黑色防渗膜覆盖的固体废物堆放场TM图像决策树分类初探

利用LandsatTM卫星遥感图像数据对北京市有黑色防渗膜覆盖的大型固体废物堆放场进行了决策树分类试验。结果表明:辅以居民地、道路等信息,该方法可以很好地识别出有黑色防渗膜覆盖的大型固体废物堆放场。     

Aerosol optical thickness (AOT) retrieval over land using satellite image-based algorithm

The aim of this study is to present and apply the proposed algorithm on archived time series Landsat TM over an urban area in the vicinity of Heathrow Airport (UK) acquired in 1984–1986 and to two up-to-dated Landsat TM images in the vicinity of Paphos Airport in Cyprus acquired in July–August 2008. The monitoring of aerosol concentrations becomes a high environmental priority particularly in urban areas. The proposed algorithm has been developed to allow the quantification of the aerosol optical thickness (AOT) over land. The algorithm compares multitemporal satellite data sets and evaluates radiometric alterations due to the optical atmospheric effects of aerosols. Novel features of this algorithm which is based on the application of radiative transfer calculations are the inclusion of applying iteration procedures for selecting the suitable object for determining the aerosol optical thickness and the automatic division into working grid cells. AOT retrieval from Landsat TM band 1 images of Cyprus has been cross-validated satisfactorily by comparing AOT values found from handheld MICROTOPS II sun photometer.     

Declining greenness in Arctic-boreal lakes

The highest concentration of the world’s lakes are found in Arctic-boreal regions [C. Verpoorter, T. Kutser, D. A. Seekell, L. J. Tranvik, Geophys. Res. Lett. 41, 6396–6402 (2014)], and consequently are undergoing the most rapid warming [J. E. Overland et al., Arctic Report Card (2018)]. However, the ecological response of Arctic-boreal lakes to warming remains highly uncertain. Historical trends in lake color from remote sensing observations can provide insights into changing lake ecology, yet have not been examined at the pan-Arctic scale. Here, we analyze time series of 30-m Landsat growing season composites to quantify trends in lake greenness for >4 × 10⁵ waterbodies in boreal and Arctic western North America. We find lake greenness declined overall by 15% from the first to the last decade of analysis within the 6.3 × 10⁶-km² study region but with significant spatial variability. Greening declines were more likely to be found in areas also undergoing increases in air temperature and precipitation. These findings support the hypothesis that warming has increased connectivity between lakes and the land surface [A. Bring et al., J. Geophys. Res. Biogeosciences 121, 621–649 (2016)], with implications for lake carbon cycling and energy budgets. Our study provides spatially explicit information linking climate to pan-Arctic lake color changes, a finding that will help target future ecological monitoring in remote yet rapidly changing regions.

Recent widespread changes in Arctic-boreal primary productivity linked to global climate change have been documented in terrestrial (4–7) and oceanic (8, 9) ecosystems. Satellite records have enabled large-scale analysis, providing evidence of uneven changes in vegetation growth in tundra (5, 6, 10–13) and boreal ecosystems (5, 14, 15), as inferred from vegetation indices. Thus, satellite records have provided critical insight into long-term global change in remote Arctic and boreal regions.However, less attention has been paid to the large-scale changes in satellite-derived observations of lake color, owing in part to the prevalence of small lakes, which were previously not detectable by earlier generations of satellites (16). Lakes are abundant in permafrost landscapes (1, 17), supporting crucial ecosystem services such as wildlife habitat, biodiversity, nutrient and carbon cycling, and recreation. The lack of pan-Arctic studies of lake color is surprising because half of the world’s total lake surface area is concentrated in Arctic-boreal regions (18), where permafrost thaw has been predicted to dramatically alter lake physical and optical properties (19).Evidence from field studies has suggested satellite lake color is linked to primary productivity (20) and dissolved organic carbon concentrations, which in turn may be influenced by terrestrial processes (21). For example, shifts in terrestrial vegetation cover are hypothesized to influence lake ecological structure and function (22). As the frequency and timing of spring thaw change, along with total annual precipitation, the magnitude and pathways of water moving within the landscape will impact the routing of terrestrial carbon into aquatic ecosystems. However, the responses of Arctic-boreal lakes to changes in terrestrial ecosystems remains highly uncertain.Lake color, as observed from space, offers a powerful approach for monitoring at large scales. Satellites such as Landsat observe lake color by quantifying surface reflectance, the fraction of incoming solar radiation emerging from and reflected off a lake’s surface in the visible wavelengths (∼450 to 700 nm). This surface reflectance, referred hereto after as satellite lake color, has been used to infer properties relevant to carbon cycling, including lake clarity (23, 24), phytoplankton stocks (25), and primary production (20, 26). Despite the fact that lake color has been designated an essential climate variable by the Global Climate Observing System, analysis of pan-Arctic trends in satellite lake color remains limited. The majority of satellite lake color studies have been conducted outside Arctic and boreal regions (27), with most high northern latitude work focused on dissolved organic carbon dynamics (28–31), single regions (32), or physical processes such as changes in inundation extent (33–37). This study builds off that work by using 35 y of satellite remote sensing data to systematically investigate changes in lake color, in particular greenness, at the continental scale.In contrast to satellite remote sensing, limnologists typically measure lake color not as reflectance, but rather as absorbance. Dissolved organic matter contributes color to lakes by absorbing light in shorter wavelengths (∼250 to 440 nm) (38). This absorption can give lakes a “brown” appearance. Decades of field studies in northern lakes have led to the hypothesis that dissolved organic matter concentrations are increasing due to shifts in hydrology and climate (39–41), resulting in large-scale changes in lake color manifested as increasing light absorption. This increase in absorption, termed browning, has been shown to influence primary productivity (42–44) because dissolved organic matter absorbs light in the same wavelengths required for photosynthesis (45). Historic Landsat sensors did not measure reliably over water in these shorter wavelengths (46), but they do measure reflectance in green wavelengths (∼560 nm).Here we explore the hypothesis that satellite lake color, and in particular lake greenness, is changing at the continental scale. We selected reflectance in the green wavelengths, referred to here as greenness, for two reasons. First, green reflectance has ecological significance. Photosynthetic pigments preferentially reflect green light (27). For this reason, satellite greenness has been used for decades to derive primary productivity in the ocean (47, 48) and, more recently, in lakes (26, 49–51). Second, previous studies have demonstrated the green band is the least influenced by intersensor calibration errors (52, 53), making it a robust choice for time-series analysis. To date, we are aware of no study that has applied this approach to evaluate continental-scale trajectories of pan-Arctic lake greenness, which has been shown to closely track rates of lake primary productivity in diverse northern lake ecosystems (20, 54).To document trends in satellite lake color, we analyze time series of lake reflectance in green wavelengths (∼560 nm) for over 400,000 lakes in western North America’s Arctic-boreal zone. Using over 54 million observations from Landsat 5, 7, and 8, we generate seasonal composites of lake greenness from 1984 to 2019 for 542,934 lakes larger than 0.1 km² as a complement to existing studies on growing season greenness in terrestrial ecosystems. At each lake site, we first extracted a time series of lake greenness, defined as growing season surface reflectance (R_s, unitless) in the green wavelengths (∼560 nm), and then calculated the overall trend in lake greenness (∆green R_s, in y⁻¹; Materials and Methods). We discuss our findings in the context of physiochemical, biological, and hydrologic controls. Ultimately, patterns of greenness were compared to historical changes in the landscape and climatic parameters known to influence lake productivity.     

An automated method for the detection of emperor penguin colonies from Landsat 8 imagery

Detailed information on the emperor penguin colonies is crucial for estimating total populations and analysing population migration. This study presents a new method for detecting colonies of emperor penguins by identifying areas covered with their faeces using Landsat 8 data. Top of Atmosphere (TOA) reflectance and Brightness Temperature (BT) of Landsat images are used as inputs. This method first uses normalized spectral indexes (normalized difference water index (NDWI), normalized difference faeces index (NDFI), normalized difference snow index (NDSI)), band ratios and an individual band reflectance to produce probability masks for areas covered by faeces differentiating from other land cover types. Then, after eliminating those pixels that have abnormal elevations and performing a median filter, the probability masks for those areas covered by faeces are used to derive the corresponding polygons. Subsequently, the geometric centre of the polygons of those areas covered by faeces is used as the location for a corresponding colony. For a widely distributed set of data around the Ross and Somov Seas, the overall classification accuracy is as high as 91% with a small standard deviation of 0.12.     

Validating Satellite-Derived Land Surface Temperature with in Situ Measurements: A Public Health Perspective

Background: Land surface temperature (LST) and percent surface imperviousness (SI), both derived from satellite imagery, have been used to characterize the urban heat island effect, a phenomenon in which urban areas are warmer than non-urban areas.Objectives: We aimed to assess the correlations between LSTs and SI images with actual temperature readings from a ground-based network of outdoor monitors.Methods: We evaluated the relationships among a) LST calculated from a 2009 summertime satellite image of the Detroit metropolitan region, Michigan; b) SI from the 2006 National Land Cover Data Set; and c) ground-based temperature measurements monitored during the same time period at 19 residences throughout the Detroit metropolitan region. Associations between these ground-based temperatures and the average LSTs and SI at different radii around the point of the ground-based temperature measurement were evaluated at different time intervals. Spearman correlation coefficients and corresponding p-values were calculated.Results: Satellite-derived LST and SI values were significantly correlated with 24-hr average and August monthly average ground temperatures at all but two of the radii examined (100 m for LST and 0 m for SI). Correlations were also significant for temperatures measured between 0400 and 0500 hours for SI, except at 0 m, but not LST. Statistically significant correlations ranging from 0.49 to 0.91 were observed between LST and SI.Conclusions: Both SI and LST could be used to better understand spatial variation in heat exposures over longer time frames but are less useful for estimating shorter-term, actual temperature exposures, which can be useful for public health preparedness during extreme heat events.