Google earth download free 2015 full version

#Google earth download free 2015 full version series#

Land Cover and Land Cover change information is used by resource managers, policy makers and scientists studying the global carbon cycle, biodiversity loss and land degradation. Visual analysisВ showed that changed areasВ regularly corresponded to changes observed with higher resolution imagery.ДЄnnual land cover maps (top) and Sentinel-2 RGB (bottom) show the loss of forest (green/brown) and subsequent regrowth of grass (yellow) after fires in Portugal.ДЇull land cover map legend is shown on the right. This assessment revealed that no-change pixels were mapped with high accuracy (99.6%) and cover changes are more likely committed than omitted.ДЄdditional validation points collected to represent the changes between 20

Validation dataset for the land cover classification of reference year 2015, per continentДЄ first statistical assessment of generic land cover changes between 20В was done to gain understanding in the consistency of and differences between the annual maps, at global and continental scales. Spatial accuracy assessment for three aggregated classes (forest, crops and natural vegetation) against a set of over 200K independent validation points revealed a high level of accuracy in different regions of the world and highlighted areas for further improvement. The updatedВ 2015 classification mapВ showsВ slightly higher accuracyВ than the previous version (v2.0.2) and improvements in characterizing forest, cropland and permanent water classes. The statistical validation meets the CEOS Land Product Validation Stage 4 requirements. The version 3.0 annual 100m land cover classesВ were mapped with high temporal stability across yearsВ and an overall mapping accuracy just over 80% (80.6% in 2015, 80.3% in 2019), when compared to the 28KВ independent validation points.

#Google earth download free 2015 full version series#

Improved time series break detection and temporal post-processing, combining a BFAST algorithm on a time series of MODIS Near-Infrared Reflectance on Vegetation (NIRv) data and a Hidden Markov Model.Random Forest model of base year (2015) applied to metrics for later years.near-real time: for the most recent complete year, with full year prior and three months posterior data.consolidated: for epochs with full year prior and posterior data available.Classifier and regression models are re-used in later years. base: reference year, calculated using full year prior and posterior data.Metrics calculated per epoch of three years, with annual updates and three different processing modes:.Addition of near-real time processing mode for year 2019, with one year prior but only three months posterior satellite observations as input.Global coverage, following the demonstration of annual change maps over Africa (for period 2015-2018).Temporal post-processing, including time series break detection, to ensure consistency of the classification and cover fractions across epoch years Use of well-established external datasets for the shoreline masking, ecological regionalisation, built-up (urban) cover, permanent and seasonal water cover, arctic vegetation, weather and topography.168 000 points) from Geo-WIKI's crowd-sourcing Extraction of metrics: base reflectances, time series harmonics, vegetation indicators, descriptive over the 3-year epoch period.Data fusion using Kalman filtering between 5-daily 100m and daily 300m input data.Calculation of input data density indicator.Data cleaningВ by sensor specific status masks and (temporal) outlier detection techniques.Dedicated satellite pre-processing on Sentinel-2 tiling grid (110x110km), with geometric and atmospheric corrections.Classification and cover fraction generation, per 3-year epoch, including: