#Stratified random drivers#

Here the constant factor is the proportion ration for each population subset. Now, to make it proportionate, the researcher uses one specific fraction or a percentage to be applied on its subgroups of population. For example, you have three sub-groups with a population size of 150, 200, 250 subjects in each subgroup respectively. In the proportionate random sampling, each stratum would have the same sampling fraction. There are two types of stratified sampling – one is proportionate stratified random sampling and another is disproportionate stratified random sampling. The researcher can represent even the smallest sub-group in the population. Stratified sampling is used when the researcher wants to understand the existing relationship between two groups. The population is divided into various subgroups such as age, gender, nationality, job profile, educational level etc. While using stratified sampling, the researcher should use simple probability sampling. The strata or sub-groups should be different and the data should not overlap. After dividing the population into strata, the researcher randomly selects the sample proportionally.ĭescription: Stratified sampling is a common sampling technique used by researchers when trying to draw conclusions from different sub-groups or strata. The strata is formed based on some common characteristics in the population data. Photogrammetric Engineering & Remote Sensing, 72, 1137–1146.Definition: Stratified sampling is a type of sampling method in which the total population is divided into smaller groups or strata to complete the sampling process. Landsat-7 long-term acquisition plan: development and validation. Proceedings of the National Academy of Sciences, 110(16), 6442–6447.Īrvidson T, Goward SN, Gasch J, & Williams D (2006). Defining pyromes and global syndromes of fire regimes.

International Journal of Wildland Fire, 19, 861–878.Īrchibald S, Lehmann CE, Gómez-Dans JL, & Bradstock RA (2013). Southern African fire regimes as revealed by remote sensing.

#Stratified random drivers#

Remote Sensing of Environment, 163, 140–152.Īrchibald S, Roy DP, Van Wilgen BW, and Scholes RJ 2009, What Limits Fire?: An examination of drivers of burnt area in sub-equatorial Africa, Global Change Biology special issue on Fire Ecology and Climate Change, 15, 613–630, 10.1111/j.Īrchibald S, Scholes RJ, Roy DP, Roberts G, & Boschetti L (2010). Global Burned Area Mapping from ENVISAT-MERIS and MODIS active fire data. By incorporating probability sampling in both the spatial and temporal domains, the present study establishes the foundation for rigorous design-based validation of global burned area products and, more generally, of terrestrial thematic products that have high temporal variability.Īccuracy Fire Global Burned Area Landsat MODI Probability Sampling Validation.Īlonso-Canas I, & Chuvieco E (2015). Globally, the standard errors were reduced by 63%, 54%, 22% and 53% for overall accuracy, omission error, commission error and total burned area estimates respectively. Using the global MODIS burned area product to establish a hypothetical population of reference data, we show that a sampling scheme based on the proposed stratification with equal sample allocation among strata is effective in reducing the standard errors of accuracy and area estimators compared to simple random sampling. To illustrate this, we use a stratification methodology based on the Olson global ecoregion map and on the MODIS global active fire product. The TSA grid is combined in time with the 16-day Landsat acquisition calendar to provide three-dimensional elements (voxels).This allows for implementation of stratified random sampling designs, where not only the location but also the time interval of the independent reference data is explicitly drawn by probability sampling. To sample the globe in the spatial domain with non-overlapping sampling units, the Thiessen Scene Area (TSA) tessellation of the Landsat path/row geometry is used. This paper presents a three-dimensional sampling grid that allows for probability sampling of Landsat data in both space and time. The majority of global burned area validation exercises use Landsat data to derive the independent reference data. Such an assessment can be achieved by implementing validation methods employing design-based inference in which the independent reference data are selected via a probability sampling design. The potential research, policy and management applications of global burned area products place a high priority on rigorous, quantitative assessment of their accuracy.