Noel Gorelick
Research Areas
Authored Publications
Sort By
Exploiting satellite observations for global surface albedo trends monitoring
Nektarios Chrysoulakis
Zina Mitraka
Theoretical and Applied Climatology, 137(2019), pp. 1-2
Preview abstract
Surface albedo is one of the essential climate variables as it influences the radiation budget and the energy balance. Because it is used in a variety of scientific fields, from local to global scale, spatially and temporally disaggregated albedo data are required, which can be derived from satellites. Satellite observations have led to directional-hemispherical (black-sky) and bi-hemispherical (white-sky) albedo products, but time series of high spatial resolution true (blue-sky) albedo estimations at global level are not available. Here, we exploit the capabilities of Google Earth Engine (GEE) for big data analysis to derive global snow-free land surface albedo estimations and trends at a 500-m scale, using satellite observations from 2000 to 2015. Our study reveals negative albedo trends mainly in Mediterranean, India, south-western Africa and Eastern Australia, whereas positive trends mainly in Ukraine, South Russia and Eastern Kazakhstan, Eastern Asia, Brazil, Central and Eastern Africa and Central Australia. The bulk of these trends can be attributed to rainfall, changes in agricultural practices and snow cover duration. Our study also confirms that at local scale, albedo changes are consistent with land cover/use changes that are driven by anthropogenic activities.
View details
Inland surface waters in protected areas globally: Current coverage and 30-year trends
Lucy Bastin
Santiago Saura
Bastian Bertzky
Grégoire Dubois
Marie-Josée Fortin
Jean-Francois Pekel
PloS one, 14(2019), e0210496
Preview abstract
Inland waters are unique ecosystems offering services and habitat resources upon which many species depend. Despite the importance of, and threats to, inland water, global assessments of protected area (PA) coverage and trends have focused on land habitats or have assessed land and inland waters together. We here provide the first assessment of the level of protection of inland open surface waters and their trends (1984–2015) within PAs for all countries, using a globally consistent, high-resolution (30 m) and validated dataset on permanent and seasonal surface waters based on Landsat images. Globally, 15% of inland surface waters are covered by PAs with mapped boundaries. Estimated inland water protection increases to 16.4% if PAs with reported area but delineated only as points are included as circular buffers. These coverage estimates slightly exceed the comparable figure for land but fall below the 17% goal of the Convention on Biological Diversity’s Aichi Target 11 for 2020. Protection levels are very uneven across countries, half of which do not yet meet the 17% target. The lowest coverage of surface water by PAs (<5%) was found in Africa and in parts of Asia. There was a global trend of permanent water losses and seasonal water gains within PAs, concomitant with an increase of both water types outside PAs. In 38% of countries, PAs lost over 5% of permanent water. Global protection targets for inland waters may well be met by 2020, but much stronger efforts are required to ensure their effective conservation, which will depend not only on sound PA governance and management but also on the sustainable use of water resources outside PAs. Given the pressures on water in a rapidly changing world, integrated management planning of water resources involving multiple sectors and entire basins is therefore necessary.
View details
Implementation of the LandTrendr Algorithm on Google Earth Engine
Robert Kennedy
Zhiqiang Yang
Justin Braaten
Lucas Cavalcante
Warren Cohen
Sean Healey
Remote Sensing, 10(2018), pp. 681
Preview abstract
The LandTrendr (LT) algorithm has been used widely for analysis of change in Landsat spectral time series data, but requires significant pre-processing, data management, and computational resources, and is only accessible to the community in a proprietary programming language (IDL). Here, we introduce LT for the Google Earth Engine (GEE) platform. The GEE platform simplifies pre-processing steps, allowing focus on the translation of the core temporal segmentation algorithm. Temporal segmentation involved a series of repeated random access calls to each pixel’s time series, resulting in a set of breakpoints (“vertices”) that bound straight-line segments. The translation of the algorithm into GEE included both transliteration and code analysis, resulting in improvement and logic error fixes. At six study areas representing diverse land cover types across the US, we conducted a direct comparison of the new LT-GEE code against the heritage code (LT-IDL). The algorithms agreed in most cases, and where disagreements occurred, they were largely attributable to logic error fixes in the code translation process. The practical impact of these changes is minimal, as shown by an example of forest disturbance mapping. We conclude that the LT-GEE algorithm represents a faithful translation of the LT code into a platform easily accessible by the broader user community.
View details
Mapping forest change using stacked generalization: An ensemble approach
Sean P Healey
Warren B Cohen
Zhiqiang Yang
C Kenneth Brewer
Evan B Brooks
Alexander J Hernandez
Chengquan Huang
M Joseph Hughes
Robert E Kennedy
Thomas R Loveland
Gretchen G Moisen
Todd A Schroeder
Stephen V Stehman
James E Vogelmann
Curtis E Woodcock
Limin Yang
Zhe Zhu
Remote Sensing of Environment, 204(2018), pp. 717-728
Preview abstract
The ever-increasing volume and accessibility of remote sensing data has spawned many alternative approaches for mapping important environmental features and processes. For example, there are several viable but highly varied strategies for using time series of Landsat imagery to detect changes in forest cover. Performance among algorithms varies across complex natural systems, and it is reasonable to ask if aggregating the strengths of an ensemble of classifiers might result in increased overall accuracy. Relatively simple rules have been used in the past to aggregate classifications among remotely sensed maps (e.g. using majority predictions), and in other fields, empirical models have been used to create situationally specific algorithm weights. The latter process, called “stacked generalization” (or “stacking”), typically uses a parametric model for the fusion of algorithm outputs. We tested the performance of several leading forest disturbance detection algorithms against ensembles of the outputs of those same algorithms based upon stacking using both parametric and Random Forests-based fusion rules. Stacking using a Random Forests model cut omission and commission error rates in half in many cases in relation to individual change detection algorithms, and cut error rates by one quarter compared to more conventional parametric stacking. Stacking also offers two auxiliary benefits: alignment of outputs to the precise definitions built into a particular set of empirical calibration data; and, outputs which may be adjusted such that map class totals match independent estimates of change in each year. In general, ensemble predictions improve when new inputs are added that are both informative and uncorrelated with existing ensemble components. As increased use of cloud-based computing makes ensemble mapping methods more accessible, the most useful new algorithms may be those that specialize in providing spectral, temporal, or thematic information not already available through members of existing ensembles.
View details
A LandTrendr multispectral ensemble for forest disturbance detection
Warren B Cohen
Zhiqiang Yang
Sean P Healey
Robert E Kennedy
Remote sensing of environment, 205(2018), pp. 131-140
Preview abstract
Monitoring and classifying forest disturbance using Landsat time series has improved greatly over the past decade, with many new algorithms taking advantage of the high-quality, cost free data in the archive. Much of the innovation has been focused on use of sophisticated workflows that consist of a logical sequence of processes and rules, multiple statistical functions, and parameter sets that must be calibrated to accurately classify disturbance. For many algorithms, calibration has been local to areas of interest and the algorithm's classification performance has been good under those circumstances. When applied elsewhere, however, algorithm performance has suffered. An alternative strategy for calibration may be to use the locally tested parameter values in conjunction with a statistical approach (e.g., Random Forests; RF) to align algorithm classification with a reference disturbance dataset, a process we call secondary classification. We tested that strategy here using RF with LandTrendr, an algorithm that runs on one spectral band or index. Disturbance detection using secondary classification was spectral band- or index-dependent, with each spectral dimension providing some unique detections and different error rates. Using secondary classification, we tested whether an integrated multispectral LandTrendr ensemble, with various combinations of the six basic Landsat reflectance bands and seven common spectral indices, improves algorithm performance. Results indicated a substantial reduction in errors relative to secondary classification based on single bands/indices, revealing the importance of a multispectral approach to forest disturbance detection. To explain the importance of specific bands and spectral indices in the multispectral ensemble, we developed a disturbance signal-to-noise metric that clearly highlighted the value of shortwave-infrared reflectance, especially when paired with near-infrared reflectance.
View details
Where we live—A summary of the achievements and planned evolution of the global urban footprint
Thomas Esch
Felix Bachofer
Wieke Heldens
Andreas Hirner
Mattia Marconcini
Daniela Palacios-Lopez
Achim Roth
Soner Üreyen
Julian Zeidler
Stefan Dech
Remote Sensing, 10(2018), pp. 895
Preview abstract
The TerraSAR-X (TSX) mission provides a distinguished collection of high resolution satellite images that shows great promise for a global monitoring of human settlements. Hence, the German Aerospace Center (DLR) has developed the Urban Footprint Processor (UFP) that represents an operational framework for the mapping of built-up areas based on a mass processing and analysis of TSX imagery. The UFP includes functionalities for data management, feature extraction, unsupervised classification, mosaicking, and post-editing. Based on> 180.000 TSX StripMap scenes, the UFP was used in 2016 to derive a global map of human presence on Earth in a so far unique spatial resolution of 12 m per grid cell: the Global Urban Footprint (GUF). This work provides a comprehensive summary of the major achievements related to the Global Urban Footprint initiative, with dedicated sections focusing on aspects such as UFP methodology, basic product characteristics (specification, accuracy, global figures on urbanization derived from GUF), the user community, and the already initiated future roadmap of follow-on activities and products. The active community of> 250 institutions already working with the GUF data documents the relevance and suitability of the GUF initiative and the underlying high-resolution SAR imagery with respect to the provision of key information on the human presence on earth and the global human settlements properties and patterns, respectively.
View details
Preview abstract
Rocky debris on glacier surfaces influences ice melt rates and the response of glaciers to climate change. However, scarce data on the extent and evolution of supraglacial debris cover have so far limited its inclusion in regional to global glacier models. Here we present global data sets of supraglacial debris‐cover extents, based on Landsat 8 and Sentinel‐2 optical satellite imagery. We find that about 4.4% (~26,000 km2) of all glacier areas (excluding the Greenland ice sheet and Antarctica) are covered with debris, but that the distribution is heterogeneous. The largest debris‐covered areas are located in high‐mountain ranges, away from the poles. At a global scale, we find a negative scaling relationship between glacier size and percentage of debris. Therefore, the influence of debris cover on glacier mass balances is expected to increase in the future, as glaciers continue to shrink.
View details
Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone
Matt Hancher
Mike Dixon
Simon Ilyushchenko
David Thau
Remote Sensing of Environment, 202(2017), pp. 18-27
Preview abstract
Google Earth Engine is a cloud-based platform for planetary-scale geospatial analysis that brings Google's massive computational capabilities to bear on a variety of high-impact societal issues including deforestation, drought, disaster, disease, food security, water management, climate monitoring and environmental protection. It is unique in the field as an integrated platform designed to empower not only traditional remote sensing scientists, but also a much wider audience that lacks the technical capacity needed to utilize traditional supercomputers or large-scale commodity cloud computing resources.
View details
Preview abstract
The location and persistence of surface water (inland and coastal) is both affected by climate and human activity and affects climate, biological diversity4 and human wellbeing. Global data sets documenting surface water location and seasonality have been produced from inventories and national descriptions, statistical extrapolation of regional data and satellite imagery, but measuring long-term changes at high resolution remains a challenge. Here, using three million Landsat satellite images, we quantify changes in global surface water over the past 32 years at 30-metre resolution. We record the months and years when water was present, where occurrence changed and what form changes took in terms of seasonality and persistence. Between 1984 and 2015 permanent surface water has disappeared from an area of almost 90,000 square kilometres, roughly equivalent to that of Lake Superior, though new permanent bodies of surface water covering 184,000 square kilometres have formed elsewhere. All continental regions show a net increase in permanent water, except Oceania, which has a fractional (one per cent) net loss. Much of the increase is from reservoir filling, although climate change is also implicated. Loss is more geographically concentrated than gain. Over 70 per cent of global net permanent water loss occurred in the Middle East and Central Asia, linked to drought and human actions including river diversion or damming and unregulated withdrawal. Losses in Australia and the USA linked to long-term droughts are also evident. This globally consistent, validated data set shows that impacts of climate change and climate oscillations on surface water occurrence can be measured and that evidence can be gathered to show how surface water is altered by human activities. We anticipate that this freely available data will improve the modelling of surface forcing, provide evidence of state and change in wetland ecotones (the transition areas between biomes), and inform water-management decision-making.
View details
Earth's surface water change over the past 30 years.
Gennadii Donchyts
Baart Fedor
Hessel Winsemius,
Jaap Kwadijk
Nick van de Giesen
Nature Climate Change, 6(2016), pp. 810
Preview abstract
Earth's surface gained 115,000 km2 of water and 173,000 km2 of land over the past 30 years, including 20,135 km2 of water and 33,700 km2 of land in coastal areas. Here, we analyse the gains and losses through the Deltares Aqua Monitor — an open tool that detects land and water changes around the globe.
View details
