public read Forest Ecosystem Monitoring Cooperative

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(802) 656-0683femc@uvm.eduwww.uvm.edu/femcRaster stacks containing LT-GEE outputs for the entire FEMC service region from 1985-2023, and their corresponding metadata, in a zipped folder. These 30m resolution maps represent historical disturbances that were detected in Landsat time-series imagery by Landtrendr on Google Earth Engine (LT-GEE) under three different algorithm configurations or 'tunings'. The 'combo6' and 'rt75' tunings included were developed via this project, and a 'defaults' tuning (which we slightly adjusted from actual LT-GEE defaults) is provided for reference. All were based on LT-GEE segmentation of the Normalized Burn Ratio (NBR) spectral index as derived from Landsat c2. These tunings were selected to provide the 'best' overall range of results, based on an iterative cross-validation ('ground-truthing') procedure using extensive land use/harvesting records, time-series imagery (TimeSync) and field reconnaissance. Stacks are rasters (.tif files) with six bands, each band representing an output variable, including year of disturbance, magnitude, duration, rate of change and others (see metadata for detailed variable definitions). As such, they should be visualized and analyzed one variable or band at a time; combining two or more bands for GIS-based visualization is not recommended. Each of the three sets of LT-GEE outputs was further parsed into four geographic subregions within the FEMC service area: 1) NY, 2) ME, 3) MA, CT and RI, 4) VT and NH. This resulted in a total of 12 tif files. Forest Ecosystem Monitoring CooperativeColinBeiercontentProviderThe need for reliable landscape monitoring tools is growing rapidly with increasing attention and reliance on forests as natural climate solutions. However, none of the leading monitoring tools were developed for the forest types and disturbance regimes of the Northern Forest region. To address this problem, our objective was to train a forest change detective using the Landtrendr on Google Earth Engine (LTGEE) platform, which we found was the best performer, by far, yet with ample room for improvement. To this end, we carried out an iterative ‘tuning’ process to improve LT-GEE performance, which we evaluated by comparing outputs against three sources of ‘ground-truth’ data: landowner harvest records/maps, orthoimagery verification (using TimeSync) and field reconnaissance. Overall, through tuning we achieved substantial gains in LT-GEE performance for change detection in the US Northeast, especially for harvest-related disturbances. Based on the best LT-GEE tunings identified among those tested, we generated historical disturbance maps for the entire FEMC coverage area (NY, VT, NH, ME, RI, CT, MA) at 30m annual resolution from 1985-2023. These data products are archived with FEMC along with an online 'user's guide' including code, API scripts and helpful tips. Overall this project has taken the first essential steps in applying the LT-GEE change detection platform for landscape monitoring that supports forest science and stewardship across the US Northeast.LT GEE Historical Disturbance Maps CAFRIRaster stacks containing LT-GEE outputs for the entire FEMC service region from 1985-2023, and their corresponding metadata, in a zipped folder. These 30m resolution maps represent historical disturbances that were detected in Landsat time-series imagery by Landtrendr on Google Earth Engine (LT-GEE) under three different algorithm configurations or 'tunings'. The 'combo6' and 'rt75' tunings included were developed via this project, and a 'defaults' tuning (which we slightly adjusted from actual LT-GEE defaults) is provided for reference. All were based on LT-GEE segmentation of the Normalized Burn Ratio (NBR) spectral index as derived from Landsat c2. These tunings were selected to provide the 'best' overall range of results, based on an iterative cross-validation ('ground-truthing') procedure using extensive land use/harvesting records, time-series imagery (TimeSync) and field reconnaissance. Stacks are rasters (.tif files) with six bands, each band representing an output variable, including year of disturbance, magnitude, duration, rate of change and others (see metadata for detailed variable definitions). As such, they should be visualized and analyzed one variable or band at a time; combining two or more bands for GIS-based visualization is not recommended. Each of the three sets of LT-GEE outputs was further parsed into four geographic subregions within the FEMC service area: 1) NY, 2) ME, 3) MA, CT and RI, 4) VT and NH. This resulted in a total of 12 tif files. VMC.1809.3994mySQL/femc/data/archive/project/change-detection-for-us-northeast-forests-1/dataset/lt-gee-historical-disturbance-maps-cafri1985-01-012023-12-31No AttributesNo Definitionno data