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The predominant aim of the current study was to evaluate the spatial dynamics of the riparian coverage of the area of influence of the Chambo River in the area of the river's source (middle-high basin), between 2500 and 3000 m.a.s.l. For its execution, Landsat 7 images from the year 2000, RapidEye from the year 2009, and Spot 6 from the year 2019 were used in the time range of 2000–2009 and 2009–2019. These were subjected to supervised classification by applying the maximum likelihood algorithm, identifying five classes of soil cover, being pasture, crops, soil-remnants of paramo, forest, and anthropic. The classification results were validated by calculating the precision measures and the kappa index. With the use of cross-tabulation matrices, the gains, losses, and persistence in the two periods studied were identified. There, it was determined that, in the first study period, the soil cover-paramo remnants presented the highest percentage of loss (26.70%), the crop cover the highest percentage of gain (28.91%), and in the second period, the crop class presented the highest percentages of losses (18.94%) and gains (17.29%). The cartographic projection of the area for the year 2030 predicts that the areas anthropic category will increase by 1.27%, that of forest will decrease by 1.19%, that of soil-remnants of paramo will gain 0.79%, and crop and pasture cover will decrease by 0.45% and 0.43%, respectively. The results obtained allow for the transitions between coverages to be attributed to population growth, afforestation, reforestation, deforestation and agricultural activities, volcanic eruptions, land colonization, and expansion of agricultural activity. Complementary studies are recommended that involve livelihoods and water quality, which facilitate the identification of vulnerable areas to propose adaptation, prevention, and/or restoration measures. [ABSTRACT FROM AUTHOR]

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Tropical forests represent half of the Earth's remaining forest area, but they are shrinking at high rates, which poses a threat to their multiple ecosystem services. As a response, international environmental agreements and related programs require information about tropical forested landscapes. Despite the increasing quantity and quality of remote sensing-based data, the effective monitoring of forests in the tropics still faces operational challenges: (a) applicability at local levels, with lack of reference or cloud-free information; (b) overcoming geographical, ecological, or biophysical variability; (c): stratification, distinguishing forest categories related to functionality and disturbance history. We conducted an extensive ground verification campaign through 36 landscapes in 9 regions of Zambia, Ecuador and Philippines, which constitute a gradient of pantropical deforestation contexts or forest transitions. We collected over 16,000 ground control points and digitized over 18,000 ha with details on land use and forest disturbance history. We trained a random forest algorithm and generated high-resolution (30 m) binary forest maps covering ~15 Mha, building on 39 optical (Landsat-8), radar (Sentinel-1) and elevation bands, indices and textures. We validated the quality of the outputs across the studied deforestation gradient and compared them to (a): 3 national land cover maps used for international reporting, (b): 4 global forest datasets (Global Forest Change, Copernicus Land Cover, JAXA and TanDEM-X Forest/Non-Forest). Our method generated highly accurate (92%) forest maps for the studied regions when compared to the global datasets, which generally overestimated forest cover. We achieved accuracies similar to the national maps, following a standardized method for all countries. The difficulties in delineating forest increased in more advanced stages of deforestation, with recurring struggles to distinguish non-forest tree-based systems (e.g. perennials, palms, or agroforestry), shrublands and grasslands. Regrowth forests were repeatedly misclassified across contexts, countries and datasets, in contrast to reference or degraded forests. Our results highlight the importance of in situ verification as accompanying method to establish efficient forest monitoring systems, especially in areas with higher rates of forest cover change and in tropical regions of advanced deforestation or early reforestation stages. These are precisely the areas where current REDD+ or Forest Landscape Restoration initiatives take place. • Pantropical ground verification with details on land use and forest disturbance. • Multi-sensor mapping and comparison of seven national/global forest datasets. • Our maps had better accuracies than the secondary, which mostly overestimated FC. • Lower accuracies and worse results found in advanced deforestation contexts. • Regrowth forests recurrently misclassified across contexts, countries and datasets. [ABSTRACT FROM AUTHOR]

This work presents a wireless sensor network (WSN) system able to determine the water quality of rivers. Particularly, we consider the Tahuando River from Ibarra, Ecuador, as a case study. The main goal of this research is to determine the river's status throughout its route, by generating data reports into an interactive user interface. To this end, we use an array of sensors collecting several measures such as: turbidity, temperature, water quality, pH, and temperature. Subsequently, from the information collected on an Internet-of-Things (IoT) server, we develop a data analysis scheme with both data representation and supervised classification. As an important result, our system outputs a map that shows the contamination levels of the river at different regions. Furthermore, in terms of data analysis performance, the proposed system reduces the data matrix by 97% from its original size, while it reaches a classification performance over 90%. Furthermore, as an additional remarkable result, we here introduce the so-called quantitative metric of balance (QMB), which measures the balance or ratio between performance and power consumption. [ABSTRACT FROM AUTHOR]