Towards Open-Scenario Semi-supervised Medical Image Classification

Semi-supervised learning (SSL) has attracted much attention since it reduces the expensive costs of collecting adequate well-labeled training data, especially for deep learning methods. However, traditional SSL is built upon an assumption that labeled and unlabeled data should be from the same distribution e.g., classes and domains. However, in practical scenarios, unlabeled data would be from unseen classes or unseen domains, and it is still challenging to exploit them by existing SSL methods. Therefore, in this paper, we proposed a unified framework to leverage these unseen unlabeled data for open-scenario semi-supervised medical image classification. We first design a novel scoring mechanism, called dual-path outliers estimation, to identify samples from unseen classes. Meanwhile, to extract unseen-domain samples, we then apply an effective variational autoencoder (VAE) pre-training. After that, we conduct domain adaptation to fully exploit the value of the detected unseen-domain samples to boost semi-supervised training. We evaluated our proposed framework on dermatology and ophthalmology tasks. Extensive experiments demonstrate our model can achieve superior classification performance in various medical SSL scenarios

Cumulative Response of Ecosystem Carbon and Nitrogen Stocks to Chronic CO2 Exposure in a Subtropical Oak Woodland

·Rising atmospheric carbon dioxide (CO2) could alter the carbon (C) and nitrogen (N) content of ecosystems, yet the magnitude of these effects are not well known. We examined C and N budgets of a subtropical woodland after 11 yr of exposure to elevated CO2. ·We used open-top chambers to manipulate CO2 during regrowth after fire, and measured C, N and tracer 15N in ecosystem components throughout the experiment. ·Elevated CO2 increased plant C and tended to increase plant N but did not significantly increase whole-system C or N. Elevated CO2 increased soil microbial activity and labile soil C, but more slowly cycling soil C pools tended to decline. Recovery of a long-term 15N tracer indicated that CO2 exposure increased N losses and altered N distribution, with no effect on N inputs. · Increased plant C accrual was accompanied by higher soil microbial activity and increased C losses from soil, yielding no statistically detectable effect of elevated CO2 on net ecosystem C uptake. These findings challenge the treatment of terrestrial ecosystems responses to elevated CO2 in current biogeochemical models, where the effect of elevated CO2 on ecosystem C balance is described as enhanced photosynthesis and plant growth with decomposition as a first-order response

Monitoring Landscape Dynamics in Central U.S. Grasslands with Harmonized Landsat-8 and Sentinel-2 Time Series Data

Remotely monitoring changes in central U.S. grasslands is challenging because these landscapes tend to respond quickly to disturbances and changes in weather. Such dynamic responses influence nutrient cycling, greenhouse gas contributions, habitat availability for wildlife, and other ecosystem processes and services. Traditionally, coarse-resolution satellite data acquired at daily intervals have been used for monitoring. Recently, the harmonized Landsat-8 and Sentinel-2 (HLS) data increased the temporal frequency of the data. Here we investigated if the increased data frequency provided adequate observations to characterize highly dynamic grassland processes. We evaluated HLS data available for 2016 to (1) determine if data from Sentinel-2 contributed to an improvement in characterizing landscape processes over Landsat-8 data alone, and (2) quantify how observation frequency impacted results. Specifically, we investigated into estimating annual vegetation phenology, detecting burn scars from fire, and modeling within-season wetland hydroperiod and growth of aquatic vegetation. We observed increased sensitivity to the start of the growing season (SOST) with the HLS data. Our estimates of the grassland SOST compared well with ground estimates collected at a phenological camera site. We used the Continuous Change Detection and Classification (CCDC) algorithm to assess if the HLS data improved our detection of burn scars following grassland fires and found that detection was considerably influenced by the seasonal timing of the fires. The grassland burned in early spring recovered too quickly to be detected as change events by CCDC; instead, the spectral characteristics following these fires were incorporated as part of the ongoing time-series models. In contrast, the spectral effects from late-season fires were detected both by Landsat-8 data and HLS data. For wetland-rich areas, we used a modified version of the CCDC algorithm to track within-season dynamics of water and aquatic vegetation. The addition of Sentinel-2 data provided the potential to build full time series models to better distinguish different wetland types, suggesting that the temporal density of data was sufficient for within-season characterization of wetland dynamics. Although the different data frequency, in both the spatial and temporal dimensions, could cause inconsistent model estimation or sensitivity sometimes; overall, the temporal frequency of the HLS data improved our ability to track within-season grassland dynamics and improved results for areas prone to cloud contamination. The results suggest a greater frequency of observations, such as from harmonizing data across all comparable Landsat and Sentinel sensors, is still needed. For our study areas, at least a 3-day revisit interval during the early growing season (weeks 14–17) is required to provide a \u3e50% probability of obtaining weekly clear observations

Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm

Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer's accuracy of 93% and a user's accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R-square values over 0.7 and field surveys with an accuracy of >= 95% for cultivated croplands and >= 76% for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season

Ecosystem CO2 exchange

Global temperature increases and precipitation changes are both expected to alter ecosystem carbon (C) cycling. We tested responses of ecosystem C cycling to simulated climate change using field manipulations of temperature and precipitation across a range of grass-dominated ecosystems along an elevation gradient in northern Arizona. In 2002, we transplanted intact plant-soil mesocosms to simulate warming and used passive interceptors and collectors to manipulate precipitation. We measured daytime ecosystem respiration and net ecosystem C exchange throughout the growing season in 2008 and 2009. Warming generally stimulated ecosystem respiration and photosynthesis, but had variable effects on daytime net C exchange. Increased precipitation stimulated ecosystem C cycling only in the driest ecosystem at the lowest elevation, while decreased precipitation showed no effects on ecosystem C cycling across all ecosystems. No significant interaction between temperature and precipitation treatments was observed. Our findings suggest that warming exerted the strongest influence on ecosystem C cycling in both years, by modulating soil moisture in the wet year and soil temperature in the dry year

Spatiotemporal Analysis of Landsat-8 and Sentinel-2 Data to Support Monitoring of Dryland Ecosystems

Drylands are the habitat and source of livelihood for about two fifths of the world’s population and are highly susceptible to climate and anthropogenic change. To understand the vulnerability of drylands to changing environmental conditions, land managers need to effectively monitor rates of past change and remote sensing offers a cost-effective means to assess and manage these vast landscapes. Here, we present a novel approach to accurately monitor land-surface phenology in drylands of the Western United States using a regression tree modeling framework that combined information collected by the Operational Land Imager (OLI) onboard Landsat 8 and the Multispectral Instrument (MSI) onboard Sentinel-2. This highly-automatable approach allowed us to precisely characterize seasonal variations in spectral vegetation indices with substantial agreement between observed and predicted values (R2 = 0.98; Mean Absolute Error = 0.01). Derived phenology curves agreed with independent eMODIS phenological signatures of major land cover types (average r-value = 0.86), cheatgrass cover (average r-value = 0.96), and growing season proxies for vegetation productivity (R2 = 0.88), although a systematic bias towards earlier maturity and senescence indicates enhanced monitoring capabilities associated with the use of harmonized Landsat-8 Sentinel-2 data. Overall, our results demonstrate that observations made by the MSI and OLI can be used in conjunction to accurately characterize land-surface phenology and exclusion of imagery from either sensor drastically reduces our ability to monitor dryland environments. Given the declines in MODIS performance and forthcoming decommission with no equivalent replacement planned, data fusion approaches that integrate observations from multispectral sensors will be needed to effectively monitor dryland ecosystems. While the synthetic image stacks are expected to be locally useful, the technical approach can serve a wide variety of applications such as invasive species and drought monitoring, habitat mapping, production of phenology metrics, and land-cover change modeling

Responses of terrestrial ecosystems to temperature and precipitation change: a meta-analysis of experimental manipulation

Global mean temperature is predicted to increase by 2-7 °C and precipitation to change across the globe by the end of this century. To quantify climate effects on ecosystem processes, a number of climate change experiments have been established around the world in various ecosystems. Despite these efforts, general responses of terrestrial ecosystems to changes in temperature and precipitation, and especially to their combined effects, remain unclear. We used meta-analysis to synthesize ecosystem-level responses to warming, altered precipitation, and their combination. We focused on plant growth and ecosystem carbon (C) balance, including biomass, net primary production (NPP), respiration, net ecosystem exchange (NEE), and ecosystem photosynthesis, synthesizing results from 85 studies. We found that experimental warming and increased precipitation generally stimulated plant growth and ecosystem C fluxes, whereas decreased precipitation had the opposite effects. For example, warming significantly stimulated total NPP, increased ecosystem photosynthesis, and ecosystem respiration. Experimentally reduced precipitation suppressed aboveground NPP (ANPP) and NEE, whereas supplemental precipitation enhanced ANPP and NEE. Plant productivity and ecosystem C fluxes generally showed higher sensitivities to increased precipitation than to decreased precipitation. Interactive effects of warming and altered precipitation tended to be smaller than expected from additive, single-factor effects, though low statistical power limits the strength of these conclusions. New experiments with combined temperature and precipitation manipulations are needed to conclusively determine the importance of temperature-precipitation interactions on the C balance of terrestrial ecosystems under future climate conditions