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Author: search.filters.author.Liang, ShunlinSubject: search.filters.subject.remote sensing

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Now showing 1 - 6 of 6
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    Developing an Integrated Remote Sensing Based Biodiversity Index for Predicting Animal Species Richness
    (MDPI, 2018-05-10) Wu, Jinhui; Liang, Shunlin
    Many remote sensing metrics have been applied in large-scale animal species monitoring and conservation. However, the capabilities of these metrics have not been well compared and assessed. In this study, we investigated the correlation of 21 remote sensing metrics in three categories with the global species richness of three different animal classes using several statistical methods. As a result, we developed a new index by integrating several highly correlated metrics. Of the 21 remote sensing metrics analyzed, evapotranspiration (ET) had the greatest impact on species richness on a global scale (explained variance: 52%). The metrics with a high explained variance on the global scale were mainly in the energy/productivity category. The metrics in the texture category exhibited higher correlation with species richness at regional scales. We found that radiance and temperature had a larger impact on the distribution of bird richness, compared to their impacts on the distributions of both amphibians and mammals. Three machine learning models (i.e., support vector machine, random forests, and neural networks) were evaluated for metric integration, and the random forest model showed the best performance. Our newly developed index exhibited a 0.7 explained variance for the three animal classes’ species richness on a global scale, with an explained variance that was 20% higher than any of the univariate metrics.
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    Assessing Terrestrial Ecosystem Resilience using Satellite Leaf Area Index
    (MDPI, 2020-02-11) Wu, Jinhui; Liang, Shunlin
    Quantitative approaches to measuring and assessing terrestrial ecosystem resilience, which expresses the ability of an ecosystem to recover from disturbances without shifting to an alternative state or losing function and services, is critical and essential to forecasting how terrestrial ecosystems will respond to global change. However, global and continuous terrestrial resilience measurement is fraught with difficulty, and the corresponding attribution of resilience dynamics is lacking in the literature. In this study, we assessed global terrestrial ecosystem resilience based on the long time-series GLASS LAI product and GIMMS AVHRR LAI 3g product, and validated the results using drought and fire events as the main disturbance indicators. We also analyzed the spatial and temporal variations of global terrestrial ecosystem resilience and attributed their dynamics to climate change and environmental factors. The results showed that arid and semiarid areas exhibited low resilience. We found that evergreen broadleaf forest exhibited the highest resilience (mean resilience value (from GLASS LAI): 0.6). On a global scale, the increase of mean annual precipitation had a positive impact on terrestrial resilience enhancement, while we found no consistent relationships between mean annual temperature and terrestrial resilience. For terrestrial resilience dynamics, we observed three dramatic raises of disturbance frequency in 1989, 1995, and 2001, respectively, along with three significant drops in resilience correspondingly. Our study mapped continuous spatiotemporal variation and captured interannual variations in terrestrial ecosystem resilience. This study demonstrates that remote sensing data are effective for monitoring terrestrial resilience for global ecosystem assessment.
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    An Optimization Algorithm for Separating Land Surface Temperature and Emissivity from Multispectral Thermal Infrared Imagery
    (Institute of Electrical and Electronics Engineers, 2001-02) Liang, Shunlin
    Land surface temperature (LST) and emissivity are important components of land surface modeling and applications. The only practical means of obtaining LST at spatial and temporal resolutions appropriate for most modeling applications is through remote sensing. While the popular split-window method has been widely used to estimate LST, it requires known emissivity values. Multispectral thermal infrared imagery provides us with an excellent opportunity to estimate both LST and emissivity simultaneously, but the difficulty is that a single multispectral thermal measurement with bands presents equations in + 1 unknowns ( spectral emissivities and LST). In this study, we developed a general algorithm that can separate land surface emissivity and LST from any multispectral thermal imagery, such as moderate-resolution imaging spectroradiometer (MODIS) and advanced spaceborne thermal emission and reflection radiometer (ASTER). The central idea was to establish empirical constraints, and regularization methods were used to estimate both emissivity and LST through an optimization algorithm. It allows us to incorporate any prior knowledge in a formal way. The numerical experiments showed that this algorithm is very effective (more than 43.4% inversion results differed from the actual LST within 0.5 , 70.2% within 1 and 84% within 1.5 ), although improvements are still needed.
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    A Direct Algorithm for Estimating Land Surface Broadband Albedos From MODIS Imagery
    (Institute of Electrical and Electronics Engineers, 2003-01) Liang, Shunlin
    Land surface albedo is a critical variable needed in land surface modeling. The conventional methods for estimating broadband albedos rely on a series of steps in the processing chain, including atmospheric correction, surface angular modeling, and narrowband-to-broadband albedo conversions. Unfortunately, errors associated with each procedure may be accumulated and significantly impact the accuracy of the final albedo products. In an earlier study, we developed a new direct procedure that links the top-of-atmosphere spectral albedos with land surface broadband albedos without performing atmospheric correction and other processes. In this paper, this method is further improved in several aspects and implemented using actual Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Several case studies indicated that this new method can predict land surface broadband albedos very accurately and eliminate aerosol effects effectively. It is very promising for global applications and is particularly suitable for nonvegetated land surfaces. Note that a Lambertian surface has been assumed in the radiative transfer simulation in this paper as a first-order approximation; this assumption can be easily removed as long as a global bidirectional reflectance distribution function climatology is available,
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    Special Issue on Global Land Product Validation
    (Institute of Electrical and Electronics Engineers, 2006-07) Liang, Shunlin; Baret, Frédéric; Morisette, Jeffrey T.
    Overview of the Special Issue on Global Land Product Validation: In parallel with the recent bloom of sensors providing frequent medium-resolution observations (Fig. 1), global land products have been increasingly developed and released within the community. The raw data acquired by these sensors are transformed into higher level products that can be more easily exploited by the user community. In many cases, multiple products are developed from each sensor and similar products derived from different sensors. With this, users need access to quantitative information on product uncertainties to help them assess the most suitable product, or combination of products for their specific needs. As remote sensing observations are generally merged with other sources of information or assimilated within process models, evaluation of product accuracy is required. Making quantified accuracy information available to the user can ultimately provide developers the necessary feedback for improving the products, and can possibly provide methods for their fusion to construct a consistent long-term series of surface status.
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    An Improved Atmospheric Correction Algorithm for Hyperspectral Remotely Sensed Imagery
    (Institute of Electrical and Electronics Engineers, 2004-04) Liang, Shunlin; Fang, Hongliang
    There is an increased trend toward quantitative estimation of land surface variables from hyperspectral remote sensing. One challenging issue is retrieving surface reflectance spectra from observed radiance through atmospheric correction, most methods for which are intended to correct water vapor and other absorbing gases. In this letter, methods for correcting both aerosols and water vapor are explored. We first apply the cluster matching technique developed earlier for Landsat-7 ETM+ imagery to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data, then improve its aerosol estimation and incorporate a new method for estimating column water vapor content using the neural network technique. The improved algorithm is then used to correct Hyperion imagery. Case studies using AVIRIS and Hyperion images demonstrate that both the original and improved methods are very effective to remove heterogeneous atmospheric effects and recover surface reflectance spectra.