Zhe Zhu

Zhe Zhu
University of Connecticut | UConn · Department of Natural Resources and the Environment

PhD

About

103
Publications
152,090
Reads
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17,681
Citations
Additional affiliations
August 2016 - December 2018
Texas Tech University
Position
  • Professor (Assistant)
October 2014 - August 2016
EROS, USGS
Position
  • Land Change Scientist
February 2013 - October 2014
Boston University
Position
  • PostDoc Position
Education
September 2007 - May 2013
Boston University
Field of study
  • Geography
September 2002 - July 2006
Wuhan University
Field of study
  • Remote Sensing

Publications

Publications (103)
Article
Full-text available
A new algorithm for Continuous Change Detection and Classification (CCDC) of land cover using all available Landsat data is developed. It is capable of detecting many kinds of land cover change continuously as new images are collected and providing land cover maps for any given time. A two-step cloud, cloud shadow, and snow masking algorithm is use...
Article
Full-text available
We developed a new algorithm for COntinuous monitoring of Land Disturbance (COLD) using Landsat time series. COLD can detect many kinds of land disturbance continuously as new images are collected and provide historical land disturbance maps retrospectively. To better detect land disturbance, we tested different kinds of input data and explored man...
Article
Full-text available
We developed the Function of mask (Fmask) 4.0 algorithm for automated cloud and cloud shadow detection in Landsats 4-8 and Sentinel-2 images. Three major innovative improvements were made as follows: (1) integration of auxiliary data, where Global Surface Water Occurrence (GSWO) data was used to improve the separation of land and water, and a globa...
Article
Full-text available
The discipline of land change science has been evolving rapidly in the past decades. Remote sensing played a major role in one of the essential components of land change science, which includes observation, monitoring, and characterization of land change. In this paper, we proposed a new framework of the multifaceted view of land change through the...
Article
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We proposed a new image compositing algorithm (MAX-RNB) based on the maximum ratio of Near Infrared (NIR) to Blue band (RNB), and evaluated it together with nine other compositing algorithms: MAX-NDVI (maximum Normalized Difference Vegetation Index), MED-NIR (median NIR band), WELD (conterminous United States Web-Enabled Landsat Data), BAP (Best Av...
Article
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Global observations at 30-m ground sampling distance (GSD) are now possible at a cadence of 1-3 days by combining Landsat 8 and 9 with Sentinel-2A and -2B satellites. Previous studies characterizing pixel-level Landsat-class measurement frequency used data from different sources but offered little information on observation availability after rigor...
Article
Ecosystem dynamics and ecological disturbances manifest as breakpoints in long-term multispectral remote sensing time series. Typically, these breakpoints are captured using univariate methods applied individually to each band, with subsequent integration of the results. However, multivariate analysis provides a promising way to fully incorporate t...
Article
Full-text available
Remote sensing time series research and applications are advancing rapidly in land, ocean, and atmosphere science, demonstrating emerging capabilities in space-based monitoring methodologies and diverse application prospects. This prompts a comprehensive review of remote sensing time series observations, time series data reconstruction, derived pro...
Article
We developed the SCARF (Spatial Mismatch and Systematic Prediction Error Corrected cAscade Random Forests) algorithm for continuous prediction of biomass dynamics using machine learning and Landsat Time Series (LTS). Our approach addresses the challenges posed by the cloudy subtropical forests in southern China, where monitoring biomass dynamics is...
Article
Full-text available
Highlights • Construction changes are detected using the U-net model and satellite time series. • A novel solution for using the U-net model on small and isolated change objects. • This approach has the potential for monitoring global construction changes. Abstract Monitoring construction changes is essential for understanding the anthropogenic imp...
Article
A growing proportion of forested landscapes are interspersed with human infrastructure, such as utility lines and roads, increasing the potential for tree-failure consequences due to storms and other causes. Utilities and other institutions have strong incentives to reduce such interactions and allocate substantial resources to risk reduction, but...
Article
Near real-time (NRT) monitoring of land disturbances holds great importance for delivering emergency aid, mitigating negative social and ecological impacts, and distributing resources for disaster recovery. Many past NRT techniques were built upon examining the overall change magnitude of a spectral anomaly with a pre-defined threshold, namely the...
Chapter
Cet ouvrage traite de l’analyse des séries temporelles d’images de télédétection par apprentissages statistique, automatique et/ou profond.Détection de changements et analyse des séries temporelles d’images 2 présente un éventail de modèles et de méthodes supervisées d’analyse, d’extraction d’informations spatio-temporelles et de classification, à...
Article
Full-text available
State-of-the-art cloud computing platforms such as Google Earth Engine (GEE) enable regional-to-global land cover and land cover change mapping with machine learning algorithms. However, collection of high-quality training data, which is necessary for accurate land cover mapping, remains costly and labor-intensive. To address this need, we created...
Preprint
Near real time (NRT) monitoring of land disturbances holds great importance for delivering emergency aids, mitigating negative social and ecological impacts, and distributing resources for disaster recovery. Many past NRT techniques were built upon examining the overall change magnitude of a spectral anomaly with a pre-defined threshold, namely the...
Article
Full-text available
Understory plant communities are an integral component of deciduous forests, playing a vital role in the overall health of the ecosystem. However, remote sensing of understory plant communities is challenging due to the obstruction by the forest canopy. In this study, we proposed an automated dense Sentinel-2 time series-based approach for understo...
Article
An accurate mapping of land disturbances with regard to their timing and locations is the prerequisite for the success of downstream disturbance characterization. This study introduces and tests a novel approach that integrates a spatial perspective into the dense Landsat time series analysis, called "Object-Based COntinuous monitoring of Land Dist...
Article
Full-text available
The National Land Cover Database (NLCD) 2016 products show that, between 2001 and 2016, nearly half of the land cover change in the conterminous United States (CONUS) involved forested areas. To ensure the quality of NLCD land cover and land cover change products, it is important to accurately detect the location and time of forest disturbance. We...
Article
Full-text available
National Land Cover Database (NLCD) 2019 is a new epoch of national land cover products for the conterminous United States. Image quality is fundamental to the quality of any land cover product. Image preprocessing has often taken a considerable proportion of overall time and effort for this kind of national project. An approach to prepare image in...
Article
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Edited by Marie Weiss Keywords: Time series analysis Change detection CCDC Landsat Land use and land cover change A B S T R A C T The use of remote sensing in time series analysis enables wall-to-wall monitoring of the land surface and is critical for assessing and understanding land cover and land use change and for understanding the Earth system...
Article
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Monitoring nighttime light (NTL) change enables us to quantitatively analyze the dynamic patterns of human activity and socioeconomic features. NASA's Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) atmospheric- and Lunar-BRDF-corrected Black Marble product (VNP46A2) provides daily global nighttime radiances with high tempora...
Article
Full-text available
Land disturbance can increase carbon emissions, cause detrimental environmental impacts, and threaten human life and property. Monitoring land disturbance in near-real-time is essential to mitigate their negative effects and prevent future losses. However, rapid and timely monitoring of land disturbance at a high spatial resolution is in its infanc...
Article
Full-text available
Since 1972, the Landsat program has been continually monitoring the Earth, to now provide 50 years of digital, multispectral, medium spatial resolution observations. Over this time, Landsat data were crucial for many scientific and technical advances. Prior to the Landsat program, detailed, synoptic depictions of the Earth's surface were rare, and...
Article
Full-text available
Coastal tidal wetlands are highly altered ecosystems exposed to substantial risk due to widespread and frequent land-use change coupled with sea-level rise, leading to disrupted hydrologic and ecologic functions and ultimately, significant reduction in climate resiliency. Knowing where and when the changes have occurred, and the nature of those cha...
Article
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Forest covers about one-third of the land area of the conterminous United States (CONUS) and plays an important role in offsetting carbon emissions and supporting local economies. Growing interest in forests as relatively cost-effective nature-based climate solutions, particularly restoration and reforestation activities has increased the demand fo...
Article
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The Landsat program has the longest collection of moderate-resolution satellite imagery, and the data are free to everyone. With the improvements of standardized image products, the flexibility of cloud computing platforms, and the development of time series approaches, it is now possible to conduct global-scale analyses of time series using Landsa...
Article
Full-text available
Land cover maps are essential for characterizing the biophysical properties of the Earth’s land areas. Because land cover information synthesizes a rich array of information related to both the ecological condition of land areas and their exploitation by humans, they are widely used for basic and applied research that requires information related t...
Article
Full-text available
Development and a growing population in Saudi Arabia have led to a substantial increase in the size of its urban areas. This sustained development has increased policymakers’ need for reliable data and analysis regarding the patterns and trends of urban expansion throughout the country. Although previous studies on urban growth in Saudi cities exis...
Article
Full-text available
Harmonic analysis of time series is an important technique to reveal seasonal land surface dynamics using remote sensing information. However, frequency selection in the harmonic analysis is often difficult because high-frequency components are useful for delineating seasonal dynamics but sensitive to noise and gaps in time series. On the other han...
Article
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Remotely sensing the spatial extent of artificial surfaces is essential for understanding human footprints and evaluating anthropogenic impacts on the global environment and climate. However, since the spectral signatures of artificial surfaces are similar to other bright natural surfaces, it is still a difficult task to separate artificial surface...
Article
Full-text available
Many remote sensing studies have individually addressed afforestation, forest disturbance and forest regeneration, and considered land use history. However, no single study has simultaneously addressed all of these components that collectively constitute successional stages and pathways of young forest and shrubland at large spatial extents. Our go...
Article
Full-text available
Sample-based estimates augmented by complete coverage land-cover maps were used to estimate area and describe patterns of annual land-cover change across the conterminous United States (CONUS) between 1985 and 2016. Most of the CONUS land cover remained stable in terms of net class change over this time, but a substantial gross change dynamic was c...
Article
Full-text available
The increasing availability of high-quality remote sensing data and advanced technologies has spurred land cover mapping to characterize land change from local to global scales. However, most land change datasets either span multiple decades at a local scale or cover limited time over a larger geographic extent. Here, we present a new land cover an...
Chapter
This chapter presents an overview of the main time series analysis methods for environment monitoring with earth observation, from classical methods to the deep learning (DL) methods. It summarizes main differences between bi-temporal change detection, annual time series and dense time series analyses, and also presents the three main types of annu...
Article
Full-text available
PlanetScope satellite data with a 3-m resolution and near-daily global coverage have been increasingly used for land surface monitoring, ranging from land cover change detection to vegetative biophysics characterization and ecological assessments. Similar to other satellite data, effective screening of clouds and cloud shadows in PlanetScope images...
Article
In contrast to abrupt changes caused by land cover conversion, subtle changes driven by a shift in the condition, structure, or other biological attributes of land often lead to minimal and slower alterations of the terrestrial surface. Accurate mapping and monitoring of subtle change are crucial for an early warning of long-term gradual change tha...
Preprint
Full-text available
The increasing availability of high-quality remote sensing data and advanced technologies have spurred land cover mapping to characterize land change from local to global scales. However, most land change datasets either span multiple decades at a local scale or cover limited time over a larger geographic extent. Here, we present a new land cover a...
Article
Full-text available
Since 2017, the orbit of Landsat 7 has drifted outside its nominal mission requirement toward an earlier acquisition time because of limited onboard fuel resources. This makes quantitative analyses from Landsat 7 data potentially unreliable for many scientific studies. To comprehensively understand the effect of ongoing (2018–2020) orbit drift on L...
Article
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We evaluated the performance of a variety of time series models, that include the harmonic (HR) model, autoregressive (AR) model, linear Gaussian state-space (LGSS) model, cubic spline (SP) model, double logistic (DL) model, and asymmetric Gaussian (AG) model, for reconstructing (all six models) and forecasting (HR, AR, and LGSS models) dense Lands...
Article
Full-text available
Forest disturbances greatly affect the ecological functioning of natural forests. Timely information regarding extent, timing and magnitude of forest disturbance events is crucial for effective disturbance management strategies. Yet, we still lack accurate, near-real-time and high-performance remote sensing tools for monitoring abrupt and subtle fo...
Article
Full-text available
Cloud Cloud shadow Fmask Tmask MAJA Sen2Cor LaSRC Cloud detection Cloud mask A B S T R A C T Accurate, automated cloud and cloud shadow detection is a key component of the processing needed to prepare optical satellite imagery for scientific analysis. Many existing cloud detection algorithms rely on temperature information to identify clouds, makin...
Article
Full-text available
We developed an algorithm called Cmask (Cirrus cloud mask) for cirrus cloud detection in Landsat 8 imagery using time series of Cirrus Band (1.36-1.39 μm) observations. For each pixel, a harmonic model, which includes a water vapor regressor, based on all available Cirrus Band observations is estimated using the Robust Iteratively Reweighted Least...
Article
The first national product of Surface Water Dynamics in France (SWDF) is generated on a monthly temporal scale and 10-m spatial scale using an automatic rule-based superpixel (RBSP) approach. The current surface water dynamic products from high resolution (HR) multispectral satellite imagery are typically analyzed to determine the annual trend and...
Article
Full-text available
Disturbance monitoring is an important application of the Landsat times series, both to monitor forest dynamics and to support wise forest management at a variety of spatial and temporal scales. In the last decade, there has been an acceleration in the development of approaches designed to put the Landsat archive to use towards these causes. Forest...
Preprint
Forest disturbances greatly affect the ecological functioning of natural forests. Timely information regarding extent, timing and magnitude of forest disturbance events is crucial for effective disturbance management strategies. Yet, we still lack an acute, near-real-time and high-performance remote sensing tools for monitoring abrupt and subtle fo...
Article
Full-text available
Monitoring cotton status during the growing season is critical in increasing production efficiency. The water status in cotton is a key factor for yield and cotton quality. Stem water potential (SWP) is a precise indicator for assessing cotton water status. Satellite remote sensing is an effective approach for monitoring cotton growth at a large sc...
Article
Full-text available
To understand the timing, extent, and magnitude of land use/land cover (LULC) change in buffer areas surrounding Midwestern US waters, we analyzed the full imagery archive (1982-2017) of three Landsat footprints covering ~100,000 km 2. The study area included urbanizing Chicago, Illinois and St. Louis, Missouri regions and agriculturally dominated...
Article
Full-text available
The National Land Cover Database (NLCD) 2016 provides a suite of data products, including land cover and land cover change of the conterminous United States from 2001 to 2016, at two- to three-year intervals. The development of this product is part of an effort to meet the growing demand for longer temporal duration and more frequent, accurate, and...
Article
Full-text available
We developed a Time-series-based Reflectance Adjustment (TRA) approach for reducing the reflectance differences between Landsat 8 and Sentinel-2 observations. This TRA approach used the time series of matched Landsat 8 and Sentinel-2 observations to build linear regression models to adjust reflectance differences between the two sensors for each in...
Article
Full-text available
The free and open policy of Landsat data in 2008 completely changed the way that Landsat data was analyzed and used, particularly for applications such as time series analysis. Nine years later, the United States Geological Survey (USGS) released the first version of Landsat Analysis Ready Data (ARD) for the United States, which was another milesto...
Preprint
Full-text available
Growing demands for temporally specific information on land surface change are fueling a new generation of maps and statistics that can contribute to understanding geographic and temporal patterns of change across large regions, provide input into a wide range of environmental modeling studies, clarify the drivers of change, and provide more timely...
Article
Full-text available
Scientific contributions from remote sensing over the last fifty years have significantly advanced our understanding of urban areas. Key contributions of urban remote sensing include but are not limited to characterization of urban areas, urban land cover changes and thermal remote sensing of urban climates. Today, the proliferation of new sensors,...
Article
Accurate delineation of global built-up area (BUA) is fundamental to a better understanding of human development and the impacts on global environmental change. Existing global datasets of human settlement were mostly generated at medium and coarse spatial resolutions, including BUA and other impervious surfaces. With multiple high-resolution satel...
Article
Accurate delineation of global built-up area (BUA) is fundamental to a better understanding of human development and the impacts on global environmental change. Existing global datasets of human settlement were mostly generated at medium and coarse spatial resolutions, including BUA and other impervious surfaces. With multiple high-resolution satel...