Background and aims: The quantitative retrieval of soil organic carbon (SOC) storage, particularly for soils with a large potential for carbon sequestration, is of global interest due to its link with the carbon cycle and the mitigation of climate change. However, complex ecosystems with good soil qualities for SOC storage are poorly studied. Methods: The interrelation between SOC and various vegetation remote sensing drivers is understood to demonstrate the link between the carbon stored in the vegetation layer and SOC of the top soil layers. Based on the mapping of SOC in two horizons (0–30 cm and 30–60 cm) we predict SOC with high accuracy in the complex and mountainous heterogeneous páramo system in Ecuador. A large SOC database (in weight % and in Mg/ha) of 493 and 494 SOC sampling data points from 0–30 cm and 30–60 cm soil profiles, respectively, were used to calibrate GPR models using Sentinel-2 and GIS predictors (i.e., Temperature, Elevation, Soil Taxonomy, Geological Unit, Slope Length and Steepness (LS Factor), Orientation and Precipitation). Results: In the 0–30 cm soil profile, the models achieved a R² of 0.85 (SOC%) and a R² of 0.79 (SOC Mg/ha). In the 30–60 cm soil profile, models achieved a R² of 0.86 (SOC%), and a R² of 0.79 (SOC Mg/ha). Conclusions: The used Sentinel-2 variables (FVC, CWC, LCC/C_ab, band 5 (705 nm) and SeLI index) were able to improve the estimation accuracy between 3–21% compared to previous results of the same study area. CWC emerged as the most relevant biophysical variable for SOC prediction.

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.

The páramo is home to a significant proportion of global biodiversity and provides essential services for the development of life for millions of people in Ecuador. However, land use/land cover (LULC) changes threaten biodiversity and modify its functioning. The objectives of this study were: (1) to evaluate the conservation status of the herbaceous páramo (HP) ecosystem by analyzing its LULC in Ecuador’s southern region. (2) to identify possible regions where the native páramo ecosystem is being restored. We analyzed Landsat 8 images using Object-Based Image Analysis (OBIA) and a Classifier Decision Tree (CDT) to achieve these objectives. The results show that the native herbaceous páramo (NHP) ecosystem is being transformed into an anthropogenic HP (AHP). The area covered by the NHP ecosystem (296,964 ha) has been reduced by 50% (149,834 ha). Nevertheless, we identified five regions where the NHP is upgrading. These regions are relevant for studying NHP regeneration in Ecuador’s southern region, where soils are mostly andosols. The LU of the páramo, with cycles of exploitation, abandonment, and regeneration in a secondary páramo, is transforming the NHP ecosystem. These exploitation practices, global climate change, and lack of knowledge about the NHP ecosystem’s regeneration and its soils’ recovery threaten to substantially reduce the NHP area, its functionality, and its ecosystem services.

Background: Soil organic carbon (SOC) affects essential biological, biochemical, and physical soil functions such as nutrient cycling, water retention, water distribution, and soil structure stability. The Andean páramo known as such a high carbon and water storage capacity ecosystem is a complex, heterogeneous and remote ecosystem complicating field studies to collect SOC data. Here, we propose a multi-predictor remote quantification of SOC using Random Forest Regression to map SOC stock in the herbaceous páramo of the Chimborazo province, Ecuador.Results: Spectral indices derived from the Landsat-8 (L8) sensors, OLI and TIRS, topographic, geological, soil taxonomy and climate variables were used in combination with 500 in situ SOC sampling data for training and calibrating a suitable predictive SOC model. The final predictive model selected uses nine predictors with a RMSE of 1.72% and a R²of 0.82 for SOC expressed in weight %, a RMSE of 25.8 Mg/ha and a R²of 0.77 for the model in units of Mg/ha. Satellite-derived indices such as VARIG, SLP, NDVI, NDWI, SAVI, EVI2, WDRVI, NDSI, NDMI, NBR and NBR2 were not found to be strong SOC predictors. Relevant predictors instead were in order of importance: geological unit, soil taxonomy, precipitation, elevation, orientation, slope length and steepness (LS Factor), Bare Soil Index (BI), average annual temperature and TOA Brightness Temperature.Conclusions: Variables such as the BI index derived from satellite images and the LS factor from the DEM increase the SOC mapping accuracy. The mapping results show that over 57% of the study area contains high concentrations of SOC, between 150 and 205 Mg/ha, positioning the herbaceous páramo as an ecosystem of global importance. The results obtained with this study can be used to extent the SOC mapping in the whole herbaceous ecosystem of Ecuador offering an efficient and accurate methodology without the need for intensive in situ sampling.

One of the main challenges for environmental sciences today is the effective treatment of dye-laden industrial effluents. This work aimed to study the potential of an untreated (natural occurring clayey composite) red clay (RC) for the adsorption of a cationic dye Basic Navy Blue 2RN (CNB) and anionic dye Drimaren Yellow CL-2R (ADY). We evaluated the effect of pH, dye concentration, and adsorbent concentration on the removal effectiveness to study the absorption process. Also, we studied the adsorption process by analyzing the feasibility of several known adsorption isotherms and kinetic models. The results show that at a pH of less than 4, the CNB and ADY removal percentages were 97% and 96%, respectively. At a pH greater than 8, the CNB and ADY removals were 75% and 25%, respectively. The CNB adsorption happened by chemisorption of a monolayer on iron-containing particles (IPs). In congtrast, the ADY adsorption occurred by monolayer physisorption on kaolinite particles (KPs) and Na, K-rich Laumontite particles (LPs). The Langmuir isotherm model fits very well with CNB experimental data. The Temkin model shows the best fit between the isotherm function and the ADY dye-adsorption data. The pseudo-second-order kinetic model fits the CNB and ADY dye-adsorption data on RC particles. The heterogeneous composition of naturally occurring clay favors different adsorption mechanisms and opens an avenue for the separation process’s engineering.