Fernando Oñate-Valdivieso

Modification of the random forest algorithm to avoid statistical dependence problems when classifying remote sensing imagery

Abstract Random forest is a classification technique widely used in remote sensing. One of its advantages is that it produces an estimation of classification accuracy based on the so called out-of-bag cross-validation method. It is usually assumed that such estimation is not biased and may be used instead of validation based on an external data-set or a cross-validation external to the algorithm. In this paper we show that this is not necessarily the case when classifying remote sensing imagery using training areas with several pixels or objects. According to our results, out-of-bag cross-validation clearly overestimates accuracy, both overall and per class. The reason is that, in a training patch, pixels or objects are not independent (from a statistical point of view) of each other; however, they are split by bootstrapping into in-bag and out-of-bag as if they were really independent. We believe that putting whole patch, rather than pixels/objects, in one or the other set would produce a less biased out-of-bag cross-validation. To deal with the problem, we propose a modification of the random forest algorithm to split training patches instead of the pixels (or objects) that compose them. This modified algorithm does not overestimate accuracy and has no lower predictive capability than the original. When its results are validated with an external data-set, the accuracy is not different from that obtained with the original algorithm. We analysed three remote sensing images with different classification approaches (pixel and object based); in the three cases reported, the modification we propose produces a less biased accuracy estimation.

RadarNet-Sur First Weather Radar Network in Tropical High Mountains

AbstractWeather radar networks are indispensable tools for forecasting and disaster prevention in industrialized countries. However, they are far less common in the countries of South America, which frequently suffer from an underdeveloped network of meteorological stations. To address this problem in southern Ecuador, this article presents a novel radar network using cost-effective, single-polarization, X-band technology: the RadarNet-Sur. The RadarNet-Sur network is based on three scanning X-band weather radar units that cover approximately 87,000 km2 of southern Ecuador. Several instruments, including five optical disdrometers and two vertically aligned K-band Doppler radar profilers, are used to properly (inter) calibrate the radars. Radar signal processing is a major issue in the high mountains of Ecuador because cost-effective radar technologies typically lack Doppler capabilities. Thus, special procedures were developed for clutter detection and beam blockage correction by integrating ground-based ...

Temporal and spatial analysis of precipitation patterns in an Andean region of southern Ecuador using LAWR weather radar

This paper focuses on the analysis of precipitation patterns, using a Local Area Weather Radar to collect information about the precipitation distribution in an Andean region of southern Ecuador (cities of Loja, Zamora and Catamayo). 54 representative events were selected to develop daily precipitation maps and to obtain their relevant characteristics, which were related to the topography and the season. The results showed that a strong correlation between the areas covered by precipitation (RA coefficient) and the season exists. In general, humid air masses come from the east (Amazon Basin), but during the main rainy season (December to April), humidity also frequently enters the study region from the west (Pacific Ocean). The rainy season is characterized by convective precipitation, associated with higher evaporation rates during austral summer. The relatively dry season is formed between May and November, but considerable precipitation amounts are registered, too, due to advective moisture transport from the Amazon Basin, a result of the predominant tropical easterlies carrying the humidity up the eastern escarpment of the Andes, generally following the natural course of the drainage systems.