Tang, XiaojingAndrianirina, CaroleBullock, Eric L.Healey, Sean P....
18页
查看更多>>摘要：The development of near-real time forest monitoring systems, which are used to create alerts for events such as logging or fire, often involves tradeoffs between accuracy and timeliness. In the context of forest monitoring, timeliness is measured by the lag between when a change occurs in the forest and the creation of an alert about the event. Conventional accuracy assessments quantify both false negative and false positive errors in change maps, but do not specify those rates as a function of lag. Recent near real-time (NRT) accuracy assessment methods summarize the relationship between lag and correctly identified change events, but do not integrate consideration of changes mapped where they have not occurred. Here, we propose an assessment framework that we call "Sigmoid Accuracy", which characterizes forest change detection accuracy as it relates to timeliness. Using a change monitoring algorithm that applies the Continuous Change Detection and Classification algorithm (CCDC) to Sentinel-1 radar data in Madagascar, we demonstrate how the assessment framework can be used to calculate a lag-dependent F1-Score to create a sigmoidal curve describing system performance as it relates to the time since a change event. We introduce two new performance metrics that define key moments along this curve: "Initial Delay", or the minimum time required to create an alert as observed in reference data, and the "Level Off Point", or the lag at which accuracy stabilizes. Our framework accommodates varying assessment designs, as demonstrated using pixel-, block-, and event-based agreement for the response design. These metrics provide a holistic way to evaluate and compare the usefulness of forest monitoring systems in real-world applications.
Yang, XiuchengZhu, ZheQiu, ShiKroeger, Kevin D....
19页
查看更多>>摘要：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 changes, is important for coastal communities and natural resource management. Large-scale mapping of coastal tidal wetland changes is extremely difficult due to their inherent dynamic nature. To bridge this gap, we developed an automated algorithm for DEtection and Characterization of cOastal tiDal wEtlands change (DECODE) using dense Landsat time series. DECODE consists of three elements, including spectral break detection, land cover classification and change characterization. DECODE assembles all available Landsat observations and introduces a water level regressor for each pixel to flag the spectral breaks and estimate harmonic time-series models for the divided temporal segments. Each temporal segment is classified (e.g., vegetated wetlands, open water, and others - including unvegetated areas and uplands) based on the phenological characteristics and the synthetic surface reflectance values calculated from the harmonic model coefficients, as well as a generic rule-based classification system. This harmonic model-based approach has the advantage of not needing the acquisition of satellite images at optimal conditions (i.e., low tide status) to avoid underestimating coastal vegetation caused by the tidal fluctuation. At the same time, DECODE can also characterize different kinds of changes including land cover change and condition change (i.e., land cover modification without conversion). We used DECODE to track status of coastal tidal wetlands in the northeastern United States from 1986 to 2020. The overall accuracy of land cover classification and change detection is approximately 95.8% and 99.8%, respectively. The vegetated wetlands and open water were mapped with user's accuracy of 94.6% and 99.0%, and producer's accuracy of 98.1% and 93.5%, respectively. The cover change and condition change were mapped with user's accuracy of 68.0% and 80.0%, and producer's accuracy of 80.5% and 97.1%, respectively. Approximately 3283 km(2) of the coastal landscape within our study area in the northeastern United States changed at least once (12% of the study area), and condition changes were the dominant change type (84.3%). Vegetated coastal tidal wetland decreased consistently (~2.6 km(2)& nbsp;per year) in the past 35 years, largely due to conversion to open water in the context of sea-level rise.
查看更多>>摘要：Accurate crop phenology information is essential for precision farming and agricultural productivity improve-ment. In recent years, in-situ equipment on crop phenology observation has been boosted, which generates high-quality real-time pictures on capturing vegetation phenological changes. However, due to the limited number of ground sites, it is impossible to measure large-scale crop phenology with local observations. Complementary, the freely available Sentinel satellites with high revisit frequency provide an opportunity to map accurate crop phenology at an unprecedented fine spatial scale. Because of the differences in viewing angle and range, the consistency of crop phenological stages varies between satellite and ground observations. To fill the gap between satellite and ground observations, we developed a spatial-aware scheme to integrate SAR and optical time-series data for accurate crop phenology tracking. To be specific, we propose a new deep learning model called Deep-CroP framework to improve the alignment between satellite and ground observations on crop phenology. The experiment results on selected ground sites demonstrate that the proposed Deep-CroP is able to accurately identify crops phenology and narrow the discrepancies from 30+ days to as high as several days. In addition, we applied the Deep-CroP to large-scale Sentinel time-series to map spatial patterns of phenology at fine resolution imagery on two study areas (i.e., TA1 and TA2). In general, the potential of satellites time-series for ground-level crop phenology observation is verified. Also, the consistency between satellite and PhenoCam observations is expected to be further improved.
查看更多>>摘要：The 17 year record of daily sea surface wind observed from the QuikSCAT and ASCAT satellite scatterometers from 2000 to 2016 is used to study quick shifting wind jet events over the South China Sea (SCS) in summer. The wind jet typically undergoes a counter-clockwise rotation from eastward to northward in about 8 days and then back to eastward in roughly 4 days. Generally, the entire cycle takes between 4 and 21 days. Such events happen 2-6 times every summer and, in total, have occurred 72 times over the last 18 years. Model simulations demonstrate that the wind stress curl associated with quick wind shifts deform the double gyre in the SCS, with a conspicuous weakening of the northern gyre and a northwestward movement of the center of the southern gyre. In addition, a quick shift of the wind jet leads to both negative sea surface temperature (SST) anomalies in the SCS that lag about one day because of the surface latent heat flux, and positive SST anomalies around the eastward current because of decreased cold advection.
Lei, FangniSenyurek, VolkanKurum, MehmetGurbuz, Ali Cafer...
18页
查看更多>>摘要：Global soil moisture mapping at high spatial and temporal resolution is important for various meteorological, hydrological, and agricultural applications. Recent research shows that the land surface reflection in the forward direction of Global Navigation Satellite System (GNSS) signals at L-band can convey high-resolution land surface information, including surface soil moisture. However, these signals are often affected by complex land surface characteristics and the bistatic nature of the GNSS-Reflectometry (GNSS-R) technique, resulting in a nonlinear relationship between the signals and surface soil moisture. In this work, a machine learning (ML) approach is used to map quasi-global soil moisture using bistatic reflectance observations acquired from the recently launched Cyclone GNSS (CYGNSS) mission. Specifically, several land surface parameters are obtained from remote sensing products and integrated with Soil Moisture Active Passive (SMAP) enhanced soil moisture retrievals to facilitate daily quasi-global CYGNSS soil moisture mapping at 9 km. Based on cross-validation against SMAP data, the ML algorithm is shown to be suitable for retrieving soil moisture from CYGNSS. Median values of unbiased root-mean-square-difference for the quasi-global coverage or regions with vegetation water content less than 5 kg/m(2) are 0.0395 cm3/cm(3 )and 0.0320 cm(3)/cm(3), respectively. Likewise, via independent evaluation against more than 100 in-situ sites, the algorithm is shown to have an unbiased root-mean-square-error of 0.0543 cm(3)/cm(3). CYGNSS-based retrievals contain similar spatial variability as SMAP across different seasons. Moreover, through a robust triple collocation technique, the accuracy of CYGNSS soil moisture is relatively high over moderately vegetated regions with correlations ranging from 0.4 to 0.8. Based on these validation results, we argue that derived CYGNSS soil moisture estimates can supplement current global soil moisture databases and provide more frequent retrievals at 9 km.
Kalmus, PeterAo, Chi O.Wang, Kuo-NungManzi, Maria Paola...
9页
查看更多>>摘要：We present a new seasonal planetary boundary layer height (PBLH) climatology product derived from 14 years of Global Navigation Satellite System radio occultation (GNSS-RO) data from multiple missions including COSMIC, TerraSAR-X, KOMPSAT-5, and PAZ. PBLH estimates are derived from the minimum gradients of retrieved refractivity profiles, with a vertical resolution of ~200 m. The climatology is obtained from occultations observed between June 2006 and December 2019, and is divided into land and ocean regimes on a 2-degree grid. We provide seasonal climatologies at 2-degree resolution as well as climatologies of diurnal cycle amplitude and phase at 5-degree resolution. The new GNSS PBLH climatology is compared to radiosonde data from the shipbased Marine ARM GPCI Investigation of Clouds (MAGIC) campaign in the subtropical northeast Pacific ocean and to previous GNSS PBLH climatology estimates. The higher spatial resolution reveals new details such as seasonal PBLH modulation due to sea ice off the coast of Antarctica. The PBLH product is the first publicly available at 2-degree resolution.
Du, PeijunGuo, ShanchuanZhang, WeiTang, Pengfei...
24页
查看更多>>摘要：As an efficient mode of modern agriculture, plastic greenhouse (PG) has significantly increased crop yields, but it is also criticized for changing the agriculture landscape and posing a threat to the environment. Accurate and timely information on PG distribution is essential for the strategic planning of modern agriculture as well as the projection of the environmental impacts. However, PG mapping over a large area has been a long-term challenge. Compared with classifier-based methods, index-based methods have the advantages of fast speed and convenience, which are very suitable for rapid large-scale mapping. The existing PG indices face the diversity of PG types and background environments, and the seasonal variation of PG spectra. To solve these problems, this study proposes a novel spectral index using Sentinel-2 images, namely the Advanced Plastic Greenhouse Index (APGI), to map PGs at a large scale. Four typical PG planting regions in the world, including Almeria (Spain), Anamur (Turkey), Weifang (China), and Nantong (China), were selected as study areas. Based on the spectral analysis, some common spectral characteristics of PGs (i.e., high reflectance in NIR wavelengths and strong absorption in red and SWIR2 wavelengths) were observed and used in the APGI for highlighting PG areas. Besides, the coastal aerosol band and the red band were selected as optimal indicators to distinguish PG from other land covers which share similar spectral characteristics with PG. The experimental results indicate that the APGI has obvious advantages in enhancing PG information and suppressing non-PG backgrounds in various scenes compared with the existing indices. The APGI achieved the PG mapping accuracy with an OA of 90.63% 97.50% and an F1 score of 80.56% - 96.20% in all study cases. Furthermore, the APGI also showed its robustness in seasonal variations and different datasets.
查看更多>>摘要：For better use of well-performed water quality parameter estimation models and the comprehensive use of multisource remote sensing data, hyperspectral reconstruction is urgently needed in the remote sensing of optically complex inland waters. In this study, we proposed a bio-optical-based hyperspectral reconstruction (BBHR) algorithm to generate hyperspectral above-surface remote-sensing reflectance (Rrs) data ranging in wavelength from 400 to 800 nm. One core advantage of the BBHR method is its in situ data independency, which theoretically renders the algorithm universal. The other advantage is its ability to reconstruct hyperspectral Rrs for the 400-800 nm spectral range, which facilitates the construction of more high accuracy chlorophyll-a concentration (Cchla) estimation models for optically complex waters. The reconstruction was tested by employing six widely used multispectral sensors: the Medium Resolution Imaging Spectrometer, (MERIS), Sentinel-3 Ocean and Land Color Instrument (S3 OLCI), Sentinel-2 Multispectral Instrument (S2 MSI), Geostationary Ocean Color Imager (GOCI), Visible Infrared Imaging Radiometer Suite (VIIRS), and Moderate Resolution Imaging Spectroradiometer (MODIS). The model performance was validated by using a ASD FieldSpec spectroradiometer-measured hyperspectral dataset containing 1396 samples and a satellite-in-situ match-up dataset with 66 samples. The results show that the proposed BBHR method exhibits satisfactory performance. The average mean absolute percentage error (MAPE), root mean square error (RMSE), R2 and bias indices of the BBHR-reconstructed Rrs over all spectral bands of the six multispectral sensors were 3.27%, 8.86 x 10-4 sr-1, 0.98, and - 6.53 x 10-5 sr-1, respectively. In the field Cchla estimation experiment that contained 391 samples (mean Cchla is 25.42 +/- 16.37 mu g/L), the BBHR algorithm improved the MAPE and RMSE indices of multispectral data from 0.47 and 12.80 mu g/L to 0.42 and 10.16 mu g/L, respectively. For the satellite image match-up dataset (66 samples), the BBHR method decreased the MAPE and RMSE indices of multispectral images from 0.51 and 12.94 mu g/L to 0.32 and 8.01 mu g/L, respectively. The proposed algorithm outperformed the other two high-accuracy models in terms of spectral fidelity and Cchla estimation. In addition, the BBHR method shows great potential for the multi-source monitoring of inland water bodies. This could improve the accuracy and robustness of the reconstruction when semi-synchronized multi-source data are input and increase the consistency of multi-source data when non-synchronized multisource data are provided. Our results revealed that BBHR is a trustworthy algorithm that offers hyperspectral Rrs data and facilitates the remote monitoring of turbid inland waterbodies.
查看更多>>摘要：Backscatter measured by scatterometers and Synthetic Aperture Radars is sensitive to the dielectric properties of the soil and normally increases with increasing soil moisture content. However, when the soil is dry, the radar waves penetrate deeper into the soil, potentially sensing subsurface scatterers such as near-surface rocks and stones. In this paper we propose an exponential model to describe the impact of such subsurface scatterers on CBand backscatter measurements acquired by the Advanced Scatterometer (ASCAT) on board of the METOP satellites. The model predicts an increase of the subsurface scattering contributions with decreasing soil wetness that may counteract the signal from the soil surface. This may cause anomalous backscatter signals that deteriorate soil moisture retrievals from ASCAT. We test whether this new model is able to explain ASCAT observations better than a bare soil backscatter model without a subsurface scattering term, using k-fold cross validation and the Bayesian Information Criterion for model selection. We find that arid landscapes with Leptosols and Arenosols represent ideal environmental conditions for the occurrence of subsurface scattering. Nonetheless, subsurface scattering may also become important in more humid environments during dry spells. We conclude that subsurface scattering is a widespread phenomenon that (i) needs to be accounted for in active microwave soil moisture retrievals and (ii) has a potential for soil mapping, particularly in arid and semi-arid environments.
查看更多>>摘要：Remote sensing-based approaches are important for evaluating ecosystem degradation and the efficient planning of ecosystem restoration efforts. However, the large majority of remote sensing-based degradation assessments are trend-based, implying that they can only detect degradation that occurred after medium or high-resolution satellite imagery became available. This makes them less suitable to map long-term degradation in ecosystems that have been under high human pressure since before. The main goal of this study was to develop a robust operational approach to map forest degradation status in heterogeneous landscapes with a long-standing degradation history to inform the planning of restoration interventions. We hereby use the tropical dry forests of Lambayeque, Peru, as a case study. Instead of using a trend-based assessment, we evaluated forest degradation status by comparing current woody cover (WC) and aboveground biomass (AGB) estimates obtained from remote sensing imagery with benchmark values consisting of the 95th percentile WC and AGB values inside environmentally homogenous land capability classes. Using boosted regression tree models and a combination of optical (Sentinel-2) and synthetic aperture radar (Sentinel-1) data of different seasons, we mapped WC and AGB, using training data obtained through very high-resolution imagery and field measurements. Further, we aimed at assessing (i) whether the inclusion of Sentinel-1 data improves mapping accuracy in comparison to using only Sentinel-2 data, and (ii) whether the use of multi-seasonal data improves accuracy in comparison to single-season data. Models combining multi-seasonal Sentinel-1 and Sentinel-2 data resulted in the most accurate WC predictions (mean absolute error (MAE): 16%; MAE normalized by dividing by the inter-quartile range of training data: 26%) and AGB predictions (MAE: 28.6 t/ha; normalized MAE: 65%), but differences in predictive accuracy with single season models or models using only Sentinel-2 data were small. The most accurate models estimated an average WC of 41% and an average AGB of 23.4 t/ha. Average WC and AGB reduction due to degradation was 35% and 36%, respectively, indicating that these forests are highly degraded. The site-specific scaling of WC and AGB allows to efficiently estimate forest degradation status irrespective of the time when this degradation occurred, and to express degradation status against site-specific benchmarks. On the condition that there are still some areas that are sufficiently undegraded to be used as a benchmark, the approach can be used to prioritize forest restoration actions and inform targets for restoration in heterogeneous landscapes suffering the impacts of undocumented long-term degradation.
NSTL
Elsevier