We presented a methodology to accurately classify mountainous regions in the tropics. These landscapes are complex in terms of their geology, ecosystems, climate and land use. Obtaining accurate maps to assess land cover change is essential. The objectives of this study were to (1) map vegetation using the Random Forest Classifier (RFC), spectral vegetation index (SVI), and ancillar geographic data (2) identify important variables that help differentiate vegetation cover, and (3) assess the accuracy of the vegetation cover classification in hard-to-reach Ecuadorian mountain region. We used Landsat 7 ETM+ satellite images of the entire scene, a RFC algorithm, and stratified random sampling. The altitude and the two band enhanced vegetation index (EVI2) provide more information on vegetation cover than the traditional and often use normalized difference vegetation index (NDVI) in other settings. We classified the vegetation cover of mountainous areas within the 1016 km² area of study, at 30 m spatial resolution, using RFC that yielded a land cover map with an overall accuracy of 95%. The user´s accuracy and the half-width of the confidence interval for 95% of the basic map units, forest (FOR), páramo (PAR), crop (CRO) and pasture (PAS) were 95.85% ± 2.86%, 97.64% ± 1.24%, 91.53% ± 3.35% and 82.82% ± 7.74%, respectively. The overall disagreement was 4.47%, which results from adding 0.43% of quantity disagreement and 4.04% of allocation disagreement. The methodological framework presented in this paper and the combined use of SVIs, ancillary geographic data, and the RFC allowed the accurate mapping of hard-to-reach mountain landscapes as well as uncovering the underlying factors that help differentiate vegetation cover in the Ecuadorian mountain geosystem.

Microbial fuel cells (MFCs) are bio-electrochemical devices used to generate bioelectricity from a wide range of substrates by using bioelectrogenic microorganisms, and have special interest to supply the energy demands for small devices and the same time as a renewable energy source. In this study, a blend of soil-organic matter were tested in a membrane-less single chamber microbial fuel cells (SMFC) devices to generate bioelectricity along with biotransformation of organic wastes, over a testing time of 60 days and without renovation of substrate in batch mode of operation. The results in the experiments have shown relationship among substrate and the performance of output voltage in the SMFCs, the highest value of output voltage was 330mV in the SMFC3 which contained a blend of 75:25 of fruits and vegetables

Microbial fuel cells represent an innovative technology which allow simultaneous waste treatment, electricity production, and environmental monitoring. This study provides a preliminary investigation of the use of terrestrial Single chamber Microbial Fuel Cells (SMFCs) as biosensors. Three cells were created using Andean soil, each one for monitoring a BOD concentration of synthetic washed rice wastewater (SRWW) of 10, 100, and 200 mg/L for SMFC1, SMFC2 and SMFC3, respectively. The results showed transient, exponential, and steady stages in the SMFCs. The maximum open circuit voltage (OCV) peaks were reached during the elapsed time of the transient stages, according to the tested BOD concentrations. A good linearity between OCV and time was observed in the increasing stage. The average OCV in this stage increased independently of the tested concentrations. SMFC1 required less time than SMFC2 to reach the steady stage, suggesting the BOD concentration is an influencing factor in SMFCs, and SMFC3 did not reach it. The OCV ratios were between 40.6–58.8 mV and 18.2–32.9 mV for SMFC1 and SMFC2. The reproducibility of the SMFCs was observed in four and three cycles for SMFC1 and SMFC2, respectively. The presented SMFCs had a good response and reproducibility as biosensor devices, and could be an alternative for environmental monitoring.