This dataset of land disturbances of the Upper Peace Region of Alberta is derived from selected Landsat multispectral datasets captured in 5-year intervals from 1985 to 2015. Disturbances were detected through time-series comparison of the selected datasets, classified based on the time of first appearance on the landscape, and then attributed to four major disturbance types:
1) energy footprints (oil, gas, and coal mining activities), 2) non-energy footprints (other infrastructure), 3) cutblocks (primarily forest harvesting), and 4) wildfire. The disturbance-type data are stacked to create a 4-band TIFF image with each band representing the time-series data for one of the four disturbance types.
Attribute Accuracy Report: The overall accuracy, defined as the total correct pixels divided by the total number of pixels in the error matrix N. The Producer accuracy is an indication of the probability of a reference pixel being correctly classified. It is calculated as the total number of correct classified pixels in a class divided by total number of pixels in that class. The User accuracy is defined as the total number of correct classified pixels in a class divided by total number of pixels that were classified in that class. It indicates the probability that a pixel classified on the map represents that class on the ground. The Kappa coefficient is used to find out if the accuracy level is significantly better than a random result, providing a better comparison of different classifications.
Accuracy assessment of the land disturbance dataset was conducted based on the 1985 - 2010 forest disturbance data (http://opendata.nfis.org/mapserver/nfis-change_eng.html) from the Canadian Forest Service/Pacific Forestry Centre since forest disturbances are considered as a subset of the cumulative land disturbances.
Overall Accuracy = (3537442/3977538) 88.9355%
Kappa Coefficient = 0.8581
Year-to-Year Land Change|Producer Accuracy (%)|User Accuracy (%)|Producer Accuracy (Pixel Ratio)|User Accuracy (Pixel Ratio)
2005-2010|86.31|89.81|692981/802917|692981/771588|
2000-2005|87.99|88.47|657743/747512|657743/743503|
1995-2000|92.41|90.00|1161119/1256430|1161119/1290199|
1990-1995|86.28|86.92|478064/554110|478064/549996|
1985-1990|88.80|87.99|547535/616569|547535/622252|
Process steps performed in ENVI 5.4 to produce this land disturbance classification dataset:
1. Pre-release versions of annual Landsat Best Available Pixel Composite (LBAPC) data from 1984 to 2012 with 30 m spatial resolution were obtained from the Natural Resources Canada/Pacific Forestry Centre for testing purposes. Of these, the 1985, 1990, 1995, 2000, 2005 to 2010 data were used as inputs to produce the time-series Land Use and Land Cover (LULC) classification stack. In addition, 2015 LBAPC data were produced in-house using the Google Earth Engine.
2. Normalized Difference Built-up Index (NDBI) imageries were produced from 1985 to 2015 LBAPC datasets with a 5-year gap.
3. Change detection results were produced by subtracting initial state NDBI (e.g., 1985) from the final state NDBI (e.g., 1990) for every 5-year gap from 1985 to 2015. The change detection result with NDBI difference from 0.2 to 1 was assigned as land disturbance.
4. A land disturbance map was produced with disturbances classified based on the time interval within which they first appeared on the landscape, i.e., in chronological order from the earliest to the latest: 1985-1990, 1990-1995, 1995-2000, 2000-2005, 2005-2010, 2010-2015.
5. The disturbances were attributed to one of four major disturbance types (i.e., energy footprints, non-energy footprints, cutblocks, or wildfire) based on the information from ancillary geospatial datasets: Digital Integrated Dispositions (DIDs), wildfire, and forestry data from the Alberta Agriculture and Forestry and Alberta Environment and Parks.
The process steps were adapted from the following journal paper.
Chowdhury, S., Chao, D.K., Shipman, T.C. and Wulder, M.A. (2017): Utilization of Landsat data to quantify land-use and land-cover changes related to oil and gas activities in West-Central Alberta from 2005 to 2013; GIScience and Remote Sensing, v. 54, no. 5, p.700–720, at http://dx.doi.org/10.1080/15481603.2017.1317453.