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    Characterizing Low-Lying Coastal Upland Forests to Predict Future Landward Marsh Expansion
    (Ecological Society of America, 2024) Powell, Elisabeth; Dubayah, Ralph; Stovall, Atticus, E.L.
    Sea level rise (SLR) is causing vegetation regime shifts on both the seaward and landward sides of many coastal ecosystems, with the Eastern coast of North America experiencing accelerated impacts due to land subsidence and the weakening of the Gulf Stream. Tidal wetland ecosystems, known for their significant carbon storage capacity, are crucial but vulnerable blue carbon habitats. Recent observations suggest that SLR rates may exceed the threshold for elevation gain primarily through vertical accretion in many coastal regions. Therefore, research has focused on mapping the upslope migration of marshes into suitable adjacent lands, as this landward gain may be the most salient process for estimating future wetland resiliency to accelerated rates of SLR. However, our understanding of coastal vegetation characteristics and dynamics in response to SLR is limited due to a lack of in-situ data and effective mapping strategies for delineating the boundaries, or ecotones, of these complex coastal ecosystems. In order to effectively study these transitioning ecosystems, it is necessary to employ reliable and scalable landscape metrics that can differentiate between marsh and coastal forests. As such, integrating vegetation structure metrics from Light detection and ranging (Lidar) could enhance traditional mapping strategies compared to using optical data alone. Here, we used terrestrial laser scanning (TLS) to measure changes in forest structure along elevation gradients that may be indicative of degradation associated with increased inundation in the Delaware Bay estuary. We analyzed a set of TLS-derived forest structure metrics to investigate their relationships with elevation, specifically seeking those that showed consistent change from the forest edge to the interior. Our findings revealed a consistent pattern between elevation and the Plant Area Index (PAI), a metric that holds potential for enhancing the delineation of complex coastal ecosystem boundaries, particularly in relation to landward marsh migration. This work provides support for utilizing lidar-derived forest structural metrics to enable a more accurate assessment of future marsh landscapes and the overall coastal carbon sink under accelerated sea-level rise conditions.
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    Changing Lifestyles Towards a Low Carbon Economy: An IPAT Analysis for China
    (MDPI, 2011-12-27) Hubacek, Klaus; Feng, Kuishuang; Chen, Bin
    China has achieved notable success in developing its economy with approximate 10 percent average annual GDP growth over the last two decades. At the same time, energy consumption and CO2 emissions almost doubled every five years, which led China to be the world top emitter in 2007. In response, China’s government has put forward a carbon mitigation target of 40%–45% reduction of CO2 emission intensity by 2020. To better understand the potential for success or failure of such a policy, it is essential to assess different driving forces such as population, lifestyle and technology and their associated CO2 emissions. This study confirms that increase of affluence has been the main driving force for China’s CO2 emissions since the late 1970s, which outweighs reductions achieved through technical progress. Meanwhile, the contribution of population growth to CO2 emissions was relatively small. We also found a huge disparity between urban and rural households in terms of changes of lifestyle and consumption patterns. Lifestyles in urban China are beginning to resemble Western lifestyles, and approaching their level of CO2 emissions. Therefore, in addition to the apparent inefficiencies in terms of production technologies there is also a lot of room for improvement on the consumption side especially in interaction of current infrastructure investments and future consumption.
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    Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA). Part 1: Introduction
    (MDPI, 2012-09-14) Baraldi, Andrea; Boschetti, Luigi
    According to existing literature and despite their commercial success, state-of-the-art two-stage non-iterative geographic object-based image analysis (GEOBIA) systems and three-stage iterative geographic object-oriented image analysis (GEOOIA) systems, where GEOOIA ⊃ GEOBIA, remain affected by a lack of productivity, general consensus and research. To outperform the degree of automation, accuracy, efficiency, robustness, scalability and timeliness of existing GEOBIA/GEOOIA systems in compliance with the Quality Assurance Framework for Earth Observation (QA4EO) guidelines, this methodological work is split into two parts. The present first paper provides a multi-disciplinary Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis of the GEOBIA/GEOOIA approaches that augments similar analyses proposed in recent years. In line with constraints stemming from human vision, this SWOT analysis promotes a shift of learning paradigm in the pre-attentive vision first stage of a remote sensing (RS) image understanding system (RS-IUS), from sub-symbolic statistical model-based (inductive) image segmentation to symbolic physical model-based (deductive) image preliminary classification. Hence, a symbolic deductive pre-attentive vision first stage accomplishes image sub-symbolic segmentation and image symbolic pre-classification simultaneously. In the second part of this work a novel hybrid (combined deductive and inductive) RS-IUS architecture featuring a symbolic deductive pre-attentive vision first stage is proposed and discussed in terms of: (a) computational theory (system design); (b) information/knowledge representation; (c) algorithm design; and (d) implementation. As proof-of-concept of symbolic physical model-based pre-attentive vision first stage, the spectral knowledge-based, operational, near real-time Satellite Image Automatic Mapper™ (SIAM™) is selected from existing literature. To the best of these authors’ knowledge, this is the first time a symbolic syntactic inference system, like SIAM™, is made available to the RS community for operational use in a RS-IUS pre-attentive vision first stage, to accomplish multi-scale image segmentation and multi-granularity image pre-classification simultaneously, automatically and in near real-time.
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    Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA). Part 2: Novel system Architecture, Information/Knowledge Representation, Algorithm Design and Implementation
    (MDPI, 2012-09-20) Baraldi, Andrea; Boschetti, Luigi
    According to literature and despite their commercial success, state-of-the-art two-stage non-iterative geographic object-based image analysis (GEOBIA) systems and three-stage iterative geographic object-oriented image analysis (GEOOIA) systems, where GEOOIA ⊃ GEOBIA, remain affected by a lack of productivity, general consensus and research. To outperform the Quality Indexes of Operativeness (OQIs) of existing GEOBIA/GEOOIA systems in compliance with the Quality Assurance Framework for Earth Observation (QA4EO) guidelines, this methodological work is split into two parts. Based on an original multi-disciplinary Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis of the GEOBIA/GEOOIA approaches, the first part of this work promotes a shift of learning paradigm in the pre-attentive vision first stage of a remote sensing (RS) image understanding system (RS-IUS), from sub-symbolic statistical model-based (inductive) image segmentation to symbolic physical model-based (deductive) image preliminary classification capable of accomplishing image sub-symbolic segmentation and image symbolic pre-classification simultaneously. In the present second part of this work, a novel hybrid (combined deductive and inductive) RS-IUS architecture featuring a symbolic deductive pre-attentive vision first stage is proposed and discussed in terms of: (a) computational theory (system design), (b) information/knowledge representation, (c) algorithm design and (d) implementation. As proof-of-concept of symbolic physical model-based pre-attentive vision first stage, the spectral knowledge-based, operational, near real-time, multi-sensor, multi-resolution, application-independent Satellite Image Automatic Mapper™ (SIAM™) is selected from existing literature. To the best of these authors’ knowledge, this is the first time a symbolic syntactic inference system, like SIAM™, is made available to the RS community for operational use in a RS-IUS pre-attentive vision first stage, to accomplish multi-scale image segmentation and multi-granularity image pre-classification simultaneously, automatically and in near real-time.
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    A Sample-Based Forest Monitoring Strategy Using Landsat, AVHRR and MODIS Data to Estimate Gross Forest Cover Loss in Malaysia between 1990 and 2005
    (MDPI, 2013-04-15) Giree, Namita; Stehman, Stephen V.; Potapov, Peter; Hansen, Matthew C.
    Insular Southeast Asia is a hotspot of humid tropical forest cover loss. A sample-based monitoring approach quantifying forest cover loss from Landsat imagery was implemented to estimate gross forest cover loss for two eras, 1990–2000 and 2000–2005. For each time interval, a probability sample of 18.5 km × 18.5 km blocks was selected, and pairs of Landsat images acquired per sample block were interpreted to quantify forest cover area and gross forest cover loss. Stratified random sampling was implemented for 2000–2005 with MODIS-derived forest cover loss used to define the strata. A probability proportional to x (πpx) design was implemented for 1990–2000 with AVHRR-derived forest cover loss used as the x variable to increase the likelihood of including forest loss area in the sample. The estimated annual gross forest cover loss for Malaysia was 0.43 Mha/yr (SE = 0.04) during 1990–2000 and 0.64 Mha/yr (SE = 0.055) during 2000–2005. Our use of the πpx sampling design represents a first practical trial of this design for sampling satellite imagery. Although the design performed adequately in this study, a thorough comparative investigation of the πpx design relative to other sampling strategies is needed before general design recommendations can be put forth.
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    Characterization of Canopy Layering in Forested Ecosystems Using Full Waveform Lidar
    (MDPI, 2013-04-22) Whitehurst, Amanda S.; Swatantran, Anu; Blair, J. Bryan; Hofton, Michelle A.; Dubayah, Ralph
    Canopy structure, the vertical distribution of canopy material, is an important element of forest ecosystem dynamics and habitat preference. Although vertical stratification, or “canopy layering,” is a basic characterization of canopy structure for research and forest management, it is difficult to quantify at landscape scales. In this paper we describe canopy structure and develop methodologies to map forest vertical stratification in a mixed temperate forest using full-waveform lidar. Two definitions—one categorical and one continuous—are used to map canopy layering over Hubbard Brook Experimental Forest, New Hampshire with lidar data collected in 2009 by NASA’s Laser Vegetation Imaging Sensor (LVIS). The two resulting canopy layering datasets describe variation of canopy layering throughout the forest and show that layering varies with terrain elevation and canopy height. This information should provide increased understanding of vertical structure variability and aid habitat characterization and other forest management activities.
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    Quality Assessment of Pre-Classification Maps Generated from Spaceborne/Airborne Multi-Spectral Images by the Satellite Image Automatic Mapper™ and Atmospheric/Topographic Correction™-Spectral Classification Software Products: Part 2 — Experimental Results
    (MDPI, 2013-10-18) Baraldi, Andrea; Humber, Michael; Boschetti, Luigi
    This paper complies with the Quality Assurance Framework for Earth Observation (QA4EO) international guidelines to provide a metrological/statistically-based quality assessment of the Spectral Classification of surface reflectance signatures (SPECL) secondary product, implemented within the popular Atmospheric/Topographic Correction (ATCOR™) commercial software suite, and of the Satellite Image Automatic Mapper™ (SIAM™) software product, proposed to the remote sensing (RS) community in recent years. The ATCOR™-SPECL and SIAM™ physical model-based expert systems are considered of potential interest to a wide RS audience: in operating mode, they require neither user-defined parameters nor training data samples to map, in near real-time, a spaceborne/airborne multi-spectral (MS) image into a discrete and finite set of (pre-attentional first-stage) spectral-based semi-concepts (e.g., “vegetation”), whose informative content is always equal or inferior to that of target (attentional second-stage) land cover (LC) concepts (e.g., “deciduous forest”). For the sake of simplicity, this paper is split into two: Part 1—Theory and Part 2—Experimental results. The Part 1 provides the present Part 2 with an interdisciplinary terminology and a theoretical background. To comply with the principle of statistics and the QA4EO guidelines discussed in the Part 1, the present Part 2 applies an original adaptation of a novel probability sampling protocol for thematic map quality assessment to the ATCOR™-SPECL and SIAM™ pre-classification maps, generated from three spaceborne/airborne MS test images. Collected metrological/statistically-based quality indicators (QIs) comprise: (i) an original Categorical Variable Pair Similarity Index (CVPSI), capable of estimating the degree of match between a test pre-classification map’s legend and a reference LC map’s legend that do not coincide and must be harmonized (reconciled); (ii) pixel-based Thematic (symbolic, semantic) QIs (TQIs) and (iii) polygon-based sub-symbolic (non-semantic) Spatial QIs (SQIs), where all TQIs and SQIs are provided with a degree of uncertainty in measurement. Main experimental conclusions of the present Part 2 are the following. (I) Across the three test images, the CVPSI values of the SIAM™ pre-classification maps at the intermediate and fine semantic granularities are superior to those of the ATCOR™-SPECL single-granule maps. (II) TQIs of both the ATCOR™-SPECL and the SIAM™ tend to exceed community-agreed reference standards of accuracy. (III) Across the three test images and the SIAM™’s three semantic granularities, TQIs of the SIAM™ tend to be significantly higher (in statistical terms) than the ATCOR™-SPECL’s. Stemming from the proposed experimental evidence in support to theoretical considerations, the final conclusion of this paper is that, in compliance with the QA4EO objectives, the SIAM™ software product can be considered eligible for injecting prior spectral knowledge into the pre-attentive vision first stage of a novel generation of hybrid (combined deductive and inductive) RS image understanding systems, capable of transforming large-scale multi-source multi-resolution EO image databases into operational, comprehensive and timely knowledge/information products.
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    Quantification of Impact of Orbital Drift on Inter-Annual Trends in AVHRR NDVI Data
    (MDPI, 2014-07-22) Nagol, Jyoteshwar R.; Vermote, Eric F.; Prince, Stephen D.
    The Normalized Difference Vegetation Index (NDVI) time-series data derived from Advanced Very High Resolution Radiometer (AVHRR) have been extensively used for studying inter-annual dynamics of global and regional vegetation. However, there can be significant uncertainties in the data due to incomplete atmospheric correction and orbital drift of the satellites through their active life. Access to location specific quantification of uncertainty is crucial for appropriate evaluation of the trends and anomalies. This paper provides per pixel quantification of orbital drift related spurious trends in Long Term Data Record (LTDR) AVHRR NDVI data product. The magnitude and direction of the spurious trends was estimated by direct comparison with data from MODerate resolution Imaging Spectrometer (MODIS) Aqua instrument, which has stable inter-annual sun-sensor geometry. The maps show presence of both positive as well as negative spurious trends in the data. After application of the BRDF correction, an overall decrease in positive trends and an increase in number of pixels with negative spurious trends were observed. The mean global spurious inter-annual NDVI trend before and after BRDF correction was 0.0016 and −0.0017 respectively. The research presented in this paper gives valuable insight into the magnitude of orbital drift related trends in the AVHRR NDVI data as well as the degree to which it is being rectified by the MODIS BRDF correction algorithm used by the LTDR processing stream.
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    Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics
    (MDPI, 2014-10-13) Dempewolf, Jan; Adusei, Bernard; Becker-Reshef, Inbal; Hansen, Matthew; Potapov, Peter; Khan, Ahmad; Barker, Brian
    Policy makers, government planners and agricultural market participants in Pakistan require accurate and timely information about wheat yield and production. Punjab Province is by far the most important wheat producing region in the country. The manual collection of field data and data processing for crop forecasting by the provincial government requires significant amounts of time before official reports can be released. Several studies have shown that wheat yield can be effectively forecast using satellite remote sensing data. In this study, we developed a methodology for estimating wheat yield and area for Punjab Province from freely available Landsat and MODIS satellite imagery approximately six weeks before harvest. Wheat yield was derived by regressing reported yield values against time series of four different peak-season MODIS-derived vegetation indices. We also tested deriving wheat area from the same MODIS time series using a regression-tree approach. Among the four evaluated indices, WDRVI provided more consistent and accurate yield forecasts compared to NDVI, EVI2 and saturation-adjusted normalized difference vegetation index (SANDVI). The lowest RMSE values at the district level for forecast versus reported yield were found when using six or more years of training data. Forecast yield for the 2007/2008 to 2012/2013 growing seasons were within 0.2% and 11.5% of final reported values. Absolute deviations of wheat area and production forecasts from reported values were slightly greater compared to using the previous year’s or the three- or six-year moving average values, implying that 250-m MODIS data does not provide sufficient spatial resolution for providing improved wheat area and production forecasts.
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    Long-Term Record of Sampled Disturbances in Northern Eurasian Boreal Forest from Pre-2000 Landsat Data
    (MDPI, 2014-06-27) Chen, Dong; Loboda, Tatiana; Channan, Saurabh; Hoffman-Hall, Amanda
    Stand age distribution is an important descriptor of boreal forest structure, which is directly linked to many ecosystem processes including the carbon cycle, the land–atmosphere interaction and ecosystem services, among others. Almost half of the global boreal biome is located in Russia. The vast extent, remote location, and limited accessibility of Russian boreal forests make remote sensing the only feasible approach to characterize these forests to their full extent. A wide variety of satellite observations are currently available to monitor forest change and infer its structure; however, the period of observations is mostly limited to the 2000s era. Reconstruction of wall-to-wall maps of stand age distribution requires merging longer-term site observations of forest cover change available at the Landsat scale at a subset of locations in Russia with the wall-to-wall coverage available from coarse resolution satellites since 2000. This paper presents a dataset consisting of a suite of multi-year forest disturbance samples and samples of undisturbed forests across Russia derived from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus images from 1985 to 2000. These samples provide crucial information regarding disturbance history in selected regions across the Russian boreal forest and are designed to serve as a training and/or validation dataset for coarse resolution data products. The overall accuracy and Kappa coefficient for the entire sample collection was found to be 83.98% and 0.83%, respectively. It is hoped that the presented dataset will benefit subsequent studies on a variety of aspects of the Russian boreal forest, especially in relation to the carbon budget and climate.
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    A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)
    (MDPI, 2015-01-29) Whitcraft, Alyssa K.; Becker-Reshef, Inbal; Justice, Christopher O.
    Global agricultural monitoring utilizes a variety of Earth observations (EO) data spanning different spectral, spatial, and temporal resolutions in order to gather information on crop area, type, condition, calendar, and yield, among other applications. Categorical requirements for space-based monitoring of major agricultural production areas have been articulated based on best practices established by the Group on Earth Observation’s (GEO) Global Agricultural Monitoring Community (GEOGLAM) of Practice, in collaboration with the Committee on Earth Observation Satellites (CEOS). We present a method to transform generalized requirements for agricultural monitoring in the context of GEOGLAM into spatially explicit (0.05°) Earth observation (EO) requirements for multiple resolutions of data. This is accomplished through the synthesis of the necessary remote sensing-based datasets concerning where (crop mask, when (growing calendar, and how frequently imagery is required (considering cloud cover impact throughout the agricultural growing season. Beyond this provision of the framework and tools necessary to articulate these requirements, investigated in depth is the requirement for reasonably clear moderate spatial resolution (10–100 m) optical data within 8 days over global within-season croplands of all sizes, a data type prioritized by GEOGLAM and CEOS. Four definitions of “reasonably clear” are investigated: 70%, 80%, 90%, or 95% clear. The revisit frequency required (RFR) for a reasonably clear view varies greatly both geographically and throughout the growing season, as well as with the threshold of acceptable clarity. The global average RFR for a 70% clear view within 8 days is 3.9–4.8 days (depending on the month), 3.0–4.1 days for 80% clear, 2.2–3.3 days for 90% clear, and 1.7–2.6 days for 95% clear. While some areas/times of year require only a single revisit (RFR = 8 days) to meet their reasonably clear requirement, generally the RFR, regardless of clarity threshold, is below to greatly below the 8 day mark, highlighting the need for moderate resolution optical satellite systems or constellations with revisit capabilities more frequent than 8 days. This analysis is providing crucial input for data acquisition planning for agricultural monitoring in the context of GEOGLAM.
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    Meeting Earth Observation Requirements for Global Agricultural Monitoring: An Evaluation of the Revisit Capabilities of Current and Planned Moderate Resolution Optical Earth Observing Missions
    (MDPI, 2015-01-29) Whitcraft, Alyssa K.; Becker-Reshef, Inbal; Killough, Brian D.; Justice, Christopher O.
    Agriculture is a highly dynamic process in space and time, with many applications requiring data with both a relatively high temporal resolution (at least every 8 days) and fine-to-moderate (FTM < 100 m) spatial resolution. The relatively infrequent revisit of FTM optical satellite observatories coupled with the impacts of cloud occultation have translated into a barrier for the derivation of agricultural information at the regional-to-global scale. Drawing upon the Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) Initiative’s general satellite Earth observation (EO) requirements for monitoring of major production areas, Whitcraft et al. (this issue) have described where, when, and how frequently satellite data acquisitions are required throughout the agricultural growing season at 0.05°, globally. The majority of areas and times of year require multiple revisits to probabilistically yield a view at least 70%, 80%, 90%, or 95% clear within eight days, something that no present single FTM optical observatory is capable of delivering. As such, there is a great potential to meet these moderate spatial resolution optical data requirements through a multi-space agency/multi-mission constellation approach. This research models the combined revisit capabilities of seven hypothetical constellations made from five satellite sensors—Landsat 7 Enhanced Thematic Mapper (Landsat 7 ETM+), Landsat 8 Operational Land Imager and Thermal Infrared Sensor (Landsat 8 OLI/TIRS), Resourcesat-2 Advanced Wide Field Sensor (Resourcesat-2 AWiFS), Sentinel-2A Multi-Spectral Instrument (MSI), and Sentinel-2B MSI—and compares these capabilities with the revisit frequency requirements for a reasonably cloud-free clear view within eight days throughout the agricultural growing season. Supplementing Landsat 7 and 8 with missions from different space agencies leads to an improved capacity to meet requirements, with Resourcesat-2 providing the largest incremental improvement in requirements met. The best performing constellation can meet 71%–91% of the requirements for a view at least 70% clear, and 45%–68% of requirements for a view at least 95% clear, varying by month. Still, gaps exist in persistently cloudy regions/periods, highlighting the need for data coordination and for consideration of active EO for agricultural monitoring. This research highlights opportunities, but not actual acquisition rates or data availability/access; systematic acquisitions over actively cropped agricultural areas as well as a policy which guarantees continuous access to high quality, interoperable data are essential in the effort to meet EO requirements for agricultural monitoring.
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    Sensitivity of Multi-Source SAR Backscatter to Changes in Forest Aboveground Biomass
    (MDPI, 2015-07-28) Huang, Wenli; Sun, Guoqing; Ni, Wenjian; Zhang, Zhiyu; Dubayah, Ralph
    Accurate estimates of forest aboveground biomass (AGB) after anthropogenic disturbance could reduce uncertainties in the carbon budget of terrestrial ecosystems and provide critical information to policy makers. Yet, the loss of carbon due to forest disturbance and the gain from post-disturbance recovery have not been sufficiently assessed. In this study, a sensitivity analysis was first conducted to investigate: (1) the influence of incidence angle and soil moisture on Synthetic Aperture Radar (SAR) backscatter; (2) the feasibility of cross-image normalization between multi-temporal and multi-sensor SAR data; and (3) the possibility of applying normalized backscatter data to detect forest biomass changes. An empirical model was used to reduce incidence angle effects, followed by cross-image normalization procedure to lessen soil moisture effect. Changes in forest biomass at medium spatial resolution (100 m) were mapped using both spaceborne and airborne SAR data. Results indicate that (1) the effect of incidence angle on SAR backscatter could be reduced to less than 1 dB by the correction model for airborne SAR data; (2) over 50% of the changes in SAR backscatter due to soil moisture could be eliminated by the cross-image normalization procedure; and (3) forest biomass changes greater than 100 Mg·ha−1 or above 50% of 150 Mg·ha−1 are detectable using cross-normalized SAR data.
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    Surface Shortwave Net Radiation Estimation from FengYun-3 MERSI Data
    (MDPI, 2015-05-19) Wang, Dongdong; Liang, Shunlin; He, Tao; Cao, Yunfeng; Jiang, Bo
    The Medium-Resolution Spectral Imager (MERSI) is one of the major payloads of China’s second-generation polar-orbiting meteorological satellite, FengYun-3 (FY-3), and it is similar to the Moderate-Resolution Imaging Spectroradiometer (MODIS). The MERSI data are suitable for mapping terrestrial, atmospheric and oceanographic variables at continental to global scales. This study presents a direct-estimation method to retrieve surface shortwave net radiation (SSNR) data from MERSI top-of-atmosphere (TOA) reflectance and cloud mask products. This study is the first attempt to use the MERSI to retrieve SSNR data. Several critical issues concerning remote sensing of SSNR were investigated, including scale effects in validating SSNR data, impacts of the MERSI calibration update on the estimation of SSNR and the dependency of the retrieval accuracy of SSNR data on view geometry. We also incorporated data from twin MODIS sensors to assess how time and the number of satellite overpasses affect the retrieval of SSNR data. Validation against one-year data over seven Surface Radiation Budget Network (SURFRAD) stations showed that the presented algorithm estimated daily SSNR at the original resolution of the MERSI with a root mean square error (RMSE) of 41.9 W/m2 and a bias of −1.6 W/m2. Aggregated to a spatial resolution of 161 km, the RMSE of MERSI retrievals can be reduced by approximately 10 W/m2. Combined with MODIS data, the RMSE of daily SSNR estimation can be further reduced to 22.2 W/m2. Compared with that of daily SSNR, estimation of monthly SSNR is less affected by the number of satellite overpasses per day. The RMSE of monthly SSNR from a single MERSI sensor is as small as 13.5 W/m2.
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    Land Surface Albedo Estimation from Chinese HJ Satellite Data Based on the Direct Estimation Approach
    (MDPI, 2015-05-04) He, Tao; Liang, Shunlin; Wang, Dongdong; Chen, Xiaona; Song, Dan-Xia; Jiang, Bo
    Monitoring surface albedo at medium-to-fine resolution (<100 m) has become increasingly important for medium-to-fine scale applications and coarse-resolution data evaluation. This paper presents a method for estimating surface albedo directly using top-of-atmosphere reflectance. This is the first attempt to derive surface albedo for both snow-free and snow-covered conditions from medium-resolution data with a single approach. We applied this method to the multispectral data from the wide-swath Chinese HuanJing (HJ) satellites at a spatial resolution of 30 m to demonstrate the feasibility of this data for surface albedo monitoring over rapidly changing surfaces. Validation against ground measurements shows that the method is capable of accurately estimating surface albedo over both snow-free and snow-covered surfaces with an overall root mean square error (RMSE) of 0.030 and r-square (R2) of 0.947. The comparison between HJ albedo estimates and the Moderate Resolution Imaging Spectral Radiometer (MODIS) albedo product suggests that the HJ data and proposed algorithm can generate robust albedo estimates over various land cover types with an RMSE of 0.011–0.014. The accuracy of HJ albedo estimation improves with the increase in view zenith angles, which further demonstrates the unique advantage of wide-swath satellite data in albedo estimation.
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    Validation of Land Cover Maps in China Using a Sampling-Based Labeling Approach
    (MDPI, 2015-08-18) Bai, Yan; Feng, Min; Jiang, Hao; Wang, Juanle; Liu, Yingzhen
    This paper presents a rigorous validation of five widely used global land cover products, i.e., GLCC (Global Land Cover Characterization), UMd (University of Maryland land cover product), GLC2000 (Global Land Cover 2000 project data), MODIS LC (Moderate Resolution Imaging Spectro-radiometer Land Cover product) and GlobCover (GLOBCOVER land cover product), and a national land cover map GLCD-2005 (Geodata Land Cover Dataset for year 2005) against an independent reference data set over China. The land cover reference data sets in three epochs (1990, 2000, and 2005) were collected on a web-based prototype system using a sampling-based labeling approach. Results show that, in China, the highest overall accuracy is observed in GLCD-2005 (72.3%), followed by MODIS LC (68.9%), GLC2000 (65.2%), GlobCover (57.7%) and GLCC (57.2%), while UMd has the lowest accuracy (48.6%); all of the products performed best in representing “Trees” and “Others”, well with “Grassland” and “Cropland”, but problematic with “Water” and “Urban” across China in general. Moreover, in respect of GLCD-2005, there are significant accuracy differences across seven geographical locations of China, ranging from 46.3% in the Southwest, 77.5% in the South, 79.2% in the Northwest, 80.8% in the North, 81.8% in the Northeast, 82.6% in the Central, to 89.0% in the East. This study indicates that a regionally focused land cover map would in fact be more accurate than extracting the same region from a globally produced map.
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    Potential Vegetation and Carbon Redistribution in Northern North America from Climate Change
    (MDPI, 2016-01-06) Flanagan, Steven A.; Hurtt, George C.; Fisk, Justin P.; Sahajpal, Ritvik; Hansen, Matthew C.; Dolan, Katelyn A.; Sullivan, Joe H.; Zhao, Maosheng
    There are strong relationships between climate and ecosystems. With the prospect of anthropogenic forcing accelerating climate change, there is a need to understand how terrestrial vegetation responds to this change as it influences the carbon balance. Previous studies have primarily addressed this question using empirically based models relating the observed pattern of vegetation and climate, together with scenarios of potential future climate change, to predict how vegetation may redistribute. Unlike previous studies, here we use an advanced mechanistic, individually based, ecosystem model to predict the terrestrial vegetation response from future climate change. The use of such a model opens up opportunities to test with remote sensing data, and the possibility of simulating the transient response to climate change over large domains. The model was first run with a current climatology at half-degree resolution and compared to remote sensing data on dominant plant functional types for northern North America for validation. Future climate data were then used as inputs to predict the equilibrium response of vegetation in terms of dominant plant functional type and carbon redistribution. At the domain scale, total forest cover changed by ~2% and total carbon storage increased by ~8% in response to climate change. These domain level changes were the result of much larger gross changes within the domain. Evergreen forest cover decreased 48% and deciduous forest cover increased 77%. The dominant plant functional type changed on 58% of the sites, while total carbon in deciduous vegetation increased 107% and evergreen vegetation decreased 31%. The percent of terrestrial carbon from deciduous and evergreen plant functional types changed from 27%/73% under current climate conditions, to 54%/46% under future climate conditions. These large predicted changes in vegetation and carbon in response to future climate change are comparable to previous empirically based estimates, and motivate the need for future development with this mechanistic model to estimate the transient response to future climate changes.
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    Assessment of the Suomi NPP VIIRS Land Surface Albedo Data Using Station Measurements and High-Resolution Albedo Maps
    (MDPI, 2016-02-08) Zhou, Yuan; Wang, Dongdong; Liang, Shunlin; Yu, Yunyue; He, Tao
    Land surface albedo (LSA), one of the Visible Infrared Imaging Radiometer Suite (VIIRS) environmental data records (EDRs), is a fundamental component for linking the land surface and the climate system by regulating shortwave energy exchange between the land and the atmosphere. Currently, the improved bright pixel sub-algorithm (BPSA) is a unique algorithm employed by VIIRS to routinely generate LSA EDR from VIIRS top-of-atmosphere (TOA) observations. As a product validation procedure, LSA EDR reached validated (V1 stage) maturity in December 2014. This study summarizes recent progress in algorithm refinement, and presents comprehensive validation and evaluation results of VIIRS LSA by using extensive field measurements, Moderate Resolution Imaging Spectroradiometer (MODIS) albedo product, and Landsat-retrieved albedo maps. Results indicate that: (1) by testing the updated desert-specific look-up-table (LUT) that uses a stricter standard to select the training data specific for desert aerosol type in our local environment, it is found that the VIIRS LSA retrieval accuracy is improved over a desert surface and the absolute root mean square error (RMSE) is reduced from 0.036 to 0.023, suggesting the potential of the updated desert LUT to the improve the VIIRS LSA product accuracy; (2) LSA retrieval on snow-covered surfaces is more accurate if the newly developed snow-specific LUT (RMSE = 0.082) replaces the generic LUT (RMSE = 0.093) that is employed in the current operational LSA EDR production; (3) VIIRS LSA is also comparable to high-resolution Landsat albedo retrieval (RMSE < 0.04), although Landsat albedo has a slightly higher accuracy, probably owing to higher spatial resolution with less impacts of mixed pixel; (4) VIIRS LSA retrievals agree well with the MODIS albedo product over various land surface types, with overall RMSE of lower than 0.05 and the overall bias as low as 0.025, demonstrating the comparable data quality between VIIRS and the MODIS LSA product.
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    A 30+ Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description and Validation
    (MDPI, 2016-03-22) Claverie, Martin; Matthews, Jessica L.; Vermote, Eric F.; Justice, Christopher O.
    In- land surface models, which are used to evaluate the role of vegetation in the context of global climate change and variability, LAI and FAPAR play a key role, specifically with respect to the carbon and water cycles. The AVHRR-based LAI/FAPAR dataset offers daily temporal resolution, an improvement over previous products. This climate data record is based on a carefully calibrated and corrected land surface reflectance dataset to provide a high-quality, consistent time-series suitable for climate studies. It spans from mid-1981 to the present. Further, this operational dataset is available in near real-time allowing use for monitoring purposes. The algorithm relies on artificial neural networks calibrated using the MODIS LAI/FAPAR dataset. Evaluation based on cross-comparison with MODIS products and in situ data show the dataset is consistent and reliable with overall uncertainties of 1.03 and 0.15 for LAI and FAPAR, respectively. However, a clear saturation effect is observed in the broadleaf forest biomes with high LAI (>4.5) and FAPAR (>0.8) values.
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    Landsat ETM+ and SRTM Data Provide Near Real-Time Monitoring of Chimpanzee (Pan troglodytes) Habitats in Africa
    (MDPI, 2016-05-20) Jantz, Samuel M.; Pintea, Lilian; Nackoney, Janet; Hansen, Matthew C.
    All four chimpanzee sub-species populations are declining due to multiple factors including human-caused habitat loss. Effective conservation efforts are therefore needed to ensure their long-term survival. Habitat suitability models serve as useful tools for conservation planning by depicting relative environmental suitability in geographic space over time. Previous studies mapping chimpanzee habitat suitability have been limited to small regions or coarse spatial and temporal resolutions. Here, we used Random Forests regression to downscale a coarse resolution habitat suitability calibration dataset to estimate habitat suitability over the entire chimpanzee range at 30-m resolution. Our model predicted habitat suitability well with an r2 of 0.82 (±0.002) based on 50-fold cross validation where 75% of the data was used for model calibration and 25% for model testing; however, there was considerable variation in the predictive capability among the four sub-species modeled individually. We tested the influence of several variables derived from Landsat Enhanced Thematic Mapper Plus (ETM+) that included metrics of forest canopy and structure for four three-year time periods between 2000 and 2012. Elevation, Landsat ETM+ band 5 and Landsat derived canopy cover were the strongest predictors; highly suitable areas were associated with dense tree canopy cover for all but the Nigeria-Cameroon and Central Chimpanzee sub-species. Because the models were sensitive to such temporally based predictors, our results are the first to highlight the value of integrating continuously updated variables derived from satellite remote sensing into temporally dynamic habitat suitability models to support near real-time monitoring of habitat status and decision support systems.