Showing papers in "Remote Sensing of Environment in 2014"

TL;DR: This work provides practitioners with a set of “good practice” recommendations for designing and implementing an accuracy assessment of a change map and estimating area based on the reference sample data.

TL;DR: Landsat 8, a NASA and USGS collaboration, acquires global moderate-resolution measurements of the Earth's terrestrial and polar regions in the visible, near-infrared, short wave, and thermal infrared as mentioned in this paper.

TL;DR: A new Automated Water Extraction Index (AWEI) is introduced improving classification accuracy in areas that include shadow and dark surfaces that other classification methods often fail to classify correctly, using Landsat 5 TM images of several water bodies.

TL;DR: In this article, a two-step cloud, cloud shadow, and snow masking algorithm is used for eliminating noisy observations and a time series model that has components of seasonality, trend, and break estimates surface reflectance and brightness temperature.

TL;DR: In this article, three refinements were implemented in the V6.3.0 daily LST Product Generation Executive (PGE)16 code: 1) refine the original viewing angle dependent split-window algorithm by adding two sets of coefficients separately for retrieval of daytime and nighttime LSTs at bare soil pixels in the hot and warm bare soil zone (HAWBSZ) within latitude range from − 38° to 49.5°, 2) adjust the emissivity difference in bands 31 and 32 for bare-soil pixels in HAWBSZ, and

TL;DR: In this paper, a new active fire detection algorithm was proposed based on the well-established MODIS Fire and Thermal Anomalies product using a contextual approach to detect both day and nighttime biomass burning and other thermal anomalies.

TL;DR: Li et al. as mentioned in this paper quantified the diurnal and seasonal surface UHI intensity (SUHII, urban-suburban temperature difference) in China's 32 major cities, and analyzed their spatial variations and possible underlying mechanisms.

TL;DR: In this article, four global mosaics of Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (SAR) HH and HV polarization data were generated at 25m spatial resolution using data acquired annually from 2007 to 2010.

TL;DR: In this article, the authors used InSAR time-series analysis of ALOS data to resolve land subsidence in the entire central Mexico region, including 17 cities, and found that groundwater extraction mainly for agricultural and urban activities is the main cause of land subsiding.

TL;DR: In this article, the authors fused high-spatial resolution hyperspectral imagery with 22 pulse/m 2 lidar data at the individual crown object scale to map 29 common tree species in Santa Barbara, California, USA.

TL;DR: The goal is development of a cloud, cloud shadow, and snow detection algorithm that results in a multitemporal stack of images that is free of “noise” factors and thus suitable for detection of land cover change.

TL;DR: In this article, a new data fusion algorithm for producing Landsat-like LST data by blending daily MODIS and periodic Landsat TM datasets was presented, where the original Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was improved and modified for predicting thermal radiance and land surface temperature.

TL;DR: In this paper, the authors evaluate the potential of the OCO-2 for retrievals of chlorophyll fluorescence and also its dependence on clouds and aerosols, and find that the single-measurement precision is 0.3-0.5 Wm(exp -2)sr(exp-1) nm(exp −1) (15-25% of typical peak values), better than current measurements from space but still difficult to interpret on a single sounding basis.

TL;DR: In this paper, a UAV was deployed over the debris-covered tongue of the Lirung Glacier in Nepal and the mass loss and surface velocity of the glacier were derived based on ortho-mosaics and digital elevation models.

TL;DR: In this article, the authors present Landsat-8 imagery that reveals the impact of offshore wind farms on suspended sediments and the environmental impact of these wakes and the source of the suspended material are still unclear.

TL;DR: In this article, four remotely sensed datasets were assessed for inclusion into county-level corn and soybean yield forecasting efforts focused on the Corn Belt region of the central United States (US), including the Normalized Difference Vegetation Index (NDVI) as derived from the Terra satellite's Moderate Resolution Imaging Spectroradiometer (MODIS), daytime and nighttime land surface temperature (LST), and precipitation from the National Weather Service (NWS) Nexrad-based gridded data product.

TL;DR: In this article, a cluster-based method was developed to estimate optimal thresholds and map urban extent from the DMSP/OLS NTL data in five major steps, including data preprocessing, urban cluster segmentation, logistic model development, threshold estimation, and urban extent delineation.

TL;DR: In this article, the authors show that both the frequency and severity of droughts and floods over the plateau are intensified during therecent decade from three-decade total water storage anomalies (TWSA) generated by Gravity Recovery andClimate Experiment (GRACE) satellite data and artificial neural network (ANN) models.

TL;DR: In this paper, the seasonal cycle of photosynthesis as estimated from satellite fluorescence retrievals at wavelengths surrounding the 740nm emission feature was examined, and the seasonality of absorbed photosynthetically-active radiation (APAR) derived with reflectances from the MODerate-resolution Imaging Spectroradiometer (MODIS).

TL;DR: In this article, the influence of the predictor data (sensor) type is the most important factor for the accu- racy of biomass estimates, with LiDAR being preferable to hyperspectral data.

TL;DR: In this paper, a new aerosol product with 1 km spatial resolution derived by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was examined using a two-stage spatial statistical model with meteorological fields and land use parameters (e.g., forest cover, road length, elevation, and point emissions).

TL;DR: In this paper, a linear regression analysis performed between spatially averaged seasonal NDVI and a weighted average of field measurements explains 59% of the variability for the Gourma region over 1984-2011, and 38% for the Fakara region over 1994-2011.

TL;DR: In this article, an innovative phenology-based classification method was developed to map corn and soybeans in multiple years using training data limited to a single year, where the classifier employed by this method takes phenological metrics as the major input.

TL;DR: In this article, a general conceptual and technical approach for linking tropical forest EACDfield to LiDAR top-of-canopy height (TCH) using regional-scale inputs of basal area and wood density is presented.

TL;DR: In this article, the authors analyzed changes in forest types, forest disturbances, and forest recovery for the Carpathian ecoregion in Eastern Europe using the Landsat archive.

TL;DR: In this paper, the authors investigated the effects of different landslide conditioning factors on landslide susceptibility mapping and concluded that using additional factors, such as geology, soil, geology and land use/cover, does not significantly increase the accuracy of the landslide susceptibility map.

TL;DR: In this article, the authors analyzed the ITC level classification accuracy of boreal tree species (Pine, Spruce and Broadleaves), considering two remote sensing data types: hyperspectral and airborne laser scanning (ALS).

TL;DR: In this article, a satellite-derived soil moisture product and the Normalized Difference Vegetation Index (NDVI) were used to investigate the impact of soil moisture on vegetation across mainland Australia between 1991 and 2009.

TL;DR: In this article, the authors developed a land use/cover database of China (NLUD-C) at 1:100000 scale that contains Chinese land use and cover data of five periods (1980s, 1995, 2000, 2005 and 2008).

TL;DR: In this article, a satellite-based geographically weighted regression (GWR) model was developed to estimate the daily concentration of ground-level PM2.5 coincident to satellite overpass at regional scale.