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Paramos, unique and biodiverse ecosystems found solely in the high mountain regions of the tropics, are under threat. Despite their crucial role as primary water sources and significant carbon repositories in Colombia, they are deteriorating rapidly and garner less attention than other vulnerable ecosystems like the Amazon rainforest. Their fertile soil and unique climate make them prime locations for agriculture and cattle grazing, often coinciding with economically critical deposits such as coal which has led to a steady decline in paramo area. Anthropic impact was evaluated using multispectral images from Landsat and Sentinel over 37 years, on the Guerrero and Rabanal paramos in central Colombia which have experienced rapid expansion of mining and agriculture. Our analysis revealed that since 1984, the Rabanal and Guerrero paramos have lost 47.96% and 59.96% of their native vegetation respectively, replaced primarily by crops, pastures, and planted forests. We detected alterations in the spectral signatures of native vegetation near coal coking ovens, indicating a deterioration of paramo health and potential impact on ecosystem services. Consequently, human activity is reducing the extent of paramos and their efficiency as water sources and carbon sinks, potentially leading to severe regional and even global consequences. [ABSTRACT FROM AUTHOR]

Remote sensing technologies have been successfully used for deforestation monitoring, and with the wide availability of satellite products from different platforms, forest monitoring applications have grown in recent years. The observed potential in these technologies motivates the development of forest mapping and monitoring tools that could also be used for neighboring applications like agriculture or land-use mapping. A literature review confirmed the research areas of interest in deforestation monitoring using synthetic aperture radar (SAR) and data fusion techniques, which guided the formulation of the method developed in this article consisting of a data preprocessing workflow for SAR (Sentinel-1) and multispectral (Sentinel-2) data and a procedure for the selection of a machine learning model for forest/nonforest segmentation evaluated in different combinations of Sentinel-1 and Sentinel-2 bands. The selected model is a random forest algorithm that uses C-band SAR dual-polarimetric bands, intensity features, and vegetation indices derived from optical/multispectral data. The selected random forest classifier's balanced accuracies were 79–81%, and the f1-scores were 0.72–0.76 for the validation set. The results allow the obtention of yearly forest/nonforest and forest loss maps in the study area of Bajo Cauca in Colombia, a region with a documented high deforestation rate. [ABSTRACT FROM AUTHOR]

Accurate estimations of aboveground biomass (AGB) in tropical forests are crucial for maintaining carbon stocks and ensuring effective forest management. By combining remote sensing (RS) data with ensemble algorithms, reliable AGB estimates in forests can be obtained. In this context, the freely available Sentinel-1 (S-1 SAR) and Sentinel 2 multispectral imagery (S-2 MSI) data have a significant role in enhancing accurate AGB estimations at a lower cost, which is relevant for the tropical dry forest (TDF) regions where AGB estimation is uncertain or there is a lack of comprehensive exploration. This study aims to address this gap by presenting a cost-effective and reliable AGB estimation approach in the TDF region of Colombia. For this purpose, we modeled and compared the performance of two ensemble algorithms, random forest (RF) and extreme gradient boosting (XGBoost), to estimate AGB using three predictor categories (polarizations/textures, spectral bands/vegetation indices, and a combination of both). We then examined the modeling potential of S-1 SAR and S-2 MSI imagery in predicting forest AGB and subsequently identified the most suitable variables. To construct AGB models' field data, we employed a clustered distributed sampling approach involving 100 subsample plots, each with an area of 400 m2. Stepwise multiple linear regression was applied to identify suitable predictors from the original satellite bands, vegetation indices, and texture metrics. To produce a map of AGB, predicted AGB values were calculated for every pixel within a specific satellite subscene using the most effective ensemble algorithm. Our study findings show that the RF model, which employed combined predictor sets, displayed superior performance when evaluated against the independent validation set. The RF model successfully estimated AGB with a high degree of accuracy, achieving an R2 value of 0.78 and an RMSE value of 42.25 Mg/ha−1. In contrast, the XGBoost model performed less accurately, obtaining an R2 value of only 0.60 and an RMSE value of 48.41 Mg/ha−1. The results also indicate that S-2 vegetation indices data were more appropriate for this purpose than S-1 texture data. Despite this, S-1 cross-polarized textures were necessary during the dry season for the combined datasets. The top predictive variables for S-2 images were cab and cw, as well as red-edge bands during the wet season. As for S-1 images, texture D_VH _Hom during the dry season was the most important variable for explaining performance. Overall, the proposed approach of using freely available Sentinel data seems to improve the accuracy of AGB estimation in heterogeneous forest cover and, as such, they should be recommended as a data source for forest AGB assessment. [ABSTRACT FROM AUTHOR]

The Caribbean is one of the world's most vulnerable regions to the projected impacts of climate change, and changes in coral reef ecosystems have been studied over the last two decades. Lately, new technology-based methods using satellites and unmanned vehicles, among others have emerged as tools to aid the governance of these ecosystems by providing managers with high-quality data for decision-making processes. This paper addresses the development of a Google Earth Engine (GEE)-based application for use in the management processes of shallow coral reef ecosystems, using images acquired with Remotely Piloted Aircraft Systems (RPAS) known as drones, at the Old Providence McBean Lagoon National Natural Park; a Marine Protected Area (MPA) located northwest of Old Providence Island, Colombia. Image acquisition and processing, known as drone imagery, is first described for flights performed using an RTK multispectral drone at five different monitoring stations within the MPA. Then, the use of the GEE app is described and illustrated. The user executes four simple steps starting with the selection of the orthomosaics uploaded to GEE and obtaining the reef habitat classification for four categories: coral, macroalgae, sand, and rubble, at any of the five monitoring stations. Results show that these classes can be effectively mapped using different machine-learning (ML) algorithms available inside GEE, helping the manager obtain high-quality information about the reef. This remote-sensing application represents an easy-to-use tool for managers that can be integrated into modern ecosystem monitoring protocols, supporting effective reef governance within a digitized society with more demanding stakeholders. [ABSTRACT FROM AUTHOR]

The article presents the critical design of the CubeSat for the FACSAT-2 (SAT-CHIRIBIQUETE) space mission for the georeferenced observation and analysis of the Colombian territory for environmental protection purposes. The satellite provides, through two payloads, data as electro-optical multispectral images (resolution between 4.75 m and 5 m) and, in parallel, data using a spectrometer in the short-wave infrared spectral range of 1000-1700 nm for monitoring greenhouse gases. Based on high-level technical requirements and the operational concept, the input identification and definition of the architecture of the space, ground, and launch segments were performed, defining a six-unit satellite, a ground segment with an S/X-band antenna in the city of Cali, and the use of an EXOpod with launcher-associated characteristics. The subsystems of the mechanical structure, electrical power system, data and command handling system, on-board communication system, and attitude control and determination system were defined and characterized in detail, in accordance with the ECSS standards of the European Space Agency. The initial design solution was customized based on spaces, operational and technical requirements, and the financial budget available for the space mission. It is noteworthy that the article contains exclusive contributions from Colombia, including the definition of the S/X-Band antenna, encryption software, and the design and implementation of the physical interface board to achieve electronic compatibility between the satellite bus and the Argus 2000 spectrometer. [ABSTRACT FROM AUTHOR]

Multispectral and multimodal images are of important usage in the field of multi-source visual information fusion. Due to the alternation or movement of image devices, the acquired multispectral and multimodal images are usually misaligned, and hence image registration is pre-requisite. Different from the registration of common images, the registration of multispectral or multimodal images is a challenging problem due to the nonlinear variation of intensity and gradient. To cope with this challenge, we propose the phase congruency network (PCNet) to enhance the structure similarity of multispectral or multimodal images. The images can then be aligned using the similarity-enhanced feature maps produced by the network. PCNet is constructed under the inspiration of the well-known phase congruency. The network embeds the phase congruency prior into two simple trainable layers and series of modified learnable Gabor kernels. Thanks to the prior knowledge, once trained, PCNet is applicable on a variety of multispectral and multimodal data such as flash/no-flash and RGB/NIR images without additional further tuning. The prior also makes the network lightweight. The trainable parameters of PCNet is 2400× and 1500×less than the deep-learning registration method deep homography network (DHN) and unsupervised deep homography network (UDHN), while its registration performance surpasses them. Experimental results validate that PCNet outperforms current state-of-the-art conventional multimodal registration algorithms. Besides, PCNet can act as a complementary part of the deep-learning registration methods, which significantly boosts their registration accuracy. On the Columbia imaging and vision laboratory (CAVE) multispectral dataset, the percentage of the number of images under 1 pixel average corner error (ACE) of UDHN is raised from 0.1% to 82.5% after the processing of PCNet. • Using phase congruency prior to design a trainable light-weight network. • The network can enhance the similarity of multispectral/multimodal images. • Achieving high accuracy registration on various multispectral/multimodal datasets. • Boosting the registration results of deep-learning methods significantly. [ABSTRACT FROM AUTHOR]

Improving the remote sensing frameworks related to land cover mapping is necessary to make informed policy, development, planning, and natural resource management decisions. These efforts are especially important in tropical countries where technical capacity is limited. Land cover legend specification is a critical first step when mapping land cover, with consequences for its subsequent use and interpretation of results. We integrated the temporal metrics of SAR (Synthetic Aperture Radar) and multispectral data (Sentinel-1 and Sentienel-2) with visual pixel classifications and field surveys using five machine learning algorithms that apply different statistical methods to assess the prediction and mapping of two different land cover legends at a high spatial resolution (10 m) in a tropical region with seasonal flooding. The evaluated legends were CORINE (Coordination of Information on the Environment) and ECOSO, a legend that we defined based on the ecological and socio-economic conditions of the study area. Compared with previous studies, we obtained high accuracies for land cover modeling (kappa = 0.82) and land cover mapping (kappa = 0.76) when using ECOSO. We also found that the CORINE legend generated lower accuracies than the ECOSO legend (kappa = 0.79 for land cover modeling and kappa = 0.61 for the land cover mapping). Although CORINE was developed for European environments, it is the official land cover legend of Colombia, a South American country with tropical ecosystems not found in Europe. Therefore, some of the CORINE classes have ambiguous definitions for the study area, explaining the lower accuracy of its modeling and mapping. We used free and open-access data and software in this research; thus, our methods can be applied in other tropical regions. [ABSTRACT FROM AUTHOR]

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In most Latin American countries, there are significant numbers of missing people and forced disappearances, over 120,000 in Colombia alone. Successful detection of shallow buried human remains by forensic search teams is difficult in varying terrain and climates. Previous research has created controlled simulated clandestine graves of murder victims to optimize search techniques and methodologies. This paper reports on a study on controlled test site results over four simulated dismembered victims' clandestine graves as this is sadly a common scenario encountered in Latin America. Multispectral images were collected once post‐burial, electrical resistivity surveys were collected 4 times, and ground penetrating radar (GPR) surveys were collected three times up to the end of the 371 day survey monitoring period. After data processing, results showed that the multispectral data set could detect the simulated clandestine and control graves, with electrical resistivity imaging relative high resistances over some of the simulated graves but not over the empty control graves. GPR results showed good imaging on the Day 8 surveys, medium imaging on the Day 294 surveys, and medium to good imaging on the Day 371 surveys. Study implications suggest that, while clandestine graves of dismembered homicide victims would likely result in smaller‐sized graves when compared to graves containing intact bodies, these graves can still potentially be detected using remote sensing and geophysical methods. [ABSTRACT FROM AUTHOR]

The fusion of hyperspectral (HS) and multispectral (MS) images with complementary high spectral and high spatial resolution information has been successfully applied to improve the low spatial resolution limitation of current satellite sensors with high spectral resolution. Fusion methods based on spectral unmixing have led to state-of-the-art results with the advantage of obtaining pure spectral signatures and their proportion in the image of analysis as a result of the fusion process. However, pure spectral signatures observed in HS images are affected by variations in atmospheric, environmental and illumination conditions which are significant sources of errors in HS image analysis. Conventional unmixing-based fusion methods neglect the spectral variability of HS images introducing and propagating errors through the fusion process, affecting further inference tasks such as classification, target detection, and change detection. Therefore, this paper proposes HS-MS image fusion algorithm based on spectral unmixing accounting for spectral variability through an augmented linear mixing model (ALMM). An alternating optimization strategy is combined with the alternating direction method of multipliers (ADMM) to efficiently minimize the underlying cost function of the fusion problem. Simulation results on realistic data demonstrate that the proposed method outperforms state-of-the-art fusion methods in different metrics. Additionally, the fusion of crop images acquired with DESIS and Sentinel-2 sensors in a region of Colombia confirms the advantages of the proposed fusion method. [ABSTRACT FROM AUTHOR]

Blue LUminous Events (BLUEs) are transient corona discharges occurring in thunderclouds and characterized by strong 337.0 nm light flashes with absent (or weak) 777.4 nm component. We present the first nighttime climatology of BLUEs as detected by the Modular Multispectral Imaging Array of the Atmosphere‐Space Interaction Monitor showing their worldwide geographical and seasonal distribution. A total (land and ocean) of ∼11 BLUEs occur around the globe every second at local midnight and the average BLUE land/sea ratio is ∼7:4. The northwest region of Colombia shows an annual nighttime peak. Globally, BLUEs are maximized during the boreal summer‐autumn, contrary to lightning which is maximed in the boreal summer. The geographical distribution of nighttime BLUEs shows three main regions in, by order of importance, the Americas, Europe/Africa and Asia/Australia. Plain Language Summary: Blue LUminous Events (BLUEs) are transient corona discharges occurring in thunderclouds and characterized by their distinct 337.0 nm light flashes with absent (or negligible) 777.4 nm component. We present the first two year nighttime climatology of BLUEs as detected by the Modular Multispectral Imaging Array of the Atmosphere‐Space Interaction Monitor on board the International Space Station that shows distinct worldwide geographical and seasonal distributions. Key Points: The first nighttime two‐year climatology of streamer corona discharges (blue luminous events) in thunderclouds is presentedGlobally, the rate of blue luminous events at local midnight is ∼11 per secondZonal and meridional distributions of blue luminous events peak in the northern tropic and the Americas, respectively [ABSTRACT FROM AUTHOR]

Wildlife-vehicle collision - WVC is a phenomenon that arises from the fragmentation of ecosystems by roads, limiting the mobility of individuals and putting at risk the stability of populations by increasing mortality. Colombia is not unaware of the problem of the WVC, evidenced in different scientific publications that describe the WVC in the roads of the country. Although the rise of artificial intelligence has significant advances in the prediction of spatial phenomena in recent years, it has not yet been sufficiently explored by Road Ecology. For this reason, this research aimed to develop a methodology to predict the sites of accumulation of WVC in eastern Antioquia, Colombia, based on artificial intelligence algorithms, geographic information systems - GIS, and multispectral image processing. During the development of this research, it was identified that the features most related to the WVC in the study area are: Distance to Forest, Distance to Biological Corridor, Ground Resistance to Movement, Cost of Movement, the bands of the Landsat 8 satellite: 9, 10, 11 and the normalized burning index (NBRI). Different machine learning algorithms were compared (k-nearest neighbours, support vector machines (SVM), random forests (RF), and artificial neural networks). SMOTE and ADASYN balancing techniques were applied. The results allowed to identify that the RF algorithm with ADASYN yielded the best performance when subjected to spatial-wise cross-validation (AUC-ROC 0.78 ± 0.12), surpassing the results of current state-of-the-art. Finally, the methodology was validated through a transfer learning experiment, training the RF-ADASYN algorithm with three zones of the eastern Antioquia region and validating on a different section (AUC-ROC = 0.87 ± 0.09), retraining the initial model with 5% of data from the validation database. • This work shows a theoretical approach to the prediction of hotspots in areas with little data collected. • The methodology used in this work can reduce the response times to the phenomenon of Wildlife Vehicle Collisions (WVC). • Our methodology uses artificial intelligence, geographic information systems, and multispectral imagery. • This paper differs from other works by its use of artificial intelligence, which Road ecology has not sufficiently explored. • This research applies a novel cross-validation technique for WVC data prediction, considering the spatial autocorrelation. [ABSTRACT FROM AUTHOR]

In 2014 the Hispanic Society Museum & Library, New York, acquired a viceregal Spanish American lacquered gourd, dating to 17th-century Colombia, which was decorated using an indigenous technique known as barniz de Pasto. This technique employs local, raw materials, including natural dyes and a plant resin commonly known as mopa mopa, harvested from Elaeagia pastoensis Mora trees that grow in the Andean rainforest. An in-depth scientific study of the gourd aimed at determining its materials and manufacturing techniques, and at comparing the results with the description of local botanical species reported by Alexander von Humboldt, the German naturalist and explorer, in the account of his travels through the region in 1801. The use of several non-invasive techniques was followed by micro-sampling. Initially, X-ray fluorescence (XRF) analysis was performed to evaluate the possible presence of inorganic pigments, while fiber optics reflectance spectroscopy (FORS) and multiband imaging provided preliminary data concerning the colorants. Dyes and pigments were fully characterized using Raman spectroscopy and high-performance liquid chromatography with photodiode array detector (HPLC-PDA); a detailed description of the resin was obtained with HPLC-PDA and pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS); the metallic elements and overall decoration were analyzed by scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM/EDS). Radiocarbon dating completed the technical information on the object. This work confirmed the identity of the resin as Elaeagia pastoensis Mora, supporting an unequivocal classification of the gourd as a barniz de Pasto object; the color palette was found to include some of the pigments listed by Humboldt, but also comprised other materials, such as calomel, a rare white pigment based on mercury(I) chloride; examination of the decoration's intricate stratigraphy provided insight into the complexity of the barniz de Pasto technique, in which silver leaf is typically applied on top of a sheet of untinted resin and covered with a variable number of dyed resin layers; finally, a pre-1650 date was firmly established for the gourd, which is in line with stylistic observations that had tentatively placed this object in the early barniz de Pasto period. In addition to providing conservators with the proper tools to preserve similar lacquered objects, the wealth of knowledge gleaned from this study has revealed fascinating details about the technique employed, demonstrating the extraordinary skill and craftsmanship of the artisans involved in the lacquer arts. [ABSTRACT FROM AUTHOR]