The Implications of Fire Management in the Andean Paramo: A Preliminary Assessment Using Satellite Remote Sensing

Abstract

Abstract:The upper ranges of the northern Andes are characterized by unique Neotropical, high altitude ecosystems known as paramos. These tundra-like grasslands are widely recognized by the scientific community for their biodiversity and their important ecosystem services for the local human population. Despite their remoteness, limited accessibility for humans and waterlogged soils, paramos are highly flammable ecosystems. They are constantly under the influence of seasonal biomass burning mostly caused by humans. Nevertheless, little is known about the spatial extent of these fires, their regime and the resulting ecological impacts. This paper presents a thorough mapping and analysis of the fires in one of the world's largest paramo, namely the "Complejo logistic regression model analysis was undertaken to test the hypothesis that the dynamics of the paramo fires can be related to human pressures. The resulting map shows that the burned paramo areas account for 57,179.8 hectares, of which 50% (28,604.3 hectares) are located within the Sumapaz National Park. The findings show that the fire season mainly occurs from January to March. The accuracy assessment carried out using a confusion matrix based on 20 reference burned areas shows values of 90.1% (producer accuracy) for the mapped burned areas with a Kappa Index of Agreement (KIA) of 0.746. The results of the logistic regression model suggest a significant predictive relevance of the variables road distance (0.55 ROC (receiver operating characteristic)) and slope gradient (0.53 ROC), indicating that the higher the probability of fire occurrence, the smaller the distance to the road and the higher the probability of more gentle slopes. The paper sheds light on fires in the Colombian paramos and provides a solid basis for further investigation of the impacts on the natural ecosystem functions and biodiversity.

Clarifying regional hydrologic controls of the Marañón River, Peru through rapid assessment to inform system-wide basin planning approaches

“…Punas and puna-like ecosystems called páramo modulate the release of water received during extreme weather patterns (prolonged wet periods) through significant soil storage capacity, releasing water gradually during dry seasons to provide sustained baseflow to rivers (Balslev and Luteyn, 1992;Buytaert et al, 2007). Puna water storage functions are well known to provide large volume water storage as ecosystem services, including serving the majority of municipal water supplies to major centers including Quito and Bogata (Borrelli et al, 2015;Buytaert et al, 2006). Indeed, basins with water reservoirs have been shown to produce substantially more runoff than basins without these ecosystems at least partially due to low levels of evapotranspiration and the high storage capacity afforded by highly organic soils (Buytaert et al, 2007(Buytaert et al, , 2006.…”

Assessment of Land Cover Changes in the Hinterland of Barranquilla (Colombia) Using Landsat Imagery and Logistic Regression

“…We created 700 random points stratified for each class in the land use and land cover map 2017 for the accuracy assessment. Verification of ground truth classes of all time steps was done by visual interpretation of the respective satellite images complemented by Google Earth and Bing high-resolution imagery (acquired via Web Map Service, WMS) [27,57,58].…”

“…When connected with GIS, binomial logistic regression is an appropriate tool for explanatory analysis of the factors of land cover changes [31,57,60]. Accordingly, we used logistic regression models to identify drivers and determining factors leading to vegetation cover changes.…”

Desertification Susceptibility Mapping Using Logistic Regression Analysis in the Djelfa Area, Algeria

“…The curve is a two-dimensional plot where the y-axis represents the probability of a correctly predicted response to an event in terms of sensitivity or true positive rate, and the x-axis represents the probability of an incorrect predicted response to an event, in terms of specificity or false positive rate [40]. If all desertified areas are correctly predicted, the area under the curve will equal 1, but in general an area under the curve greater than 0.5 is acceptable [110][111][112][113].…”