NTL-Unet: A Satellite-Based Approach for Non-Technical Loss Detection in Electricity Distribution Using Sentinel-2 Imagery and Machine Learning.

This study introduces an orbital monitoring system designed to quantify non-technical losses (NTLs) within electricity distribution networks. Leveraging Sentinel-2 satellite imagery alongside advanced techniques in computer vision and machine learning, this system focuses on accurately segmenting urban areas, facilitating the removal of clouds, and utilizing OpenStreetMap masks for pre-annotation. Through testing on two datasets, the method attained a Jaccard index (IoU) of 0.9210 on the training set, derived from the region of France, and 0.88 on the test set, obtained from the region of Brazil, underscoring its efficacy and resilience. The precise segmentation of urban zones enables the identification of areas beyond the electric distribution company's coverage, thereby highlighting potential irregularities with heightened reliability. This approach holds promise for mitigating NTL, particularly through its ability to pinpoint potential irregular areas.

Multitemporal monitoring of paramos as critical water sources in Central Colombia.

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.

Carbon stock estimation in a Brazilian mangrove using optical satellite data.

The research proposes a model to estimate the carbon stock in mangrove forests from multispectral images from Landsat 8 and Sentinel 2B satellites. The Gramame River mangrove, located on the southern coast of Paraíba State, Brazil, was adopted as the study area. Carbon stocks in biomass, below and above ground, were measured from a forest inventory, and vegetation indices were processed on the Google Earth Engine (GEE) platform. To define the fit curves, linear and non-linear regressions were used. The choice of the model considered the highest coefficients of determination (R2), the biomass and carbon stock were estimated from the equations. The biomass carbon stock, calculated from field data, corresponded to 22.27 Gg C, equivalent to 81.75 Gg CO2, with 13.85 Gg C (50.84 Gg CO2) and 8.42 Gg C (30.91 Gg CO2) stored in biomass above and below ground, respectively. Among the models fitted to the indices calculated from Landsat 8 images, NDVI was the one that best explained the spatial distribution of biomass and carbon, with 90.26%. For Sentinel 2B, SAVI was able to explain 80.76%. The total estimated plant carbon stocks corresponded to 26.66 Gg (16.20 Gg C above and 10.36 Gg C below ground) for Landsat 8 and 27.76 Gg C (16.93 Gg C above and 10.83 Gg C below ground) for Sentinel 2B. The proposed work methodology and the suggested mathematical models can be replicated to analyze carbon stocks in other locations, especially in the Americas, because they share the same species.

Semi-empirical models for remote estimating colored dissolved organic matter (CDOM) in a productive tropical estuary.

Inland waters are important components of the global carbon cycle as they regulate the flow of terrestrial carbon to the oceans. In this context, remote monitoring of Colored Dissolved Organic Matter (CDOM) allows for analyzing the carbon content in aquatic systems. In this study, we develop semi-empirical models for remote estimation of the CDOM absorption coefficient at 400 nm (aCDOM) in a tropical estuarine-lagunar productive system using spectral reflectance data. Two-band ratio models usually work well for this task, but studies have added more bands to the models to reduce interfering signals, so in addition to the two-band ratio models, we tested three- and four-band ratios. We used a genetic algorithm (GA) to search for the best combination of bands, and found that adding more bands did not provide performance gains, showing that the proper choice of bands is more important. NIR-Green models outperformed Red-Blue models. A two-band NIR-Green model showed the best results (R2 = 0.82, RMSE = 0.22 m-1, and MAPE = 5.85%) using field hyperspectral data. Furthermore, we evaluated the potential application for Sentinel-2 bands, especially using the B5/B3, Log(B5/B3) and Log(B6/B2) band ratios. However, it is still necessary to further explore the influence of atmospheric correction (AC) to estimate the aCDOM using satellite data.

Spatial-temporal dynamics of decaying stages of pelagic Sargassum spp. along shorelines in Puerto Rico using Google Earth Engine.

Coastal social-ecological systems in the Caribbean are affected by pelagic Sargassum spp. influxes and decomposition, but most satellite monitoring efforts focus on offshore waters. We developed a method to detect and spatial-temporally assess sargassum accumulations and their decaying stages along the shoreline and nearshore waters. A multi-predictor Random Forest model combining Sentinel-2 MultiSpectral Instrument reflectance bands and several vegetation, seaweed, water, and water quality indices was developed within the online Google Earth Engine platform. The model achieved 97 % overall accuracy and identified both fresh and decomposing sargassum, as well as the Sargassum-brown-tide generated from decomposing sargassum. We identified three hotspots of sargassum accumulation in La Parguera, Puerto Rico and found that sargassum was present every month in at least one of its forms during the entire time series (September 2015-January 2022). This research provides information to understand sargassum impacts and areas where mitigation efforts need to focus.

Satellite assessment of eutrophication hot spots and algal blooms in small and medium-sized productive reservoirs in Uruguay's main drinking water basin.

Intensive agricultural activities favor eutrophication and harmful phytoplankton blooms due to the high export of nutrients and damming of rivers. Productive watersheds used for water purification can have multiple reservoirs with phytoplankton blooms, which constitutes a high health risk. In general, water quality monitoring does not cover small- and medium-sized reservoirs (0.25-100 ha) of productive use due to their large number and location in private properties. In this work, the in situ trophic state of fourteen reservoirs was simultaneously assessed using Sentinel-2 images in the Santa Lucía River Basin, the main drinking water basin in Uruguay. These reservoirs are hypereutrophic (0.18-5.22 mg total P L-1) with high phytoplankton biomasses (2.8-4439 µg chlorophyll-a L-1), mainly cyanobacteria. Based on data generated in situ and Sentinel-2 imagery, models were fitted to estimate satellite Chl-a and transparency in all the basin reservoirs (n = 486). The best fits were obtained with the green-to-red band ratio (560 and 665 nm, R2 = 0.84) to estimate chlorophyll-a and reflectance at 833 nm (R2 = 0.73) to determine transparency. The spatial distribution of the trophic state was explored by spatial autocorrelation and hotspot analysis, and the variation in spatial patterns could be determined prior and subsequent to a maximum cyanobacteria value in water treatment plant intakes. Therefore, reservoirs with greater potential for phytoplankton biomass export were identified. This work provides the first fitted tool for satellite monitoring of numerous reservoirs and strengthens the country's ability to respond to harmful phytoplankton blooms in its main drinking water basin.

Analysis of Biophysical Variables in an Onion Crop (Allium cepa L.) with Nitrogen Fertilization by Sentinel-2 Observations.

The production of onions bulbs (Allium cepa L.) requires a high amount of nitrogen. According to the demand of sustainable agriculture, the information-development and communication technologies allow for improving the efficiency of nitrogen fertilization. In the south of the province of Buenos Aires, Argentina, between 8000 and 10,000 hectares per year-1 are cultivated in the districts of Villarino and Patagones. This work aimed to analyze the relationship of biophysical variables: leaf area index (LAI), canopy chlorophyll content (CCC), and canopy cover factor (fCOVER), with the nitrogen fertilization of an intermediate cycle onion crop and its effects on yield. A field trial study with different doses of granulated urea and granulated urea was carried out, where biophysical characteristics were evaluated in the field and in Sentinel-2 satellite observations. Field data correlated well with satellite data, with an R2 of 0.91, 0.96, and 0.85 for LAI, fCOVER, and CCC, respectively. The application of nitrogen in all its doses produced significantly higher yields than the control. The LAI and CCC variables had a positive correlation with yield in the months of November and December. A significant difference was observed between U250 (62 Mg ha-1) and the other treatments. The U500 dose led to a yield increase of 27% compared to U250, while the difference between U750 and U500 was 6%.

Investigation of pre and post environmental impact of the lockdown (COVID-19) on the water quality of the Capibaribe and Tejipió rivers, Recife metropolitan region, Brazil.

The coronavirus pandemic has seriously affected human health, although some improvements on environmental indexes have temporarily occurred, due to changes on socio-cultural and economic standards. The objective of this study was to evaluate the impacts of the coronavirus and the influence of the lockdown associated with rainfall on the water quality of the Capibaribe and Tejipió rivers, Recife, Northeast Brazil, using cloud remote sensing on the Google Earth Engine (GEE) platform. The study was carried out based on eight representative images from Sentinel-2. Among the selected images, two refer to the year 2019 (before the pandemic), three refer to 2020 (during a pandemic), two from the lockdown period (2020), and one for the year 2021. The land use and land cover (LULC) and slope of the study region were determined and classified. Water turbidity data were subjected to descriptive and multivariate statistics. When analyzing the data on LULC for the riparian margin of the Capibaribe and Tejipió rivers, a low permanent preservation area was found, with a predominance of almost 100% of the urban area to which the deposition of soil particles in rivers are minimal. The results indicated that turbidity values in the water bodies varied from 6 mg. L-1 up to 40 mg. L-1. Overall, the reduction in human-based activities generated by the lockdown enabled improvements in water quality of these urban rivers.

Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine.

Crops and ecosystems constantly change, and risks are derived from heavy rains, hurricanes, droughts, human activities, climate change, etc. This has caused additional damages with economic and social impacts. Natural phenomena have caused the loss of crop areas, which endangers food security, destruction of the habitat of species of flora and fauna, and flooding of populations, among others. To help in the solution, it is necessary to develop strategies that maximize agricultural production as well as reduce land wear, environmental impact, and contamination of water resources. The generation of crop and land-use maps is advantageous for identifying suitable crop areas and collecting precise information about the produce. In this work, a strategy is proposed to identify and map sorghum and corn crops as well as land use and land cover. Our approach uses Sentinel-2 satellite images, spectral indices for the phenological detection of vegetation and water bodies, and automatic learning methods: support vector machine, random forest, and classification and regression trees. The study area is a tropical agricultural area with water bodies located in southeastern Mexico. The study was carried out from 2017 to 2019, and considering the climate and growing seasons of the site, two seasons were created for each year. Land use was identified as: water bodies, land in recovery, urban areas, sandy areas, and tropical rainforest. The results in overall accuracy were: 0.99% for the support vector machine, 0.95% for the random forest, and 0.92% for classification and regression trees. The kappa index was: 0.99% for the support vector machine, 0.97% for the random forest, and 0.94% for classification and regression trees. The support vector machine obtained the lowest percentage of false positives and margin of error. It also acquired better results in the classification of soil types and identification of crops.

Forest phenoclusters for Argentina based on vegetation phenology and climate.

Forest biodiversity conservation and species distribution modeling greatly benefit from broad-scale forest maps depicting tree species or forest types rather than just presence and absence of forest, or coarse classifications. Ideally, such maps would stem from satellite image classification based on abundant field data for both model training and accuracy assessments, but such field data do not exist in many parts of the globe. However, different forest types and tree species differ in their vegetation phenology, offering an opportunity to map and characterize forests based on the seasonal dynamic of vegetation indices and auxiliary data. Our goal was to map and characterize forests based on both land surface phenology and climate patterns, defined here as forest phenoclusters. We applied our methodology in Argentina (2.8 million km2 ), which has a wide variety of forests, from rainforests to cold-temperate forests. We calculated phenology measures after fitting a harmonic curve of the enhanced vegetation index (EVI) time series derived from 30-m Sentinel 2 and Landsat 8 data from 2018-2019. For climate, we calculated land surface temperature (LST) from Band 10 of the thermal infrared sensor (TIRS) of Landsat 8, and precipitation from Worldclim (BIO12). We performed stratified X-means cluster classifications followed by hierarchical clustering. The resulting clusters separated well into 54 forest phenoclusters with unique combinations of vegetation phenology and climate characteristics. The EVI 90th percentile was more important than our climate and other phenology measures in providing separability among different forest phenoclusters. Our results highlight the potential of combining remotely sensed phenology measures and climate data to improve broad-scale forest mapping for different management and conservation goals, capturing functional rather than structural or compositional characteristics between and within tree species. Our approach results in classifications that go beyond simple forest-nonforest in areas where the lack of detailed ecological field data precludes tree species-level classifications, yet conservation needs are high. Our map of forest phenoclusters is a valuable tool for the assessment of natural resources, and the management of the environment at scales relevant for conservation actions.

Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine.

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013-2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2 = 0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

Spectro-temporal analysis of the Paraopeba River water after the tailings dam burst of the Córrego do Feijão mine, in Brumadinho, Brazil.

Remote sensing is an important tool for environmental assessment, especially in the event of disasters such as the tailings dam burst at the Córrego do Feijão mine, located in the Paraopeba River basin, Brazil. Thus, this study aimed to carry out a spectro-temporal analysis of the Paraopeba River water given the dam burst, using multispectral images from the MSI sensor onboard Sentinel-2 satellites. For this analysis, sections along the river were defined by the creation of buffers, with 10-km intervals each, starting from the origin of the burst. For each section, the average visible to near-infrared (NIR) reflectance values per band and the Normalized Difference Water Index (NDWI) were obtained. We found that the red edge and NIR bands (B5, B6, B7, B8, and B8A) showed higher reflectance values when compared to the visible bands in the months immediately after the disaster, especially in the first 20 km. In these months, negative NDWI values were also found for almost all sections downstream, demonstrating the large volume of mining tailings in the Paraopeba River. The seasonal variation of the observed values indicates the resuspension of the material deposited at the river bottom with the beginning of the rainy season. Finally, we highlight the usefulness of the MSI/Sentinel-2 red edge and NIR bands for further studies on the monitoring from space of water bodies subjected to contamination by large amounts of mud with iron ore tailings and contaminants, as occurred in the state of Minas Gerais, southeastern Brazil.

Multitemporal satellite imagery analysis for soil organic carbon assessment in an agricultural farm in southeastern Brazil.

Soil organic carbon (SOC) plays a crucial role for soil health. However, large datasets needed to accurately assess SOC at high resolution across scales are labor-intensive, time-consuming, and expensive. Ancillary geodata, including remote sensing spectral indices (RS-SIs) and topographic indicators (TIs), have been proposed as spatial covariates. Reported relationships between SOC and RS-SIs are erratic, possibly because single-date RS-SIs do not accurately capture SOC spatial variability due to transient confounding factors in the soil (e.g., moisture). However, multitemporal RS-SI data analysis may lead to noise reduction in SOC versus RS-SI relationships. This study aimed at: i) comparing single-date versus multitemporal RS-Sis derived from Sentinel-2 imagery for assessment of topsoil (0-0.2 m) SOC in two agricultural fields located in south-eastern Brazil; ii) comparing the performance of RS-SIs and TIs; iii) using adequate RS-SIs and TIs to compare sampling schemes defined on different collection grids; and iv) studying the temporal changes of SOC (0-0.2 m and 0.2-0.4 m). Results showed that: i) single-date RS-SIs were not reliable proxies for topsoil SOC at the study sites. For most of the tested RS-SIs, multitemporal data analysis produced accurate proxies for SOC; e.g., for the Normalized Difference Vegetation Index, the 4.5th multitemporal percentile predicted SOC with an R2 of 0.64; ii) The best TI was elevation (ranging from 643 to 684 m) with an R2 of 0.70; iii) The multitemporal SI and elevation maps indicated that the different sampling schemes were equally representative of the topsoil SOC's distribution across the entire area; and iv) From 2012 through 2019, topsoil SOC increased from 19.3 to 24.1 g kg-1. The ratio between SOC in the topsoil and subsoil (0.2-0.4 m) decreased from 1.7 to 1.1. Further testing of the proposed multitemporal RS-SI analysis is necessary to confirm its dependability for SOC assessment in Brazil and elsewhere.

Hotspot analysis of spatial distribution of algae blooms in small and medium water bodies.

Intensive land use favors eutrophication processes and algae bloom proliferation in freshwaters, which is considered to be one of the main environmental issues worldwide. In general, and particularly in South America, inland water monitoring only covers the main water bodies due to the high costs and efforts involved. In order to improve the coverage of spatial and temporal of algae bloom monitoring, remote sensing serves as an alternative tool. Thereby, the analysis of significant spatial clusters of high values (hotspots) and low values (coldspots) of chlorophyll-a has been applied in coastal studies; however, at present, there are no studies in freshwaters. In this study, Getis-Ord Gi* hotspot analysis was applied to detect spatial distribution patterns of algae bloom dynamics in small- and medium-sized freshwater bodies. Four in situ samplings were carried out in five suburban lakes of Uruguay, in agreement with the satellite capture. Total and cyanobacterial chlorophyll-a concentration, and suspended solids were evaluated. Linear models were developed by combining pre-established indexes with additional Sentinel-2 spectral bands and in situ data. The relationship between red and red edge regions allowed mapping the chlorophyll-a in the study lakes with an adjustment of R2 = 0.83. Hotspot analysis was performed with the selected linear model, and significant chlorophyll-a variability within each lake was successfully detected. The novel application of hotspots analyses presented in this work represents a contribution to advance knowledge in the remote detection of algae bloom dynamics and improve monitoring capabilities of inland water bodies.

Water quality assessment using Sentinel-2 imagery with estimates of chlorophyll a, Secchi disk depth, and Cyanobacteria cell number: the Cantareira System reservoirs (São Paulo, Brazil).

Satellite images were used to assess surface water quality based on the concentration of chlorophyll a (chla), light penetration measured by the Secchi disk method (SD), and the Cyanobacteria cells number per mL (cyano). For this case study, six reservoirs interconnected were evaluated, comprising the Cantareira System (CS) in São Paulo State (Brazil). The work employed Sentinel-2 images from 2015 to 2018, SNAP image processing software, and the native products conc_chl and kd_z90max, treated using Case 2 Regional Coast Color (C2RCC) atmospheric correction. The database was obtained from CETESB, the agency legally responsible for operation of the Inland Water Quality Monitoring Network in São Paulo State. The results demonstrated robustness in the estimates of chla (RMSE = 3.73; NRMSE% = 19%) and SD (RMSE = 2,26; NRMSE% = 14%). Due to the strong relationship between cyano and chla (r2 = 0.84, p < 0.01, n = 90), both obtained from field measurements, there was also robustness in cyano estimates based on the estimates of chla from the satellite images. The data revealed a clear pattern, with the upstream reservoirs being more eutrophic, compared to those downstream. There were evident concerns, about water quality, particularly due to the high numbers of Cyanobacteria cells, especially in the upstream reservoirs.

Modeling the association between Aedes aegypti ovitrap egg counts, multi-scale remotely sensed environmental data and arboviral cases at Puntarenas, Costa Rica (2017-2018).

Problems with vector surveillance are a major barrier for the effective control of vector-borne disease transmission through Latin America. Here, we present results from a 80-week longitudinal study where Aedes aegypti (L.) (Diptera: Culicidae) ovitraps were monitored weekly at 92 locations in Puntarenas, a coastal city in Costa Rica with syndemic Zika, chikungunya and dengue transmission. We used separate models to investigate the association of either Ae. aegypti-borne arboviral cases or Ae. aegypti egg counts with remotely sensed environmental variables. We also evaluated whether Ae. aegypti-borne arboviral cases were associated with Ae. aegypti egg counts. Using cross-correlation and time series modeling, we found that arboviral cases were not significantly associated with Ae. aegypti egg counts. Through model selection we found that cases had a non-linear response to multi-scale (1-km and 30-m resolution) measurements of temperature standard deviation (SD) with a lag of up to 4 weeks, while simultaneously increasing with finely-grained NDVI (30-m resolution). Meanwhile, median ovitrap Ae. aegypti egg counts increased, and respectively decreased, with temperature SD (1-km resolution) and EVI (30-m resolution) with a lag of 6 weeks. A synchrony analysis showed that egg counts had a travelling wave pattern, with synchrony showing cyclic changes with distance, a pattern not observed in remotely sensed data with 30-m and 10-m resolution. Spatially, using generalized additive models, we found that eggs were more abundant at locations with higher temperatures and where EVI was leptokurtic during the study period. Our results suggest that, in Puntarenas, remotely sensed environmental variables are associated with both Ae. aegypti-borne arbovirus transmission and Ae. aegypti egg counts from ovitraps.

Satellite-derived mapping of kelp distribution and water optics in the glacier impacted Yendegaia Fjord (Beagle Channel, Southern Chilean Patagonia).

Giant kelp Macrocystis pyrifera is a brown alga with extensive global distribution, however, recent evidence suggests that its dynamics presents high degree of regional variability. In southern Chilean fjord region, largely unexplored kelp forests are currently being threatened by global change and human impacts. High-resolution satellite (Sentinel-2) imagery was used to describe temporal and spatial distribution patterns of kelp beds in Yendegaia Fjord (Beagle Channel) using Spectral Mixture Analysis (SMA), and to characterize water optical gradients of this habitat strongly influenced by river runoff from a melting glacier. The suitability of SMA for kelp classification was contrasted with other vegetation indices (NDVI, EVI, FAI). Validation was made using drone aerial photographs of kelp canopies. Different analysis tools resulted in up to 35% difference in kelp coverage estimation. The overall accuracy (66-82%) of kelp classification followed an order FAI < EVI < NDVI < SMA. Omission error of SMA and lower coincidence with vegetation indices occurred in pixels with low kelp pixel abundance (<0.50). Based on SMA, the lowest kelp abundance was observed in the river mouth with high turbidity, increasing towards the Beagle Channel. The highest kelp abundance was observed in late summer, but otherwise no clear seasonal patterns could be observed. Water turbidity presented both spatial and seasonal variation. Strong particle sedimentation (leading to light attenuation, interference with remote detection of kelps, and even to their detachment due to substrate quality) and tidal fluctuations in glacier-impacted fjord-type environments can be identified as key features affecting both the kelp population dynamics as well as their remote sensing. Also, low sun elevation at high latitudes in mid winter produces uncertainties in image analyses. In all, the remote sensing approach used in the present study can be regarded as a useful tool to map and monitor kelps forests from a remote region.

Temporal multispectral and 3D analysis of Cerro de Pasco, Peru.

Mining operations across the world often lead to contamination of land, water resources, ecosystems and in some cases, entire communities.Results of recent health and ground sampling studies revealed extensive lead contamination within the populace and around the City of Cerro de Pasco, Peru. Tailings excavated from a large open pit zinc mine in the center of the city have been aggregated in four large stockpiles within close proximity to neighborhoods, schools, and hospitals. Visual comparison of ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) imagery from 2001 and Sentinel-2 imagery from 2018 suggests a size increase in one tailing stockpile in particular near the neighborhood of Paragsha. Due to ongoing mining efforts, the hypothesis motivating the work presented here is that Pb-bearing minerals would be detectable through multispectral analysis, an increase in Pb mineral percent abundance would be observed and tailing stockpile volume would be detectable between 2001 and 2016. This hypothesis is tested using Spectral Angle Mapper (SAM), Adaptive Coherence Estimator (ACE), and Jeffries-Matusita distance calculation on ASTER (2001) and Sentinel-2 (2018) VNIR and SWIR bands. Volume and area estimate of tailing stockpiles were calculated using a photogrammetrically derived point cloud. SAM detected the presence of five Pb-bearing minerals around Cerro de Pasco and Paragsha. The results of the temporal SAM analysis displayed an increase of approximately 17% of Pb-bearing minerals around the greater Cerro de Pasco city area and approximately 11% for the neighborhood of Paragsha. Jeffries-Matusita distance results suggest clear correlation between contamination sources and affected locations. Total tailing stockpile volume was measured to be approximately 200,300,000 m3. Volume for Pile 4 was estimated to have increased by approximately 46,000,000 m3 between 2001 and 2018. These presented results will hopefully inspire and guide future remote sensing campaigns, perhaps involving a UAV or aircraft-based hyperspectral instrument.

Mapping of spatial and temporal variation of water characteristics through satellite remote sensing in Lake Panguipulli, Chile.

Central-southern Chile is characterized by a series of large lakes that originate in the Andes Mountains. This region is facing increasing anthropogenic impact, which threatens the oligotrophic status of these lakes. While monitoring programs are often based on a limited spatial and temporal coverage, remote sensing offers promising tools for large-scale observations improving our capacity to study comprehensively indicators of lake properties. Seasonal trends (long-term means) and intra-lake variation of surface water temperature (SWT), turbidity and chlorophyll a in Lake Panguipulli were studied through satellite imagery from Landsat 5 TM, 7 ETM+ and 8 OLI (1998-2018; SWT, turbidity), and Sentinel-2A/B MSI (2016-2017; chlorophyll). Remotely sensed data were validated against in situ data from monitoring database. Satellite-derived SWT (representing the surface skin layer of water, so-called skin temperature) showed good similarity with in situ (bulk) temperature (RRMSD 0.17, R2 = 0.86), although was somewhat lower (RMSD of 2.77 °C; MBD of -2.10 °C). Seasonal long-term means of turbidity from satellite imagery corresponded to those from in situ data, while satellite-derived predictions (based on OC2v2 algorithm) overestimated chlorophyll a levels slightly in summer-spring. SWT ranged from 8.0 °C in winter to 17.5 °C in summer. Mean turbidity (1.6 FNU) and chlorophyll a (1.1 µg L-1) levels were at their lowest in summer. Spatial and seasonal patterns reflected the bathymetry and previously described mixing patterns of this monomictic lake: warming of shallow bays in spring extended to wider area along with summer stratification period, while mixing of the water column was reflected in spatially more homogenous SWT in fall-winter. Spatial heterogeneity in summer was confirmed by a clear separation of different lake areas based on SWT, turbidity and chlorophyll a using 3-D plot. Mapping of spatial and seasonal variation using satellite imagery allowed identifying lake areas with different characteristics, improving strategies for water resource management.

An assessment of commonly employed satellite-based remote sensors for mapping mangrove species in Mexico using an NDVI-based classification scheme.

Optimizing the classification accuracy of a mangrove forest is of utmost importance for conservation practitioners. Mangrove forest mapping using satellite-based remote sensing techniques is by far the most common method of classification currently used given the logistical difficulties of field endeavors in these forested wetlands. However, there is now an abundance of options from which to choose in regards to satellite sensors, which has led to substantially different estimations of mangrove forest location and extent with particular concern for degraded systems. The objective of this study was to assess the accuracy of mangrove forest classification using different remotely sensed data sources (i.e., Landsat-8, SPOT-5, Sentinel-2, and WorldView-2) for a system located along the Pacific coast of Mexico. Specifically, we examined a stressed semiarid mangrove forest which offers a variety of conditions such as dead areas, degraded stands, healthy mangroves, and very dense mangrove island formations. The results indicated that Landsat-8 (30 m per pixel) had  the lowest overall accuracy at 64% and that WorldView-2 (1.6 m per pixel) had the highest at 93%. Moreover, the SPOT-5 and the Sentinel-2 classifications (10 m per pixel) were very similar having accuracies of 75 and 78%, respectively. In comparison to WorldView-2, the other sensors overestimated the extent of Laguncularia racemosa and underestimated the extent of Rhizophora mangle. When considering such type of sensors, the higher spatial resolution can be particularly important in mapping small mangrove islands that often occur in degraded mangrove systems.