2 research outputs found
Methodology for generating a global forest management layer
The first ever global map of forest management was generated based on remote sensing data. To collect training data, we launched a series of Geo-Wiki (https://www.geo-wiki.org/) campaigns involving forest experts from different world regions, to explore which information related to forest management could be collected by visual interpretation of very high-resolution images from Google Maps and Microsoft Bing, Sentinel time series and normalized difference vegetation index (NDVI) profiles derived from Google Earth Engine. A machine learning technique was then used with the visually interpreted sample (280K locations) as a training dataset to classify PROBA-V satellite imagery. Finally, we obtained a global wall-to-wall map of forest management at a 100m resolution for the year 2015. The map includes classes such as intact forests; forests with signs of management, including logging; planted forests; woody plantations with a rotation period up to 15 years; oil palm plantations; and agroforestry. The map can be used to deliver further information about forest ecosystems, protected and observed forest status changes, biodiversity assessments, and other ecosystem-related aspects
Global forest management data at a 100m resolution for the year 2015
We provide four data records:
1.The reference data set as a comma-separated file ("reference_data_set.csv") with the following attributes:
“ID” is a unique location identifier
“Latitude, Longitude” are centroid coordinates of a 100m x 100m pixel.
“Land_use_ID “is a land use class:
11 - Naturally regenerating forest without any signs of human activities, e.g., primary forests.
20 - Naturally regenerating forest with signs of human activities, e.g., logging, clear cuts etc.
31 - Planted forest.
32 - Short rotation plantations for timber.
40 - Oil palm plantations.
53 - Agroforestry.
“Flag” identifies a data origin: 1- the crowdsourced locations, 2- the control data set, 0 – the additional experts' classifications following the opportunistic approach.
2. The 100 m forest management map in a geoTiff format with the classes presented - "FML_v3.2.tif ".
3. The predicted class probability from the Random Forest classification in a geoTiff format - "ProbaV_LC100_epoch2015_global_v2.0.3_forest-management--layer-proba_EPSG-4326.tif"
4. Validation data set as a comma-separated file ("validation_data_set.csv) with the following attributes:
“ID” is a unique location identifier
“pixel_center_x” , “pixel_center_y ” are centroid coordinates of a 100m x 100m pixel in lat/lon projection
“first_landuse_class “is a land use class, as in (1).
“second_landuse_class “is a second possible land use class, as in (1), identified in case it was difficult to assign one class with high confidence