Freeborn, Patrick H.Jolly, W. MattCochrane, Mark A.Roberts, Gareth...
13页
查看更多>>摘要：Despite the ecological and socioeconomic impacts of wildfires, little attention has been paid to the spatiotemporal patterns of nighttime fire activity across the conterminous United States (CONUS). Daytime fire radiative power (FRP) detected by the Moderate Resolution Imaging Spectroradiometer (MODIS) was nearly evenly split (54% vs. 46%) between inside and outside wildfires from 2003 to 2020. In contrast, 94% of nighttime FRP was detected within wildfires, of which 95% was detected within large wildfires (> 2023 ha). Nighttime proportions (i.e., the proportion of total summed FRP detected by MODIS at night) were lowest (3%) outside wildfires when coincident 1000-hr fuel moistures were highest and vegetation fires were smaller and less intense. As 1000-hr fuel moistures decreased, MODIS active fire pixels shifted out of agricultural and prescribed fires and into wildfires with higher nighttime per-pixel values of FRP such that nighttime proportions peaked at 29% for the largest wildfires. Increases in nighttime proportions within larger wildfires were attributed to increases in nighttime persistence whereby under the driest conditions, daytime fire activity detected by MODIS was more likely to continue burning with sufficient vigour to be detected again at night. From 2003-2020, MODIS detected significant (p < 0.01) increasing trends in nighttime wildfire fire activity, with a +54%, +42% and +21% increase in the annual nighttime sum of FRP, annual nighttime active fire pixel counts and annual mean nighttime per-pixel values of FRP, respectively, detected in the latter half of the study period. Nighttime trends were corroborated using observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) as well annual wildfire statistics reported by U.S. federal, state and local agencies. Moreover, MODIS detected a significant positive trend in the nighttime proportion of FRP emitted from wildfires, indicating that in the absence of diurnal differences in detection biases, increases in nighttime fire activity since 2003 have outpaced daytime increases. However an analysis of MODIS omission rates revealed that increasing nighttime proportions were at least partially attributed to a relatively greater improvement in nighttime detection performance compared to the daytime for larger wildfires burning during drier conditions. Nighttime fire activity already poses additional risks to firefighters and communities, and this work suggests that projected increases in the frequency of large wildfires will be accompanied by increases in the extent and intensity of nighttime fire activity.
Shirmard, HojatFarahbakhsh, EhsanMuller, R. DietmarChandra, Rohitash...
21页
查看更多>>摘要：The decline of the number of newly discovered mineral deposits and increase in demand for different minerals in recent years has led exploration geologists to look for more efficient and innovative methods for processing different data types at each stage of mineral exploration. As a primary step, various features, such as lithological units, alteration types, structures, and indicator minerals, are mapped to aid decision-making in targeting ore deposits. Different types of remote sensing datasets, such as satellite and airborne data, make it possible to overcome common problems associated with mapping geological features. The rapid increase in the volume of remote sensing data obtained from different platforms has encouraged scientists to develop advanced, innovative, and robust data processing methodologies. Machine learning methods can help process a wide range of remote sensing datasets and determine the relationship between components such as the reflectance continuum and features of interest. These methods are robust in processing spectral and ground truth measurements against noise and uncertainties. In recent years, many studies have been carried out by supplementing geological surveys with remote sensing datasets, which is now prominent in geoscience research. This paper provides a comprehensive review of the implementation and adaptation of some popular and recently established machine learning methods for processing different types of remote sensing data and investigates their applications for detecting various ore deposit types. We demonstrate the high capability of combining remote sensing data and machine learning methods for mapping different geological features that are critical for providing potential maps. Moreover, we find there is scope for advanced methods such as deep learning to process the new generation of remote sensing data that provide high spatial and spectral resolution for creating improved mineral prospectivity maps.
Hermosilla, TxominWulder, Michael A.White, Joanne C.Coops, Nicholas C....
17页
查看更多>>摘要：Deriving land cover from remotely sensed data is fundamental to many operational mapping and reporting programs as well as providing core information to support science activities. The ability to generate land cover maps has benefited from free and open access to imagery, as well as increased storage and computational power. The accuracy of the land cover maps is directly linked to the calibration (or training) data used, the predictors and ancillary data included in the classification model, and the implementation of the classification, among other factors (e.g., classification algorithm, land cover heterogeneity). Various means for improving calibration data can be implemented, including using independent datasets to further refine training data prior to mapping. Opportunities also arise from a profusion of possible calibration datasets from pre-existing land cover products (static and time series) and forest inventory maps through to observation from airborne and spaceborne lidar observations. In this research, for the 650 Mha forested ecosystems of Canada, we explored approaches to refine calibration data, integrate novel predictors, and optimize classifier implementation. We refined calibration data using measures of forest vertical structure, integrated novel spatial (via distance-to metrics) model predictors, and implemented a regionalized approach for optimizing training data selection and model-building to ensure local relevance of calibration data and capture of regional variability in land cover conditions. We found that additional vetting of training data involved the removal of 44.7% of erroneous samples (e.g. treed vegetation without vertical structure) from the training pool. Nationally, distance to ephemeral waterbodies was a key predictor of land cover, while the importance of distance to permanent water bodies varied on a regional basis. Regionalization of model implementation ensured that classification models used locally relevant descriptors and resulted in improved classification outcomes (overall accuracy: 77.9% +/- 1.4%) compared to a generalized, national model (70.3% +/- 2.5%). The methodological developments presented herein are portable to other land cover projects, monitoring programs, and remotely sensed data sources. The increasing availability of remotely sensed data for land cover mapping, as well as non-image data for aiding with model development (from calibration data to complementary spatial data layers) provide new opportunities to improve and further automate land cover mapping procedures.
查看更多>>摘要：Rapid shrub expansion has been observed across the Arctic, driving a need for regional-scale estimates of shrub biomass and shrub-mediated ecosystem processes such as rainfall interception. Synthetic-Aperture Radar (SAR) data have been shown sensitive to vegetation canopy characteristics across many ecosystems, thereby potentially providing an accurate and cost-effective tool to quantify shrub canopy cover. This study evaluated the sensitivity of L-band Advanced Land Observing Satellite 2 (ALOS-2) data to the aboveground biomass and Leaf Area Index (LAI) of dwarf birch and alder in the Trail Valley Creek watershed, Northwest Territories, Canada. The sigma degrees VH /sigma degrees VV ratio showed strong sensitivity to both LAI (R2 = 0.72 with respect to in-situ measurements) and wet aboveground biomass (R2 = 0.63) of dwarf birch. Our ALOS-2-derived maps revealed high variability of birch shrub LAI and biomass across spatial scales. The LAI map was fed into the sparse Gash model to estimate shrub rainfall interception, an important but under-studied component of the Arctic water balance. Results suggest that on average across the watershed, 17 +/- 3% of incoming rainfall was intercepted by dwarf birch (during summer 2018), highlighting the importance of shrub rainfall interception for the regional water balance. These findings demonstrate the unexploited potential of L-band SAR observations from satellites for quantifying the impact of shrub expansion on Arctic ecosystem processes.
查看更多>>摘要：The Tibetan Plateau (TP) hosts numerous glaciers and lakes which are critical natural water reserves but highly vulnerable to changing climate. In contrast to general drying trends in global endorheic basins in recent decades, the widespread lake expansions across the endorheic TP stand out as a puzzling "anomaly". To quantify the contribution of glacier mass changes to lake expansion at fine basin-scale details, we compute spatially resolved estimation of mass change in both glaciers and lakes across the endorheic TP between 2000 and 2010/14 based on multi-mission remote sensing observations. Our glacier mass balance estimates were based on the differences between the newly released global TanDEM-X DEM and the historical SRTM-C DEM, which provide nearly complete coverage (98%) of the glacierized area on the endorheic TP. We provide lake water storage changes of all lakes 1 km(2) on the inner TP. These estimates reveal that the massive lake water increase (9.44 +/- 1.43 Gt yr(-1)) was essentially not from the mass loss of glaciers which represents only about 4.7 +/- 8.8% of the lake water change (0.44 +/- 0.80 Gt yr(-1)). The relationship in individual basins was, however, highly heterogeneous. About 20% of total lake storage gain had no causality with glacier feeding. In comparison, for 28% of lake water surplus, mainly in the northwestern TP, the positive glacier mass balance infers that glaciers retained some precipitation surplus that could otherwise have been drained to downstream lakes. For the other 52% of lake storage gain, mostly in southern and eastern regions, the glacier mass loss varied among the basins with limited contributing levels (mostly <20%). Our analyses highlight remarkable spatial and temporal variabilities in lake/glacier changes on the endoreic TP and contribute to a better understanding of the role of glaciers in the recent Tibetan lake growth and the impact of climate change on the two types of water reserves.
Dawson, GeoffreyLandy, JackTsamados, MichelKomarov, Alexander S....
18页
查看更多>>摘要：Satellite observations of pan-Arctic sea ice thickness have so far been constrained to winter months. For radar altimeters, conventional methods cannot differentiate leads from meltwater ponds that accumulate at the ice surface in summer months, which is a critical step in the ice thickness calculation. Here, we use over 350 optical and synthetic aperture radar (SAR) images from the summer months to train a 1D convolution neural network for separating CryoSat-2 radar altimeter returns from sea ice floes and leads with an accuracy >80%. This enables us to generate the first pan-Arctic measurements of sea ice radar freeboard for May-September between 2011 and 2020. Results indicate that the freeboard distributions in May and September compare closely to those from a conventional 'winter' processor in April and October, respectively. The freeboards capture expected patterns of sea ice melt over the Arctic summer, matching well to ice draft observations from the Beaufort Gyre Exploration Program (BGEP) moorings. However, compared to airborne laser scanner freeboards from Operation IceBridge and airborne EM ice thickness surveys from the Alfred Wegener Institute (AWI) IceBird program, CryoSat-2 freeboards are underestimated by 0.02-0.2 m, and ice thickness is underestimated by 0.28-1.0 m, with the largest differences being over thicker multi-year sea ice. To create the first pan-Arctic summer sea ice thickness dataset we must address primary sources of uncertainty in the conversion from radar freeboard to ice thickness.
查看更多>>摘要：High-throughput mapping of latent heat flux (MET) is critical to efforts to optimize water resources management and to accelerate forest tree breeding for improved drought tolerance. Ideally, investigation of the energy response at the tree level may promote tailored irrigation strategies and, thus, maximize crop biomass productivity. However, data availability is limited and planning experimental campaigns in the field can be highly operationally complex. To this end, a multi-platform multi-sensor observational approach is herein developed to dissect the MET signature of a black poplar (Populus nigra) breeding population ("POP6") at the canopy level. POP6 comprised more than 4600 trees representing 503 replicated genotypes, whose parents were derived from contrasting environmental conditions. Trees were trialed in two adjacent plots where different irrigation treatments (moderate drought [mDr] and well-watered [WW]) were applied. Data collected from satellite and unmanned aerial vehicles (UAVs) remote sensing as well as from ground-based proximal sensors were integrated at consistent spatial aggregation and combined to compute the surface energy balance of the trees through a modified Priestley-Taylor method. Here, we demonstrated that MET response was significantly different between WW and mDr trees, whereby genotypes in mDr conditions exhibited larger standard deviations. Importantly, genotypes classified as drought tolerant based on the stress susceptibility index (SSI) presented MET values significantly higher than the rest of the population. This study confirmed that water limitation in mDr settings led to reduced soil moisture in the tree root zone and, thus, to lower MET. These results pave the way to breeding poplar and other bioenergy crops with this underexploited trait for higher MET. Most notably, the illustrated work demonstrates a multi-platform multi-sensor data fusion approach to tackle the global challenge of monitoring landscape-scale ecosystem processes at fine resolution.
Plant, GenevieveKort, Eric A.Murray, Lee T.Maasakkers, Joannes D....
14页
查看更多>>摘要：Anthropogenic methane emissions from urban centers are important and addressable, yet remain poorly characterized, and the representativeness of studies from individual cities is unknown. A satellite-based approach provides a pathway to tackle this challenge on a national or global scale. Here we present a space-based method that uses the simultaneous, daily observation of methane and carbon monoxide from the TROPOMI satellite to estimate urban methane emissions. We assess and validate the method and demonstrate that using these simultaneous observations enables robust assessment of methane emissions from urban centers without relying on atmospheric transport models. Initial assessments with this approach in eight United States cities suggest emission inventory underestimates previously discovered in older East Coast cities are more broadly representative, with aggregated emissions totaling 1.47 (0.56, 3.19, 95% confidence interval) Tg CH4/year, compared to the Environmental Protection Agency estimate of 0.52 Tg CH4/year. We show this data driven approach provides a pathway to study urban methane emissions across the globe and track how emissions respond if urban mitigation measures are implemented by investigating three additional megacities outside the United States.
查看更多>>摘要：Many countries and regions build underground gas storages (UGSs) to regulate the energy demand and supply in different seasons. The Hutubi underground gas storage (HUGS) in Xinjiang province is the largest UGS in China, so it is of great significance to monitor its operation. At present, the researches on the HUGS mainly rely on traditional geodetic monitoring (e.g., in-situ leveling and global navigation satellite system (GNSS)), which has a short time span and low spatial resolution. The whole-domain and long-temporal sequence surface displacements induced by gas recovery (before 2013) and injection/extraction (since 09/06/2013) were seldom reported. In this study, the large-scale background deformation was firstly obtained using the ALOS PALSAR data (2006-2011), and the displacement time series of the HUGS over 2003-2020 was observed by all available Synthetic Aperture Radar (SAR) data from multiple SAR sensors (Envisat ASAR, TerraSAR/TanDEM-X, Sentinel1). The results show that this area had a long history of slow subsidence (2.2 mm/yr) before 2013. Since 06/ 2013, the surface of the HUGS showed periodic uplift and subsidence with a net uplift. The average uplift rate in the center was about 7 mm/yr during 08/2013-07/2015 and 13 mm/yr during 03/2015-05/2020. The deformation has a temporal correspondence with the gas extraction/injection process. The accuracy is assessed by the cross-validation of the results of different datasets and the GNSS measurements. The compound dislocation model (CDM) is used to model the dynamic displacements caused by gas injection/extraction in HUGS. This model can reflect the UGS volume changes and the estimated central depth (3499.7 m) and height (108.6 m) of the storage are consistent with the actual central depth (-3585 m) and height (-110 m). We also restore the dynamic change of the pore pressure and the injected gas volume during the 5-7 cycles, on the basis of the linear relations between gas injection volume, pore pressure, and CDM parameters. In 12/2017, the estimated gas inventory of the HUGS is 91.29 x 108 m3, about 4.7% smaller than the real gas inventory of 95.77 x 108 m3. At the end of the seventh injection/extraction cycle, the predicted maximum gas injection volume and pore pressure reached 11.2 billion m3 and 36 MPa, respectively.
NSTL
Elsevier