Project description:This study proposed the use of satellite hyperspectral imagery to support tick-borne infectious diseases surveillance based on monitoring the variation in amplifier hosts food sources. To verify this strategy, we used the data of the human rickettsiosis occurrences in southeastern Brazil, region in which the emergence of this disease is associated with the rising capybara population. Spatio-temporal analysis based on Monte Carlo simulations was used to identify risk areas of human rickettsiosis and hyperspectral moderate-resolution imagery was used to identify the increment and expansion of sugarcane crops, main food source of capybaras. In general, a pixel abundance associated with increment of sugarcane crops was detected in risk areas of human rickettsiosis. Thus, the hypothesis that there is a spatio-temporal relationship between the occurrence of human rickettsiosis and the sugarcane crops increment was verified. Therefore, due to the difficulty of monitoring locally the distribution of infectious agents, vectors and animal host's, satellite hyperspectral imagery can be used as a complementary tool for the surveillance of tick-borne infectious diseases and potentially of other vector-borne diseases.
Project description:Dryland pastoralism has long attracted considerable attention from researchers in diverse fields. However, rigorous formal study is made difficult by the high level of mobility of pastoralists as well as by the sizable spatio-temporal variability of their environment. This article presents a new computational approach for studying mobile pastoralism that overcomes these issues. Combining multi-temporal satellite images and agent-based modeling allows a comprehensive examination of pastoral resource access over a realistic dryland landscape with unpredictable ecological dynamics. The article demonstrates the analytical potential of this approach through its application to mobile pastoralism in northeast Nigeria. Employing more than 100 satellite images of the area, extensive simulations are conducted under a wide array of circumstances, including different land-use constraints. The simulation results reveal complex dependencies of pastoral resource access on these circumstances along with persistent patterns of seasonal land use observed at the macro level.
Project description:Forests cover approximately one-third of Central Europe. Oak (Quercus) and European beech (Fagus sylvatica) are considered the natural dominants at low and middle elevations, respectively. Many coniferous forests (especially of Picea abies) occur primarily at midelevations, but these are thought to have resulted from forestry plantations planted over the past 200 years. Nature conservation and forestry policy seek to promote broadleaved trees over conifers. However, there are discrepancies between conservation guidelines (included in Natura 2000) and historical and palaeoecological data with regard to the distribution of conifers. Our aim was to bring new evidence to the debate on the conservation of conifers versus broadleaved trees at midelevations in Central Europe. We created a vegetation and land-cover model based on pollen data for a highland area of 11,300 km2 in the Czech Republic and assessed tree species composition in the forests before the onset of modern forestry based on 18th-century archival sources. Conifers dominated the study region throughout the entire Holocene (approximately 40-60% of the area). Broadleaved trees were present in a much smaller area than envisaged by current ideas of natural vegetation. Rather than casting doubt on the principles of Central European nature conservation in general, our results highlight the necessity of detailed regional investigations and the importance of historical data in challenging established notions on the natural distribution of tree species.
Project description:The intensive commercial exploitation of California sheephead (Semicossyphus pulcher) has become a complex, multimillion-dollar industry. The fishery is of concern because of high harvest levels and potential indirect impacts of sheephead removals on the structure and function of kelp forest ecosystems. California sheephead are protogynous hermaphrodites that, as predators of sea urchins and other invertebrates, are critical components of kelp forest ecosystems in the northeast Pacific. Overfishing can trigger trophic cascades and widespread ecological dysfunction when other urchin predators are also lost from the system. Little is known about the ecology and abundance of sheephead before commercial exploitation. Lack of a historical perspective creates a gap for evaluating fisheries management measures and marine reserves that seek to rebuild sheephead populations to historical baseline conditions. We use population abundance and size structure data from the zooarchaeological record, in concert with isotopic data, to evaluate the long-term health and viability of sheephead fisheries in southern California. Our results indicate that the importance of sheephead to the diet of native Chumash people varied spatially across the Channel Islands, reflecting modern biogeographic patterns. Comparing ancient (~10,000 calibrated years before the present to 1825 CE) and modern samples, we observed variability and significant declines in the relative abundance of sheephead, reductions in size frequency distributions, and shifts in the dietary niche between ancient and modern collections. These results highlight how size-selective fishing can alter the ecological role of key predators and how zooarchaeological data can inform fisheries management by establishing historical baselines that aid future conservation.
Project description:In the fields of ecology and conservation, taxonomic and geographic biases may compromise scientific progress. Using pollinator research as a case study, we evaluate four drivers of these biases and propose solutions to address (i) untested generalisations from highly studied taxa, (ii) information accessibility, (iii) scattered environmental regulations and (iv) restricted infrastructure and funding resources. Expanding the taxonomic, functional and geographic breadth of research and legislation, and involving scientists in policymaking, can generate greater equity, accessibility and impact of future science. Using search engines in different languages, Open Access (OA) publishing and promoting mutually beneficial collaborations between scientists from developed and developing countries, may help to overcome geographic biases in research and funding. We suggest reviewing potentially similar biases and their drivers in other branches of ecology and conservation and identifying further ways to achieve information balance in science.
Project description:Effectively supporting the United Nations' Sustainable Development Goals requires reliable, substantial, and timely data. For solar panel installation monitoring, where accurate reporting is crucial in tracking green energy production and sustainable energy access, official and regulated documentation remains inconsistent. Reports of solar panel installations have been supplemented with object detection models developed and used on openly available aerial imagery, a type of imagery collected by aircraft or drones and limited by cost, extent, and geographic location. We address these limitations by providing a solar panel dataset derived from 31 cm resolution satellite imagery to support rapid and accurate detection at regional and international scales. We also include complementary satellite imagery at 15.5 cm resolution with the aim of further improving solar panel detection accuracy. The dataset of 2,542 annotated solar panels may be used independently to develop detection models uniquely applicable to satellite imagery or in conjunction with existing solar panel aerial imagery datasets to support generalized detection models.
Project description:The whale shark Rhincodon typus is an endangered, highly migratory species with a wide, albeit patchy, distribution through tropical oceans. Ten aerial survey flights along the southern Mozambican coast, conducted between 2004-2008, documented a relatively high density of whale sharks along a 200 km stretch of the Inhambane Province, with a pronounced hotspot adjacent to Praia do Tofo. To examine the residency and movement of whale sharks in coastal areas around Praia do Tofo, where they may be more susceptible to gill net entanglement, we tagged 15 juveniles with SPOT5 satellite tags and tracked them for 2-88 days (mean = 27 days) as they dispersed from this area. Sharks travelled between 10 and 2,737 km (mean = 738 km) at a mean horizontal speed of 28 ± 17.1 SD km day-1. While several individuals left shelf waters and travelled across international boundaries, most sharks stayed in Mozambican coastal waters over the tracking period. We tested for whale shark habitat preferences, using sea surface temperature, chlorophyll-a concentration and water depth as variables, by computing 100 random model tracks for each real shark based on their empirical movement characteristics. Whale sharks spent significantly more time in cooler, shallower water with higher chlorophyll-a concentrations than model sharks, suggesting that feeding in productive coastal waters is an important driver of their movements. To investigate what this coastal habitat choice means for their conservation in Mozambique, we mapped gill nets during two dedicated aerial surveys along the Inhambane coast and counted gill nets in 1,323 boat-based surveys near Praia do Tofo. Our results show that, while whale sharks are capable of long-distance oceanic movements, they can spend a disproportionate amount of time in specific areas, such as along the southern Mozambique coast. The increasing use of drifting gill nets in this coastal hotspot for whale sharks is likely to be a threat to regional populations of this iconic species.
Project description:The worldwide growth of cancer incidence can be explained in part by changes in the prevalence and distribution of risk factors. There are geographical gaps in the estimates of cancer prevalence, which could be filled with innovative methods. We used deep learning (DL) features extracted from satellite images to predict cancer prevalence at the census tract level in seven cities in the United States. We trained the model using detailed cancer prevalence estimates from 2018 available in the CDC (Center for Disease Control) 500 Cities project. Data from 3500 census tracts covering 14,483,366 inhabitants were included. Features were extracted from 170,210 satellite images with deep learning. This method explained up to 64.37% (median = 43.53%) of the variation of cancer prevalence. Satellite features are highly correlated with individual socioeconomic and health measures that are linked to cancer prevalence (age, smoking and drinking status, and obesity). A higher similarity between two environments is associated with better generalization of the model (p = 1.10-6). This method can be used to accurately estimate cancer prevalence at a high spatial resolution without using surveys at a fraction of the cost.
Project description:Applying manure to pasture fields is a very common method of fertilization. However, rainfall can cause the manure to leach into water bodies near the field, contaminating the water and damaging the environment and the animals living in it, ultimately affecting human life. This paper presents a dataset consisting of images of 30 plots after manure application, verified by on-site investigations. This involved visiting 38 different plots, of which 8 were discarded because they were not suitable, either because of their small size, the lack of a specific manure application date, or the images being too cloudy in that period. The imagery is collected through Google Earth Engine using the satellite Sentinel-2, which offers 13 hyperspectral bands in the range of ultraviolet and near-infrared wavelengths including the visible spectrum. From these 13 bands, the most common hyperspectral indices in the literature for precision agriculture are calculated and added into the images as channels. 51 hyperspectral indices are calculated, summing up to a total of 64 channels per image when adding the raw bands from Sentinel-2. No normalization has been performed on any of the channels. The data can be used for further research of automatic classification of manure application to control its use and prevent contamination.
Project description:Advances in breeding efforts to increase the rate of genetic gains and enhance crop resilience to climate change have been limited by the procedure and costs of phenotyping methods. The recent rapid development of sensors, image-processing technology, and data-analysis has provided opportunities for multiple scales phenotyping methods and systems, including satellite imagery. Among these platforms, satellite imagery may represent one of the ultimate approaches to remotely monitor trials and nurseries planted in multiple locations while standardizing protocols and reducing costs. However, the deployment of satellite-based phenotyping in breeding trials has largely been limited by low spatial resolution of satellite images. The advent of a new generation of high-resolution satellites may finally overcome these limitations. The SkySat constellation started offering multispectral images at a 0.5 m resolution since 2020. In this communication we present a case study on the use of time series SkySat images to estimate NDVI from wheat and maize breeding plots encompassing different sizes and spacing. We evaluated the reliability of the calculated NDVI and tested its capacity to detect seasonal changes and genotypic differences. We discuss the advantages, limitations, and perspectives of this approach for high-throughput phenotyping in breeding programs.