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Statistical method for determining the distribution of surfaces by type of coverage on large urbanized drainage basins

Zhuk V.

Instal

2018

nr 3

45--48

An important factor that affects the quantitative and qualitative parameters of stormwater runoff is the distribution of the catchment’s surface according to the type of land cover. In order to optimize the solution of this problem, for especially large urbanized catchments, an improved step-by-step method of simple statistical sampling is proposed. The method is applied to the part of the territory of the Lviv city belonging to the Baltic Sea basin. With a 95% confidence interval and an error of 3%, the averaged value of impervious surfaces is 0.370.

Jednym z najważniejszych czynników wpływających na parametry ilościowe i jakościowe wody deszczowej jest rozdzielenie powierzchni zlewni w zależności od rodzaju pokrycia (uszczelnienia) terenu. W celu zoptymalizowania rozwiązania tego problemu dla dużych terenów zurbanizowanych zaproponowano ulepszoną metodę prostego próbkowania statystycznego. Metoda została zastosowana na części obszaru miasta Lwowa należącego do zlewni Morza Bałtyckiego. Z 95% przedziałem ufności i błędem 3% uzyskano uśredniony ułamek powierzchni nieprzepuszczalnych równy 0.370.

Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping

Drzewiecki W.

Geodesy and Cartography

2017

Vol. 66, no. 2

171--209

We evaluated the performance of nine machine learning regression algorithms and their ensembles for sub-pixel estimation of impervious areas coverages from Landsat imagery. The accuracy of imperviousness mapping in individual time points was assessed based on RMSE, MAE and R2. These measures were also used for the assessment of imperviousness change intensity estimations. The applicability for detection of relevant changes in impervious areas coverages at sub-pixel level was evaluated using overall accuracy, F-measure and ROC Area Under Curve. The results proved that Cubist algorithm may be advised for Landsat-based mapping of imperviousness for single dates. Stochastic gradient boosting of regression trees (GBM) may be also considered for this purpose. However, Random Forest algorithm is endorsed for both imperviousness change detection and mapping of its intensity. In all applications the heterogeneous model ensembles performed at least as well as the best individual models or better. They may be recommended for improving the quality of sub-pixel imperviousness and imperviousness change mapping. The study revealed also limitations of the investigated methodology for detection of subtle changes of imperviousness inside the pixel. None of the tested approaches was able to reliably classify changed and non-changed pixels if the relevant change threshold was set as one or three percent. Also for fi ve percent change threshold most of algorithms did not ensure that the accuracy of change map is higher than the accuracy of random classifi er. For the threshold of relevant change set as ten percent all approaches performed satisfactory.