and can be sustained in the long term. The workflow applies radiometric terrain correction (RTC) to the Sentinel-1 ground range detected (GRD) product, using the Copernicus 30 m digital elevation model (DEM). The method is used to generate data for a range of sites around the world and has been validated as producing good results. This dataset over Africa is made available publicly as a AWS public dataset and can be accessed through the Digital Earth Africa platform and its Open Data Cube API. We expect this dataset to support a wide range of applications, including natural resource monitoring, agriculture, and land cover mapping across Africa.

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Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park

Remote Sensing ◽

10.3390/rs13122299 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2299

Author(s):

Andrea Tassi ◽

Daniela Gigante ◽

Giuseppe Modica ◽

Luciano Di Martino ◽

Marco Vizzari

Keyword(s):

Land Cover ◽

National Park ◽

Time Window ◽

Central Italy ◽

Google Earth ◽

Data Cube ◽

Landsat 8 ◽

Change Analysis ◽

Object Based ◽

Google Earth Engine

With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area.

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Assimilation of inundation extent observations into a flood forecasting system: a tempered particle filter for combatting degeneracy and sample impoverishment.

10.5194/egusphere-egu21-10878 ◽

2021 ◽

Author(s):

Concetta Di Mauro ◽

Renaud Hostache ◽

Patrick Matgen ◽

Peter Jan van Leeuwen ◽

Nancy Nichols ◽

...

Keyword(s):

Particle Filter ◽

Main Idea ◽

Water Levels ◽

Smooth Transition ◽

Observation Data ◽

Flood Inundation ◽

State Variables ◽

Time Step ◽

Radar Images ◽

Forecasting System

Data assimilation uses observation for updating model variables and improving model output accuracy. In this study, flood extent information derived from Earth Observation data (namely Synthetic Aperture Radar images) are assimilated into a loosely coupled flood inundation forecasting system via a Particle Filter (PF). A previous study based on a synthetic experiment has shown the validity and efficiency of a recently developed PF-based assimilation framework allowing to effectively integrate remote sensing-derived probabilistic flood inundation maps into a coupled hydrologic-hydraulic model. One of the main limitations of this recent framework based on sequential importance sampling is the sample degeneracy and impoverishment, as particles loose diversity and only few of them keep a substantial importance weight in the posterior distribution. In order to circumvent this limitation, a new methodology is adopted and evaluated: a tempered particle filter. The main idea is to update a set of state variables, namely through a smooth transition (iterative and adaptative process). To do so, the likelihood is factorized using small tempering factors. Each iteration includes subsequent resampling and mutation steps using a Monte Carlo Metropolis Hasting algorithm. The mutation step is required to regain diversity between the particles after the resampling. The new methodology is tested using synthetic twin experiments and the results are compared to the one obtained with the previous approach. The new proposed method enables to substantially improve the predictions of streamflow and water levels within the hydraulic domain at the assimilation time step. Moreover, the preliminary results show that these improvements are longer lasting. The proposed tempered particle filter also helps in keeping more diversity within the ensemble.

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Analyzing large-scale Earth Observation data repositories made simple with OpenEO Platform

10.5194/egusphere-egu21-9602 ◽

2021 ◽

Author(s):

Edzer Pebesma ◽

Patrick Griffiths ◽

Christian Briese ◽

Alexander Jacob ◽

Anze Skerlevaj ◽

...

Keyword(s):

Large Scale ◽

Virtual Machines ◽

Google Earth ◽

Earth Observation ◽

Data Cube ◽

Observation Data ◽

Data Repositories ◽

Software Ecosystem ◽

Wide Range ◽

Earth Observation Data

The OpenEO API allows the analysis of large amounts of Earth Observation data using a high-level abstraction of data and processes. Rather than focusing on the management of virtual machines and millions of imagery files, it allows to create jobs that take a spatio-temporal section of an image collection (such as Sentinel L2A), and treat it as a data cube. Processes iterate or aggregate over pixels, spatial areas, spectral bands, or time series, while working at arbitrary spatial resolution. This pattern, pioneered by Google Earth Engine&#8482; (GEE), lets the user focus on the science rather than on data management.The openEO H2020 project (2017-2020) has developed the API as well as an ecosystem of software around it, including clients (JavaScript, Python, R, QGIS, browser-based), back-ends that translate API calls into existing image analysis or GIS software or services (for Sentinel Hub, WCPS, Open Data Cube, GRASS GIS, GeoTrellis/GeoPySpark, and GEE) as well as a hub that allows querying and searching openEO providers for their capabilities and datasets. The project demonstrated this software in a number of use cases, where identical processing instructions were sent to different implementations, allowing comparison of returned results.A follow-up, ESA-funded project &#8220;openEO Platform&#8221; realizes the API and progresses the software ecosystem into operational services and applications that are accessible to everyone, that involve federated deployment (using the clouds managed by EODC, Terrascope, CreoDIAS and EuroDataCube), that will provide payment models (&#8220;pay per compute job&#8221;) conceived and implemented following the user community needs and that will use the EOSC (European Open Science Cloud) marketplace for dissemination and authentication. A wide range of large-scale cases studies will demonstrate the ability of the openEO Platform to scale to large data volumes.&#160; The case studies to be addressed include on-demand ARD generation for SAR and multi-spectral data, agricultural demonstrators like crop type and condition monitoring, forestry services like near real time forest damage assessment as well as canopy cover mapping, environmental hazard monitoring of floods and air pollution as well as security applications in terms of vessel detection in the mediterranean sea.While the landscape of cloud-based EO platforms and services has matured and diversified over the past decade, we believe there are strong advantages for scientists and government agencies to adopt the openEO approach. Beyond the absence of vendor/platform lock-in or EULA&#8217;s we mention the abilities to (i) run arbitrary user code (e.g. written in R or Python) close to the data, (ii) carry out scientific computations on an entirely open source software stack, (iii) integrate different platforms (e.g., different cloud providers offering different datasets), and (iv) help create and extend this software ecosystem. openEO uses the OpenAPI standard, aligns with modern OGC API standards, and uses the STAC (SpatioTemporal Asset Catalog) to describe image collections and image tiles.

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Landsat Images Classification Algorithm (LICA) to Automatically Extract Land Cover Information in Google Earth Engine Environment

Remote Sensing ◽

10.3390/rs12071201 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1201 ◽

Cited By ~ 3

Author(s):

Alessandra Capolupo ◽

Cristina Monterisi ◽

Eufemia Tarantino

Keyword(s):

Big Data ◽

Land Cover ◽

Open Data ◽

Google Earth ◽

Classification Algorithm ◽

Specific Class ◽

Landsat Images ◽

Sparse Vegetation ◽

Main Technique ◽

Google Earth Engine

Remote sensing has been recognized as the main technique to extract land cover/land use (LC/LU) data, required to address many environmental issues. Therefore, over the years, many approaches have been introduced and explored to optimize the resultant classification maps. Particularly, index-based methods have highlighted its efficiency and effectiveness in detecting LC/LU in a multitemporal and multisensors analysis perspective. Nevertheless, the developed indices are suitable to extract a specific class but not to completely classify the whole area. In this study, a new Landsat Images Classification Algorithm (LICA) is proposed to automatically detect land cover (LC) information using satellite open data provided by different Landsat missions in order to perform a multitemporal and multisensors analysis. All the steps of the proposed method were implemented within Google Earth Engine (GEE) to automatize the procedure, manage geospatial big data, and quickly extract land cover information. The algorithm was tested on the experimental site of Siponto, a historic municipality located in Apulia Region (Southern Italy) using 12 radiometrically and atmospherically corrected satellite images collected from Landsat archive (four images, one for each season, were selected from Landsat 5, 7, and 8, respectively). Those images were initially used to assess the performance of 82 traditional spectral indices. Since their classification accuracy and the number of identified LC categories were not satisfying, an analysis of the different spectral signatures existing in the study area was also performed, generating a new algorithm based on the sequential application of two new indices (SwirTirRed (STRed) index and SwiRed index). The former was based on the integration of shortwave infrared (SWIR), thermal infrared (TIR), and red bands, whereas the latter featured a combination of SWIR and red bands. The performance of LICA was preferable to those of conventional indices both in terms of accuracy and extracted classes number (water, dense and sparse vegetation, mining areas, built-up areas versus water, and dense and sparse vegetation). GEE platform allowed us to go beyond desktop system limitations, reducing acquisition and processing times for geospatial big data.

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Integrated Mapping of Spatial Urban Dynamics—A European-Chinese Exploration. Part 1—Methodology for Automatic Land Cover Classification Tailored towards Spatial Allocation of Ecosystem Services Features

Remote Sensing ◽

10.3390/rs13091744 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1744

Author(s):

Ellen Banzhaf ◽

Wanben Wu ◽

Xiangyu Luo ◽

Julius Knopp

Keyword(s):

Ecosystem Services ◽

Land Cover ◽

Urban Expansion ◽

Sample Selection ◽

Mapping Method ◽

Google Earth ◽

Classification Model ◽

Observation Data ◽

Urban Dynamics ◽

Earth Observation Data

Urbanisation processes inherently influence land cover (LC) and have dramatic impacts on the amount, distribution and quality of vegetation cover. The latter are the source of ecosystem services (ES) on which humans depend. However, the temporal and thematical dimensions are not documented in a comparable manner across Europe and China. Three cities in China and three cities in Europe were selected as case study areas to gain a picture of spatial urban dynamics at intercontinental scale. First, we analysed available global and continental thematic LC products as a data pool for sample selection and referencing our own mapping model. With the help of the Google Earth Engine (GEE) platform and earth observation data, an automatic LC mapping method tailored for more detailed ES features was proposed. To do so, differentiated LC categories were quantified. In order to obtain a balance between efficiency and high classification accuracy, we developed an optimal classification model by evaluating the importance of a large number of spectral, texture-based indices and topographical information. The overall classification accuracies range between 73% and 95% for different time slots and cities. To capture ES related LC categories in great detail, deciduous and coniferous forests, cropland, grassland and bare land were effectively identified. To understand inner urban options for potential new ES, dense and dispersed built-up areas were differentiated with good results. In addition, this study focuses on the differences in the characteristics of urban expansion witnessed in China and Europe. Our results reveal that urbanisation has been more intense in the three Chinese cities than in the three European cities, with an 84% increase in the entire built-up area over the last two decades. However, our results also show the results of China’s ecological restoration policies, with a total of 963 km2 of new green and blue LC created in the last two decades. We proved that our automatic mapping can be effectively applied to future studies, and the monitoring results will be useful for consecutive ES analyses aimed at achieving more environmentally friendly cities.

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Mapping and Assessing the Dynamics of Shifting Agricultural Landscapes Using Google Earth Engine Cloud Computing, a Case Study in Mozambique

Remote Sensing ◽

10.3390/rs12081279 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1279 ◽

Cited By ~ 1

Author(s):

Sosdito Mananze ◽

Isabel Pôças ◽

Mário Cunha

Keyword(s):

Cloud Computing ◽

Land Cover ◽

Google Earth ◽

Agricultural Landscapes ◽

Landsat 8 ◽

Textural Features ◽

Observation Data ◽

Ground Truth Data ◽

Spectral Bands ◽

Google Earth Engine

Land cover maps obtained at high spatial and temporal resolutions are necessary to support monitoring and management applications in areas with many smallholder and low-input agricultural systems, as those characteristic in Mozambique. Various regional and global land cover products based on Earth Observation data have been developed and made publicly available but their application in regions characterized by a large variety of agro-systems with a dynamic nature is limited by several constraints. Challenges in the classification of spatially heterogeneous landscapes, as in Mozambique, include the definition of the adequate spatial resolution and data input combinations for accurately mapping land cover. Therefore, several combinations of variables were tested for their suitability as input for random forest ensemble classifier aimed at mapping the spatial dynamics of smallholder agricultural landscape in Vilankulo district in Mozambique. The variables comprised spectral bands from Landsat 7 ETM+ and Landsat 8 OLI/TIRS, vegetation indices and textural features and the classification was performed within the Google Earth Engine cloud computing for the years 2012, 2015, and 2018. The study of three different years aimed at evaluating the temporal dynamics of the landscape, typically characterized by high shifting nature. For the three years, the best performing variables included three selected spectral bands and textural features extracted using a window size of 25. The classification overall accuracy was 0.94 for the year 2012, 0.98 for 2015, and 0.89 for 2018, suggesting that the produced maps are reliable. In addition, the areal statistics of the class classified as agriculture were very similar to the ground truth data as reported by the Serviços Distritais de Actividades Económicas (SDAE), with an average percentage deviation below 10%. When comparing the three years studied, the natural vegetation classes are the predominant covers while the agriculture is the most important cause of land cover changes.

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National Open Data Cubes and Their Contribution to Country-Level Development Policies and Practices

Data ◽

10.3390/data4040144 ◽

2019 ◽

Vol 4 (4) ◽

pp. 144 ◽

Cited By ~ 4

Author(s):

Trevor Dhu ◽

Gregory Giuliani ◽

Jimena Juárez ◽

Argyro Kavvada ◽

Brian Killough ◽

...

Keyword(s):

International Development ◽

Water Resource Management ◽

Open Data ◽

Data Cube ◽

Observation Data ◽

Country Level ◽

Global Trend ◽

Development Policies ◽

Earth Observation Data ◽

Development Agendas

The emerging global trend of satellite operators producing analysis-ready data combined with open source tools for managing and exploiting these data are leading to more and more countries using Earth observation data to drive progress against key national and international development agendas. This paper provides examples from Australia, Mexico, Switzerland, and Tanzania on how the Open Data Cube technology has been combined with analysis-ready data to provide new insights and support better policy making across issues as diverse as water resource management through to urbanization and environmental–economic accounting.

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Supporting better decisions across the nexus of water, energy and food through earth observation data: case of the Zambezi basin

Proceedings of the International Association of Hydrological Sciences ◽

10.5194/piahs-376-15-2018 ◽

2018 ◽

Vol 376 ◽

pp. 15-23 ◽

Cited By ~ 1

Author(s):

Fortune Faith Gomo ◽

Christopher Macleod ◽

John Rowan ◽

Jagadeesh Yeluripati ◽

Kairsty Topp

Keyword(s):

Ecosystem Services ◽

Land Cover ◽

Spatial Scales ◽

European Space Agency ◽

Google Earth ◽

Tree Cover ◽

Observation Data ◽

Management Options ◽

Basin Scale ◽

The Future

Abstract. The water–energy–food (WEF) nexus has been promoted in recent years as an intersectional concept designed to improve planning and regulatory decision-making across the three sectors. The production and consumption of water, energy and food resources are inextricably linked across multiple spatial scales (from the global to the local), but a common feature is competition for land which through different land management practices mediates provisioning ecosystem services. The nexus perspective seeks to understand the interlinkages and use systems-based thinking to frame management options for the present and the future. It aims to highlight advantage and minimise damaging and unsustainable outcomes through informed decisions regarding trade-offs inclusive of economic, ecological and equity considerations. Operationalizing the WEF approach is difficult because of the lack of complete data, knowledge and observability – and the nature of the challenge also depends on the scale of the investigation. Transboundary river basins are particularly challenging because whilst the basin unit defines the hydrological system this is not necessarily coincident with flows of food and energy. There are multiple national jurisdictions and geopolitical relations to consider. Land use changes have a profound influence on hydrological, agricultural, energy provisioning and regulating ecosystem services. Future policy decisions in the water, energy and food sectors could have profound effects, with different demands for land and water resources, intensifying competition for these resources in the future. In this study, we used Google Earth Engine (GEE) to analyse the land cover changes in the Zambezi river basin (1.4 million km2) from 1992 to 2015 using the European Space Agency annual global land cover dataset. Early results indicate transformative processes are underway with significant shifts from tree cover to cropland, with a 4.6 % loss in tree cover and a 16 % gain in cropland during the study period. The changes were found to be occurring mainly in the eastern (Malawi and Mozambique) and southern (Zimbabwe and southern Zambia) parts of the basin. The area under urban land uses was found to have more than doubled during the study period gearing urban centres increasingly as the foci for resource consumption. These preliminary findings are the first step in understanding the spatial and temporal interlinkages of water, energy and food by providing reliable and consistent evidence spanning the local, regional, national and whole transboundary basin scale.

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Open Data Cube for Natural Resources Mapping in Mexico

10.29007/d19p ◽

2019 ◽

Author(s):

José Luis Ornelas De Anda ◽

Juan Carlos Camacho Pérez ◽

Hugo Alfredo Sánchez Miranda

Keyword(s):

Natural Resources ◽

Spatial Data ◽

Landsat Imagery ◽

Open Data ◽

Temporal Analysis ◽

Data Cube ◽

Full Potential ◽

Observation Data ◽

Visual Interpretation ◽

Earth Observation Data

In recent years, the efforts to enhance the analysis of Earth’s surface with satellite imagery have forced the scientific community to develop different techniques and methodologies. The Open Data Cube aims to provide tools to execute multi-temporal analysis and get accurate products, excluding low-quality pixels in small or large areas of study with an accuracy subject to the resolution of the data used for the analysis. This means that we can make use of the full potential of Earth observation data available from satellite data providers, in this document we take a closer look at Landsat Imagery and its applications. The beginning of the implementation of the Open Data Cube platform began in 2018, positioning itself as a valuable source of spatial data for Natural Resources projects in INEGI and seeks to support the decision-making process based on territorial analyzes with great certainty. The use of this technological solution represents a great leap between the traditional visual interpretation of raster data and the automated processing of data in time series.

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