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

Ellen Banzhaf; Wanben Wu; Xiangyu Luo; Julius Knopp

doi:10.3390/rs13091744

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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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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Quantifying Tree Cover Loss in Urban Forests within Nairobi City Metropolitan Area from Earth Observation Data

Environmental Sciences Proceedings ◽

10.3390/iecf2020-07952 ◽

2020 ◽

Vol 3 (1) ◽

pp. 78

Author(s):

Francis Oloo ◽

Godwin Murithi ◽

Charlynne Jepkosgei

Keyword(s):

Land Cover ◽

Metropolitan Area ◽

Land Surface ◽

Urban Forest ◽

Urban Forests ◽

Earth Observation ◽

Tree Cover ◽

Observation Data ◽

Vegetation Health ◽

Earth Observation Data

Urban forests contribute significantly to the ecological integrity of urban areas and the quality of life of urban dwellers through air quality control, energy conservation, improving urban hydrology, and regulation of land surface temperatures (LST). However, urban forests are under threat due to human activities, natural calamities, and bioinvasion continually decimating forest cover. Few studies have used fine-scaled Earth observation data to understand the dynamics of tree cover loss in urban forests and the sustainability of such forests in the face of increasing urban population. The aim of this work was to quantify the spatial and temporal changes in urban forest characteristics and to assess the potential drivers of such changes. We used data on tree cover, normalized difference vegetation index (NDVI), and land cover change to quantify tree cover loss and changes in vegetation health in urban forests within the Nairobi metropolitan area in Kenya. We also used land cover data to visualize the potential link between tree cover loss and changes in land use characteristics. From approximately 6600 hectares (ha) of forest land, 720 ha have been lost between 2000 and 2019, representing about 11% loss in 20 years. In six of the urban forests, the trend of loss was positive, indicating a continuing disturbance of urban forests around Nairobi. Conversely, there was a negative trend in the annual mean NDVI values for each of the forests, indicating a potential deterioration of the vegetation health in the forests. A preliminary, visual inspection of high-resolution imagery in sample areas of tree cover loss showed that the main drivers of loss are the conversion of forest lands to residential areas and farmlands, implementation of big infrastructure projects that pass through the forests, and extraction of timber and other resources to support urban developments. The outcome of this study reveals the value of Earth observation data in monitoring urban forest resources.

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Continental-Scale Land Cover Mapping at 10 m Resolution Over Europe (ELC10)

Remote Sensing ◽

10.3390/rs13122301 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2301

Author(s):

Zander Venter ◽

Markus Sydenham

Keyword(s):

Land Cover ◽

Atmospheric Correction ◽

Google Earth ◽

Tree Planting ◽

Training Data ◽

Classification Model ◽

Night Time ◽

Continental Scale ◽

Land Cover Maps ◽

Sentinel 2

Land cover maps are important tools for quantifying the human footprint on the environment and facilitate reporting and accounting to international agreements addressing the Sustainable Development Goals. Widely used European land cover maps such as CORINE (Coordination of Information on the Environment) are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a 10 m resolution land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A random forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area Frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across eight land cover classes and could account for statistical unit land cover proportions within 3.9% (R2 = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10 m land cover maps including S2GLC and FROM-GLC10. Spectro-temporal metrics that capture the phenology of land cover classes were most important in producing high mapping accuracies. We found that the atmospheric correction of Sentinel-2 and the speckle filtering of Sentinel-1 imagery had a minimal effect on enhancing the classification accuracy (< 1%). However, combining optical and radar imagery increased accuracy by 3% compared to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The addition of auxiliary data (terrain, climate and night-time lights) increased accuracy by an additional 2%. By using the centroid pixels from the LUCAS Copernicus module polygons we increased accuracy by <1%, revealing that random forests are robust against contaminated training data. Furthermore, the model requires very little training data to achieve moderate accuracies—the difference between 5K and 50K LUCAS points is only 3% (86 vs. 89%). This implies that significantly less resources are necessary for making in situ survey data (such as LUCAS) suitable for satellite-based land cover classification. At 10 m resolution, the ELC10 map can distinguish detailed landscape features like hedgerows and gardens, and therefore holds potential for aerial statistics at the city borough level and monitoring property-level environmental interventions (e.g., tree planting). Due to the reliance on purely satellite-based input data, the ELC10 map can be continuously updated independent of any country-specific geographic datasets.

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Qualifying Land Use and Land Cover Dynamics and Their Impacts on Ecosystem Service in Central Himalaya Transboundary Landscape Based on Google Earth Engine

Land ◽

10.3390/land10020173 ◽

2021 ◽

Vol 10 (2) ◽

pp. 173

Author(s):

Changjun Gu ◽

Yili Zhang ◽

Linshan Liu ◽

Lanhui Li ◽

Shicheng Li ◽

...

Keyword(s):

Land Use ◽

Ecosystem Services ◽

Land Cover ◽

Ecosystem Service ◽

Forest Cover ◽

Google Earth ◽

Forest Cover Change ◽

Sacred Landscape ◽

Lulc Changes ◽

Google Earth Engine

Land use and land cover (LULC) changes are regarded as one of the key drivers of ecosystem services degradation, especially in mountain regions where they may provide various ecosystem services to local livelihoods and surrounding areas. Additionally, ecosystems and habitats extend across political boundaries, causing more difficulties for ecosystem conservation. LULC in the Kailash Sacred Landscape (KSL) has undergone obvious changes over the past four decades; however, the spatiotemporal changes of the LULC across the whole of the KSL are still unclear, as well as the effects of LULC changes on ecosystem service values (ESVs). Thus, in this study we analyzed LULC changes across the whole of the KSL between 2000 and 2015 using Google Earth Engine (GEE) and quantified their impacts on ESVs. The greatest loss in LULC was found in forest cover, which decreased from 5443.20 km2 in 2000 to 5003.37 km2 in 2015 and which mainly occurred in KSL-Nepal. Meanwhile, the largest growth was observed in grassland (increased by 548.46 km2), followed by cropland (increased by 346.90 km2), both of which mainly occurred in KSL-Nepal. Further analysis showed that the expansions of cropland were the major drivers of the forest cover change in the KSL. Furthermore, the conversion of cropland to shrub land indicated that farmland abandonment existed in the KSL during the study period. The observed forest degradation directly influenced the ESV changes in the KSL. The total ESVs in the KSL decreased from 36.53 × 108 USD y−1 in 2000 to 35.35 × 108 USD y−1 in 2015. Meanwhile, the ESVs of the forestry areas decreased by 1.34 × 108 USD y−1. This shows that the decrease of ESVs in forestry was the primary cause to the loss of total ESVs and also of the high elasticity. Our findings show that even small changes to the LULC, especially in forestry areas, are noteworthy as they could induce a strong ESV response.

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FREE GLOBAL DSM ASSESSMENT ON LARGE SCALE AREAS EXPLOITING THE POTENTIALITIES OF THE INNOVATIVE GOOGLE EARTH ENGINE PLATFORM

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-1-w1-627-2017 ◽

2017 ◽

Vol XLII-1/W1 ◽

pp. 627-633 ◽

Cited By ~ 5

Author(s):

A. Nascetti ◽

M. Di Rita ◽

R. Ravanelli ◽

M. Amicuzi ◽

S. Esposito ◽

...

Keyword(s):

High Performance ◽

Large Scale ◽

Global Scale ◽

Google Earth ◽

Observation Data ◽

Geometric Accuracy ◽

Computing Platform ◽

Aster Gdem ◽

Google Earth Engine ◽

Earth Observation Data

The high-performance cloud-computing platform Google Earth Engine has been developed for global-scale analysis based on the Earth observation data. In particular, in this work, the geometric accuracy of the two most used nearly-global free DSMs (SRTM and ASTER) has been evaluated on the territories of four American States (Colorado, Michigan, Nevada, Utah) and one Italian Region (Trentino Alto- Adige, Northern Italy) exploiting the potentiality of this platform. These are large areas characterized by different terrain morphology, land covers and slopes. The assessment has been performed using two different reference DSMs: the USGS National Elevation Dataset (NED) and a LiDAR acquisition. The DSMs accuracy has been evaluated through computation of standard statistic parameters, both at global scale (considering the whole State/Region) and in function of the terrain morphology using several slope classes. The geometric accuracy in terms of Standard deviation and NMAD, for SRTM range from 2-3 meters in the first slope class to about 45 meters in the last one, whereas for ASTER, the values range from 5-6 to 30 meters. In general, the performed analysis shows a better accuracy for the SRTM in the flat areas whereas the ASTER GDEM is more reliable in the steep areas, where the slopes increase. These preliminary results highlight the GEE potentialities to perform DSM assessment on a global scale.

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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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Global assessment of mountain ecosystem services using earth observation data

Ecosystem Services ◽

10.1016/j.ecoser.2020.101213 ◽

2020 ◽

Vol 46 ◽

pp. 101213

Author(s):

Adrienne Grêt-Regamey ◽

Bettina Weibel

Keyword(s):

Ecosystem Services ◽

Global Assessment ◽

Earth Observation ◽

Observation Data ◽

Mountain Ecosystem ◽

Earth Observation Data

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Space Observatory for carbon budget monitoring in Russian forests using Earth observations and modelling

10.5194/egusphere-egu2020-20567 ◽

2020 ◽

Author(s):

Sergey Bartalev

Keyword(s):

Land Cover ◽

Carbon Budget ◽

Earth Observation ◽

Forest Carbon ◽

Observation Data ◽

Space Observatory ◽

Russian Science ◽

Land Cover Types ◽

Russian Forests ◽

Earth Observation Data

Russian forest is a factor of global importance for implementation of international conventions on climate considering its potential for absorption and accumulation of the atmospheric carbon at an impressive scale. Considering recently adopted Paris agreement on climate the comprehensive and accurate estimation of Russian forests&#8217; carbon budget became a top priority research and development issue on national agenda. However existing quantitative estimates of Russian forests&#8217; carbon budget are of significant level of uncertainty. One of the most obvious reasons for such uncertainty is not sufficiently reliable and up-to-date information on characteristics of forests and their dynamics.The Russian Science Foundation has supported an ambitious research megaproject titled &#8220;Space Observatory for Forest Carbon&#8221; (SOFC) started in year 2019 and aimed at the development of a new concept and comprehensive methods for forest carbon budget monitoring using Earth observation data and forest growth and dynamics models. The main SOFC project objectives are as follows:- Development of a new concept and methodology for Russian forests and their carbon budget monitoring based on the integration of remote sensing and ground data along with improved models of forest structure and dynamics;- Development of new annually updated GIS databases on the characteristics and multi-annual dynamics of Russian forests;- Development of an informational system and technology for the continuous monitoring of Russian forests&#8217; carbon budget.Information necessary for carbon budget estimation includes data on various land cover types, forest characteristics (growing stock volume, species composition, age, site-index) and ecological parameters (Net Primary Production, heterotrophic respiration). Data on natural (fires, diseases and pests, windstorm, droughts) and anthropogenic (felling, pollution) forest disturbances causing deforestation, as well as information on subsequent reforestation processes are also vital.The existing remote sensing methods can provide significant part of missing country-wide information about the land cover types and forest characteristics for the national-scale carbon budget estimation and monitoring. Multi-year time series of this data since the beginning of the century allow modelling the forest dynamics and its biophysical characteristics. The Earth observation data derived information on forest fires&#8217; impact includes burnt area mapping over various land cover types as well as forest fire severity assessment allowing characterisation of fire induced carbon emissions. Furthermore, developed methods for processing and analysis of multi-year satellite data time series enable detection of forest cover changes caused by various destructive factors making it possible to substantially improve the accuracy of carbon budget estimation.Obtained information on forest ecosystems&#8217; parameters is used to improve existing and develop new approaches to forest carbon budget estimation, as well as to simulate various scenarios of Russian economy development depending on forest management practices and climate change trajectories.This work was supported by the Russian Science Foundation [grant number 19-77-30015].

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MRSSC: A BENCHMARK DATASET FOR MULTIMODAL REMOTE SENSING SCENE CLASSIFICATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b2-2021-785-2021 ◽

2021 ◽

Vol XLIII-B2-2021 ◽

pp. 785-792

Author(s):

K. Liu ◽

A. Wu ◽

X. Wan ◽

S. Li

Keyword(s):

Remote Sensing ◽

Domain Adaptation ◽

Image Interpretation ◽

Visual Navigation ◽

Classification Model ◽

Observation Data ◽

Scene Classification ◽

Manned Spacecraft ◽

Optical Images ◽

Earth Observation Data

Abstract. Scene classification based on multi-source remote sensing image is important for image interpretation, and has many applications, such as change detection, visual navigation and image retrieval. Deep learning has become a research hotspot in the field of remote sensing scene classification, and dataset is an important driving force to promote its development. Most of the remote sensing scene classification datasets are optical images, and multimodal datasets are relatively rare. Existing datasets that contain both optical and SAR data, such as SARptical and WHU-SEN-City, which mainly focused on urban area without wide variety of scene categories. This largely limits the development of domain adaptive algorithms in remote sensing scene classification. In this paper, we proposed a multi-modal remote sensing scene classification dataset (MRSSC) based on Tiangong-2, a Chinese manned spacecraft which can acquire optical and SAR images at the same time. The dataset contains 12167 images (optical 6155 and 6012 for optical and SAR, resp.) of seven typical scenes, namely city, farmland, mountain, desert, coast, lake and river. Our dataset is evaluated by state-of-theart domain adaptation methods to establish a baseline with average classification accuracy of 79.2%. The MRSSC dataset will be released freely for the educational purpose and can be found at China Manned Space Engineering data service website (http://www.msadc.cn). This dataset will fill the gap between remote sensing scene classification between different image sources, and paves the way for a generalized image classification model for multi-modal earth observation data.

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