Lapadat, CathleenSchweiger, Anna K.Juzwik, JenniferMontgomery, Rebecca...
14页
查看更多>>摘要：The oak wilt disease caused by the invasive fungal pathogen Bretziella fagacearum is one of the greatest threats to oak-dominated forests across the Eastern United States. Accurate detection and monitoring over large areas are necessary for management activities to effectively mitigate and prevent the spread of oak wilt. Canopy spectral reflectance contains both phylogenetic and physiological information across the visible near-infrared (VNIR) and short-wave infrared (SWIR) ranges that can be used to identify diseased red oaks. We develop partial least square discriminant analysis (PLS-DA) models using airborne hyperspectral reflectance to detect diseased canopies and assess the importance of VNIR, SWIR, phylogeny, and physiology for oak wilt detection. We achieve high accuracy through a three-step phylogenetic process in which we first distinguish oaks from other species (90% accuracy), then red oaks from white oaks (Quercus macrocarpa) (93% accuracy), and, lastly, infected from noninfected trees (80% accuracy). Including SWIR wavelengths increased model accuracy by ca. 20% relative to models based on VIS-NIR wavelengths alone; using a phylogenetic approach also increased model accuracy by ca. 20% over a single-step classification. SWIR wavelengths include spectral information important in differentiating red oaks from other species and in distinguishing diseased red oaks from healthy red oaks. We determined the most important wavelengths to identify oak species, red oaks, and diseased red oaks. We also demonstrated that several multispectral indices associated with physiological decline can detect differences between healthy and diseased trees. The wavelengths in these indices also tended to be among the most important wavelengths for disease detection within PLS-DA models, indicating a convergence of the methods. Indices were most significant for detecting oak wilt during late August, especially those associated with canopy photosynthetic activity and water status. Our study suggests that coupling phylogenetics, physiology, and canopy spectral reflectance provides an interdisciplinary and comprehensive approach that enables detection of forest diseases at large scales. These results have potential for direct application by forest managers for detection to initiate actions to mitigate the disease and prevent pathogen spread.
Jang, EunnaKim, Young JunIm, JunghoPark, Young-Gyu...
15页
查看更多>>摘要：Sea surface salinity (SSS) provides information on the variability of ocean dynamics (global water cycle and ocean circulation) and air-sea interactions, thereby contributing to the identification and prediction of significant changes in the global climate. Monitoring global SSS via satellite observations has been possible using L-band microwave radiometers since 2010; however, their performance is limited by their retrieval algorithms under conditions such as radio frequency interference, low sea surface temperatures, and strong winds. This study proposes a new global SSS model using multi-source data based on seven machine learning approaches: K-nearest neighbor, support vector regression, artificial neural network, random forest, extreme gradient boosting, light gradient boosting model, and gradient boosted regression trees (GBRT). Five Soil Moisture Active Passive (SMAP) products, Hybrid Coordinate Ocean Model (HYCOM) SSS, and four ancillary data were used as input variables. All models produced better performance than either SMAP or HYCOM SSS products, with the top performing GBRT model reducing the root mean square difference for the validation dataset from 1.062 to 0.259 practical salinity units compared to the SMAP SSS product. The improved SSS products had increased correlation with the in-situ data for both low-and high-salinity waters across all global oceans, thus further advancing the understanding and monitoring of global SSS.
查看更多>>摘要：Timely monitoring of above-ground biomass (AGB) is essential for indicating the crop growth status and pre-dicting grain yield and carbon dynamics. Non-destructive remote sensing techniques with a large spatial coverage have become a promising method for crop biomass monitoring. However, most existing crop biomass models have only been tested at a single growth stage or only at a small number of growth stages at a single location. This has limited the ability of these AGB models to be transferred spatially, to other fields or regions, to predict AGB at any growth stage during the season, or to be potentially used with data from other sensing systems. Here, a new crop biomass algorithm (CBA-Wheat) was developed to estimate AGB over the entire growing season. It uses information on the crop growth stage, based on phenological scale observations (Zadoks scale or ZS), the day of the year or thermal indices (growing degree days), to correct AGB estimations from remotely sensed vegetation indices. The model transferability was evaluated across multiple regional test sites and different data sources (UAV and hand-held spectroscopic data). Results showed that the coefficient values [slope (k) and intercept (b)] of ordinary least squares regression (OLSR) of AGB with vegetation indices had a strong relationship with ZS. These k and b relationships were used to correct the OLSR model parameters based on the observed phenological stage (ZS value). The two-band enhanced vegetation index (EVI2) was the best vegetation index for predicting AGB with the new CBA-WheatZS model, with R-2 and RMSE values of 0.83 and 2.07 t/ha for an experimental trial site, 0.78 and 2.05 t/ha for multiple independent regional test sites, and 0.69 and 1.87 t/ha when transferred to EVI2 derived from UAV. Model performance was lower with the day of the year and thermal index corrections; however, the use of relative growing degree-days (RGS; CBA-WheatRGS), instead of ZS information, to adjust the model parameters showed a high consistency with the CBA-WheatZS model, and a good potential for estimation of AGB at regional scales without the need for local phenological observations. The CBA-WheatRGS had validated R-2 and RMSE values of 0.82 and 2.01 t/ha for the experimental trial site, 0.76 and 2.39 t/ha for multiple independent regional test sites, and 0.66 and 2.14 t/ha for UAV hyperspectral imagery. These results demonstrated a good potential to estimate biomass from remotely sensed imagery at varying spatio-temporal scales in winter wheat.
查看更多>>摘要：It has been over ten years since the successful launch of the first-ever dedicated satellite for global soil moisture monitoring; Soil Moisture and Ocean Salinity (SMOS). Looking towards the future, P-band (0.3-1 GHz) is a promising technique to replace or enhance the L-band (1.4 GHz) SMOS and SMAP (Soil Moisture Active Passive) missions because of an expected reduction in roughness and vegetation impact, leading to an improved soil moisture accuracy over rougher soil surfaces and more densely vegetated areas. Accordingly, this investigation evaluated the tau-omega model at P-band (0.75 GHz) using a tower-based experiment in Victoria, Australia, where brightness temperature observations were collected concurrently at P-and L-band over bare and wheat covered flat and periodic soil surfaces. The potential to retrieve soil moisture without discriminating periodic and flat surfaces was investigated by applying the roughness and vegetation parameters calibrated for flat soil to retrieve the moisture of periodic soil. Results showed that P-band had a comparable RMSE across different roughness configurations (variations less than 0.016 m(3)/m(3)) for both bare and wheat-covered soil, while the L band RMSE was only comparable for wheat-covered soil, indicating that periodic surfaces did not need to be discriminated in such scenarios. Conversely, a difference of 0.022 m(3)/m(3) was observed for L-band with bare soil. A reduced vegetation impact was also demonstrated at P-band, with an RMSE of 0.029 m(3)/m(3) achieved when completely ignoring the wheat existence with under 4-kg/m(2) vegetation water content, whereas at L-band the RMSE increased to 0.063 m(3)/m(3). This study therefore paves the way for a successful P-band radiometer mission for obtaining more accurate global soil moisture information.
查看更多>>摘要：Vegetation responds at varying temporal scales to changing soil water availability. These process dynamics complicate assessments of plant-water relations but also offer various access points to advance understanding of vegetation responses to environmental change. Remote sensing (RS) provides large capacity to quantify sensitive and robust information of vegetation responses and underlying abiotic change driver across observational scales. Retrieved RS based vegetation parameters are often sensitive to various environmental and plant specific factors in addition to the targeted plant response. Further, individual plant responses to water limitation act at different temporal and spatial scales, while RS sampling schemes are often not optimized to assess these dynamics. The combination of these aspects complicates the interpretation of RS parameter when assessing plant-water relations. We consequently aim to advance insight on the sensitivity of physiological, biochemical and structural RS parameter for plant adaptation in response to emerging soil water limitation. We made a field experiment in maize in Tuscany (Central Italy), while irrigation was stopped in some areas of the drip-irrigated field. Within a period of two weeks, we measured the hydraulic and physiological state of maize plants in situ and complemented these detailed measurements with extensive airborne observations (e.g. sun-induced chlorophyll fluorescence (SIF), vegetation indices sensitive for photosynthesis, pigment and water content, land surface temperature). We observe a double response of far-red SIF with a short-term increase after manifestation of soil water limitation and a decrease afterwards. We identify different response times of RS parameter representing different plant adaptation mechanisms ranging from short term responses (e.g. stomatal conductance, photosynthesis) to medium term changes (e.g. pigment decomposition, changing leaf water content). Our study demonstrates complementarity of common and new RS parameter to mechanistically assess the complex cascade of functional, biochemical and structural plant responses to evolving soil water limitation.
查看更多>>摘要：Surface Urban heat island (SUHI) is a major adverse environmental consequence of urbanization. Many algo-rithms measuring SUHI across varying spatial or temporal scales are developed rapidly with the availability of thermal infrared (TIR) remote sensing data from satellites. However, the trade-off between the spatial and temporal resolution of TIR sensors limits the study of SUHI on both spatial and temporal domains. Therefore, this study aims to improve the characterization of SUHI using spatiotemporally enhanced land surface temperature (LST) derived from the synergistic use of coarse-and fine-spatial-resolution TIR data. Combining the spatial downscaling and temporal interpolation techniques, we generated daily 100 m-resolution LST in both daytime and nighttime to analyze the SUHI in different local climate zones (LCZs) in Beijing. LCZ is a manifestation of the urban form on the thermal environment, covering hundreds of meters to several kilometers in horizontal scale. The results indicate the spatiotemporally enhanced LST is reliable in capturing the LCZ-based SUHI magnitude compared with original observations, and providing a more accurate time range when the SUHI reaches to its maximum compared with those time-discontinuous original observations. Compared with temporally interpo-lated coarse-resolution LST, the spatiotemporally enhanced LST shows a larger annual variation of SUHI (especially in LCZ 2 with a mean absolute SUHI difference of 0.8 K and 1.3 K for daytime and nighttime, respectively) and provides larger SUHI difference between compact building and open building (especially when there is a significant SUHI effect). The superiority of the spatiotemporally enhanced LSTs in analyzing LCZ-based SUHI is more evident in daily and monthly SUHI analysis than in single-day analysis or annual analysis, espe-cially in compact building types (LCZ 1 and 2). These findings are valuable information for better and healthier urban planning for SUHI mitigation and public health care.
Xie, QiaoyunHuete, AlfredoHall, Christopher C.Medlyn, Belinda E....
19页
查看更多>>摘要：Species composition is a key determinant of grassland ecosystem function and resilience. Climate change is predicted to alter the distribution of cool season (C-3) and warm season (C-4) grasses, however, the lack of spatial distributions and temporal variations of grass functional type information severely limits our understanding of climate impacts on grasslands. This study classified C-3 and C-4 grasses per pixel according to the peak of growing season generated from Enhanced Vegetation Index time series. From 2003 to 2017, the C-3-C-4 composition of Australian rain-fed grasslands and pastures was mapped at 500 m resolution on an annual basis across a wide geographical range (10 degrees S - 45 degrees S), and revealed extreme inter-annual fluctuations. Over the 15-year period, the satellite-derived ratio of C-4 to C-3 grasses significantly increased (p < 0.05), indicating a long-term shift in community composition that was confirmed with 182,911 Atlas of Living Australia ground observations. The most pronounced changes occurred in mid-latitude transitional areas where C-3 and C-4 grasses co-dominate. Our climate analysis indicated the inter-annual fluctuations of C-4/C-3 grass ratios were significantly associated (p < 0.05) with warm/cool season rainfall ratios, and not with temperature or annual rainfall. This suggests that an increase in the warm/cool season rainfall ratio favors C-4 grasses and a decrease in the warm/cool season rainfall ratio favors C-3 grasses. Our findings reveal spatially-detailed dynamics of grasslands and demonstrate large-scale grassland compositional changes over 15 years. The grass composition maps should help improve ecological forecasting of grass distributions and enable researches on grassland ecosystem responses to climate change that are relevant to both adaptation of rangeland agricultural and fire management practices. Our study should also help predict grass distribution under future climate conditions, and assist in the accurate modelling of global water, carbon, and energy exchanges between the land surface and atmosphere.
NSTL
Elsevier