<p><strong>Background:</strong> Detailed geomorphological analysis of the karst depressions in Yucatán has received little attention because the measurement of morphometric parameters taken in the field involves a lot of work, time and costs. A pioneering exercise is presented that arose with two questions. What would be the characteristics of the relief that can be observed and / or measured in a cenote through images acquired with a drone? Would they be features similar to those seen with Google Earth images? <strong>Objective:</strong> To identify the units of the relief and morphometry of a cenote using images from two different platforms. <strong>Methodology:</strong> An open cenote located in the municipality of Chapab, Yucatán, was studied. A flight was carried out with a drone obtaining 259 images in 14 minutes, with which an orthomosaic, a digital elevation model and a point cloud were generated. We proceeded to the analysis of the data of the visible range (RGB), work with filters in ArcMap; the same spatial analysis procedure was performed with an image from Google Earth. <strong>Results:</strong> Nine units of the relief were identified in detail (permanent lake, intermittent lake, an area subject to flooding, beach, mouth, flooded slope, upper slope, scarp and lake coast); the differences between inputs are mainly in the morphometric parameters and in the values of the elevations. <strong>Implications:</strong> Drones facilitate imaging that allows detailed characterization of karst depressions to represent attributes that cannot be expressed on small scales. <strong>Conclusion</strong>: The anatomy of the Polol cenote technically corresponds to a sinkhole that houses a karst lake; nine relief units were identified that reveal limnological processes generating a karst-lacustrine environment that leads to the geomorphological enrichment of this region.</p>

<p><strong>Background</strong>. The Milpa is one of the traditional agricultural systems of Yucatan, Mexico; it is implemented by using the agricultural procedure called “Slash &amp; Burn”. Quality and quantity of forest fuel (<em>i.e.</em> biomass) are two of the main factors related to burn severity. Burning affects soil properties related to fertility and crop production. The use of new approaches of remote sensing technologies such as Unattended Aerial Vehicles (UAVs), can allow studying the importance of fire in agriculture to improve productivity in slash &amp; burn agricultural systems. <strong>Objective</strong>. Analyze the land covers and its influence on the severity of the agricultural burning in a "Milpa" agroecosystem with multi-spectral images acquired by UAVs. <strong>Methodology</strong>. The study site was located in the municipality of Tzucacab, Yucatan, Mexico. Two plots were selected [10-15 and 20-25 years of fallow]; land cover was characterized before and after slash &amp; burn. Three multispectral sensors [Red, Green, Blue (RGB); Near Infrared (NIR) and; Thermal Infra-Red (TIR)] were mounted on UAVs, to obtained multispectral imagery and generate orthomosaics for later analyses. <strong>Results</strong>. With the imagery, the Normalized Difference Vegetation Index [NDVI] was calculated and its spectral behavior evaluated. The imagery was used to analyze the fire intensity. On RGB imagery, patterns of areas with greater dry biomass cover associated to high burn severity and, areas with green vegetation or naked soil associated to low burn severity were observed. Land covers with high fuel potential showed low NDVI index values. <strong>Implications.</strong> The analyses of the multispectral imagery taken by drones allow the quick evaluation of the land covers and the intensity of agricultural fires, with the pertinent adjustments, in the near future this could become a standard methodology to accomplish this kind of evaluations. <strong>Conclusions</strong>. This approach allowed to analyze the state of land covers to visually assess the quality of fuel and its influence on the intensity of an agricultural fire. The RGB and NIR imagery obtained by UAVs can be a good tool to predict the intensity of an agricultural fire, TIR imagery could be used to find mathematical relations between land covers and fire intensity.</p>

<p><strong>Background</strong>. The rapid technological development of drones has been used in different disciplines as a new tool for the collection of geospatial data. Depending on the application of geospatial data, users may not require high absolute precision and therefore the measurement error within the model (relative precision) may be more important. <strong>Objective</strong>. To determine the root mean square error and the relative precision of the orthomosaics and digital elevation models at different heights generated with the SfM photogrammetric technique and a drone, over a sinkhole located in the Cenote Ring in the state of Yucatán. <strong>Methodology</strong>. Aerial images were acquired with a DJI Phantom 4 drone over a sinkhole at two heights, 80 and 100 m. Subsequently, the aerial images were processed obtaining digital elevation models (DEM) and orthomosaic with which the root mean squared error (RMSE) of the UTM coordinates (x, y) and elevations (z) was calculated. Finally, the relative precision was calculated by comparing the measurements in the field with those obtained in the DEMs and orthomosaics. <strong>Results</strong>. Flights at 100 m altitude showed the least variation in coordinates and elevations compared to flights at 80 m. The highest relative precision was recorded at 100 m high in the orthomosaics and ranged from 0.03 to 0.36 m with an average value of 0.22 m. <strong>Implications</strong>. With these results we can affirm that it is possible to carry out studies without control points in applications where a consistent and centimeter precision is not required. <strong>Conclusion</strong>. The 100 m high flights had the lowest RMSE and the highest relative accuracy.</p>