Using Remote Sensing to Map the Risk of Human Monkeypox Virus in the Congo Basin

Although the incidence of human monkeypox has greatly increased in Central Africa over the last decade, resources for surveillance remain extremely limited. We conducted a geospatial analysis using existing data to better inform future surveillance efforts. Using active surveillance data collected between 2005 and 2007, we identified locations in Sankuru district, Democratic Republic of Congo (DRC) where there have been one or more cases of human monkeypox. To assess what taxa constitute the main reservoirs of monkeypox, we tested whether human cases were associated with (i) rope squirrels (Funisciurus sp.), which were implicated in monkeypox outbreaks elsewhere in the DRC in the 1980s, or (ii) terrestrial rodents in the genera Cricetomys and Graphiurus, which are believed to be monkeypox reservoirs in West Africa. Results suggest that the best predictors of human monkeypox cases are proximity to dense forests and associated habitat preferred by rope squirrels. The risk of contracting monkeypox is significantly greater near sites predicted to be habitable for squirrels (OR?=?1.32; 95% CI 1.08-1.63). We recommend that semi-deciduous rainforests with oil-palm, the rope squirrel's main food source, be prioritized for monitoring.     

GIS and Remote Sensing for Detecting Yield Loss in Cranberry Culture

The primary goal of our research is to develop key elements of a precision agriculture program applicable to high-value woody perennial crops, such as cranberries. These crop systems exhibit tremendous variability in crop yields and quality as imposed by variations in soil properties (water availability and nutrient deficiency) that lead to crop stress (disease development and weed competition). Some of the variability present in the growing environment results in persistent yield losses as well as crop-quality reductions. We are using state-of-the-art methodologies (GIS, GPS, remote sensing) to identify and map spatial variations of the crop. Through image-processing methods (NDVI and unsupervised classification), approximately 65% of the variation in yield was described using 4-m multispectral satellite data as a base image.     

Identification of Priority Conservation Areas and Potential Corridors for Jaguars in the Caatinga Biome,Brazil

The jaguar, Panthera onca, is a top predator with the extant population found within the Brazilian Caatinga biome now known to be on the brink of extinction. Designing new conservation units and potential corridors are therefore crucial for the long-term survival of the species within the Caatinga biome. Thus, our aims were: 1) to recognize suitable areas for jaguar occurrence, 2) to delineate areas for jaguar conservation (PJCUs), 3) to design corridors among priority areas, and 4) to prioritize PJCUs. A total of 62 points records of jaguar occurrence and 10 potential predictors were analyzed in a GIS environment. A predictive distributional map was obtained using Species Distribution Modeling (SDM) as performed by the Maximum Entropy (Maxent) algorithm. Areas equal to or higher than the median suitability value of 0.595 were selected as of high suitability for jaguar occurrence and named as Priority Jaguar Conservation Units (PJCU). Ten PJCUs with sizes varying from 23.6 km² to 4,311.0 km² were identified. Afterwards, we combined the response curve, as generated by SDM, and expert opinions to create a permeability matrix and to identify least cost corridors and buffer zones between each PJCU pair. Connectivity corridors and buffer zone for jaguar movement included an area of 8.884,26 km² and the total corridor length is about 160.94 km. Prioritizing criteria indicated the PJCU representing c.a. 68.61% of the total PJCU area (PJCU # 1) as of high priority for conservation and connectivity with others PJCUs (PJCUs # 4, 5 and 7) desirable for the long term survival of the species. In conclusion, by using the jaguar as a focal species and combining SDM and expert opinion we were able to create a valid framework for practical conservation actions at the Caatinga biome. The same approach could be used for the conservation of other carnivores.     

Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting.     

Using Real-Time PCR to Assess Changes in the Hydrocarbon-Degrading Microbial Community in Antarctic Soil During Bioremediation

A real-time polymerase chain reaction (PCR) method to quantify the proportion of microorganisms containing alkane monooxygenase was developed and used to follow changes in the microbial community in hydrocarbon-contaminated Antarctic soil during a bioremediation field trial. Assays for the alkB and rpoB genes were validated and found to be both sensitive and reproducible (less than 2% intrarun variation and 25–38% interrun variation). Results from the real-time PCR analysis were compared to analysis of the microbial population by a culture-based technique [most probable number (MPN) counts]. Both types of analysis indicated that fertilizer addition to hydrocarbon-contaminated soil stimulated the indigenous bacterial population within 1 year. The proportion of alkB containing microorganisms was positively correlated to the concentration of n-alkanes in the soil. After the concentration of n-alkanes in the soil decreased, the proportion of alkane-degrading microorganisms decreased, but the proportion of total hydrocarbon-degrading microorganisms increased, indicating another shift in the microbial community structure and ongoing biodegradation.     

Using Satellite Remote Sensing to Estimate the Colored Dissolved Organic Matter Absorption Coefficient in Lakes

Given the importance of colored dissolved organic matter (CDOM) for the structure and function of lake ecosystems, a method that could estimate the amount of CDOM in lake waters over large geographic areas would be highly desirable. Satellite remote sensing has the potential to resolve this problem. We carried out model simulations to evaluate the suitability of different satellite sensors (Landsat, IKONOS, and the Advanced land Imager [ALI]) to map the amount of CDOM in concentration ranges that occur in boreal lakes of the Nordic countries. The results showed that the 8-bit radiometric resolution of Landsat 7 is not adequate when absorption by CDOM at 420 nm is higher than 3 m⁻¹. On the other hand, the 16-bit radiometric resolution of ALI, a prototype of the next generation of Landsat, is suitable for mapping CDOM in a wider range of concentrations. An ALI image of southern Finland was acquired on 14, July 2002 and in situ measurements were carried out in 15 lakes (18 stations). The results showed that there is a high correlation (R² = 0.84) between the 565 nm/660 nm ALI band ratio and the CDOM absorption coefficient in lakes. Analysis of 245 lakes in the acquired satellite image showed a normal distribution of CDOM concentration among the lakes. However, the size distribution of lakes was highly skewed toward small lakes, resulting in the CDOM concentration per unit lake area being skewed toward high values. We showed that remote sensing enables synoptic monitoring of the CDOM concentration in a large number of lakes and thus enables scaling up to the level of large ecosystems and biomes.     

应用遥感技术研究土地利用/土地覆盖变化及其对土壤侵蚀过程的影响--以印度北部Pali Gad山地流域为例

应用遥感技术评估了印度北部Pali Gad山地流域过去几十年里土地利用／土地覆盖变化及其造成的土壤侵蚀程度,并基于摩根参数模型（Morgan Parametric Model）的方法来测定土壤的侵蚀程度;结果表明,由于不同的坡向受到太阳光照的不同可以引起土地覆盖的变迁;海拔和坡度已不再是阻碍人们获取自然资源的因素,人们的活动范围正转移到更高的海拔和更陡峭的坡度;揭示了土地利用／土地覆盖变化对土壤侵蚀进程有着直接的影响。     

Using Poaching Levels and Elephant Distribution to Assess the Conservation Efficacy of Private,Communal and Government Land in Northern Kenya

Efforts to curb elephant poaching have focused on reducing demand, confiscating ivory and boosting security patrols in elephant range. Where land is under multiple uses and ownership, determining the local poaching dynamics is important for identifying successful conservation models. Using 2,403 verified elephant, Loxodonta africana, mortality records collected from 2002 to 2012 and the results of aerial total counts of elephants conducted in 2002, 2008 and 2012 for the Laikipia-Samburu ecosystem of northern Kenya, we sought to determine the influence of land ownership and use on diurnal elephant distribution and on poaching levels. We show that the annual proportions of illegally killed (i.e., poached) elephants increased over the 11 years of the study, peaking at 70% of all recorded deaths in 2012. The type of land use was more strongly related to levels of poaching than was the type of ownership. Private ranches, comprising only 13% of land area, hosted almost half of the elephant population and had significantly lower levels of poaching than other land use types except for the officially designated national reserves (covering only 1.6% of elephant range in the ecosystem). Communal grazing lands hosted significantly fewer elephants than expected, but community areas set aside for wildlife demonstrated significantly higher numbers of elephants and lower illegal killing levels relative to non-designated community lands. While private lands had lower illegal killing levels than community conservancies, the success of the latter relative to other community-held lands shows the importance of this model of land use for conservation. This work highlights the relationship between illegal killing and various land ownership and use models, which can help focus anti-poaching activities.     

Assessing Vegetation Cover Dynamics Induced by Policy-Driven Ecological Restoration and Implication to Soil Erosion in Southern China

In the aftermath of the severe droughts and floods at the end of the 20th century, the Chinese government launched several ecological restoration projects, including the Natural Forest Protection Program in 1998 and the Grain-for-Green Program in 1999, to promote afforestation and reforestation to reduce surface runoff and consequent soil erosion nationwide. However, it is still unclear how vegetation has changed in southern China since the launch of these programs. In this study, we used the MODIS Enhanced Vegetation Index (EVI) to analyze the vegetation cover dynamics in southern China from 2000 to 2009 and evaluate the resulting effects of controlling soil erosion. Our observations indicate that 5.3% of the study area significantly increased and 0.98% significantly decreased in EVI value (p < 0.05). The spring EVI had largest increase in space. The conversions of croplands on steep slopes to forests resulting from national policies led to significant increases in EVI. The increase in EVI was not driven by annual average temperature and annual precipitation. By referencing ecological restoration statistical data and field observations, we showed that ecological restoration programs significantly improved vegetation cover in southern China. Increase in the area of farmland-converted forestlands has reduced soil erosion based upon monitoring sediment yields at hydrologic stations in the Yangtze River. This study displays the spatial patterns of trend in vegetation growth since the beginning of the 21st century in southern China and highlights the important role of China’s afforestation program.     

A Model-Based Study of Phytoplankton Condition Using Remote Sensing Data for the Western Kamchatka Shelf

Russian Journal of Marine Biology - A method for estimating the integral microalgae biomass beneath a unit surface was designed based on a mathematical model for phytoplankton vital function in the...     

Using the RESRAD Code to Assess Human Exposure Risk to 226Ra, 232Th,and 40K in Soil

This article evaluates the health risk raised by exposure to naturally occurring radionuclides in soil around Khak-Sefid, Ramsar, Iran, which is an area of high natural background radiation. A high purity germanium detector was used to determine levels of radionuclides in soil samples and the cancer morbidity risk for a hypothetical resident farmer was evaluated using the RESidual RADioactivity (RESRAD) code. The average activity concentrations of ²²⁶Ra, ²³²Th, and ⁴⁰K were found to be 13,201 ± 391, 27.9 ± 2.4, and 415.5 ± 16 Bq/kg, respectively. The maximum assessed cancer morbidity risks were calculated from external and internal exposure pathways as 4.73 × 10^?2 and 3.40 × 10^?2 for ²²⁶Ra, 1.41 × 10^?4 and 7.88 × 10^?5 for ²³²Th, and 1.3 × 10^?4 and 4.233 × 10^?4 for ⁴⁰K. The RESRAD calculations also showed total cancer morbidity risks from external gamma and plant ingestion pathways were more important than from other exposure pathways. A sensitivity analysis was also performed to determine the input parameter values in the risk assessment process. In general, due to the high calculated risk of ²²⁶Ra compared with ²³²Th and ⁴⁰K it can be the major source of concern for human heath in the study area.     

Using Range-Wide Abundance Modeling to Identify Key Conservation Areas for the Micro-Endemic Bolson Tortoise (Gopherus flavomarginatus)

A widespread biogeographic pattern in nature is that population abundance is not uniform across the geographic range of species: most occurrence sites have relatively low numbers, whereas a few places contain orders of magnitude more individuals. The Bolson tortoise Gopherus flavomarginatus is endemic to a small region of the Chihuahuan Desert in Mexico, where habitat deterioration threatens this species with extinction. In this study we combined field burrows counts and the approach for modeling species abundance based on calculating the distance to the niche centroid to obtain range-wide abundance estimates. For the Bolson tortoise, we found a robust, negative relationship between observed burrows abundance and distance to the niche centroid, with a predictive capacity of 71%. Based on these results we identified four priority areas for the conservation of this microendemic and threatened tortoise. We conclude that this approach may be a useful approximation for identifying key areas for sampling and conservation efforts in elusive and rare species.     

The Ethnography of Landscape: GIS and Remote Sensing in the Study of Forest Change in West African Guinea Savanna

In this paper we explore the application of Amazonian forest change models to the Guinea savanna environment of western Africa. In particular, we examine recent research documenting the growth of peri-village forest islands in the area of Kissidougou, Guinea, which purports to challenge the pervasive view of Guinea savanna deforestation brought about by human activity. As a more systematic approach to the analysis of human/environmental interactions, we propose an "ethnography of landscape," in which the findings of detailed, local ethnographic and ecological case studies are combined with remotely sensed information on what the wider region looks like as it changes over time. In pursuing this research, we employ aerial photography and Geographic Information Systems (GIS) techniques to examine our study area of Kilimi in northwestern Sierra Leone. We show that a Kissidougou-type model of peri-village forest island growth is not applicable to this region, where other environmental change processes are underway. In future work, we propose to employ Landsat satellite image archives to examine, monitor, and compare processes of ecological change throughout the Guinea savanna. [deforestation, ecological anthropology, remote sensing, Sierra Leone, West Africa]     

The Use of Genus-Specific Amplicon Pyrosequencing to Assess Phytophthora Species Diversity Using eDNA from Soil and Water in Northern Spain

Phytophthora is one of the most important and aggressive plant pathogenic genera in agriculture and forestry. Early detection and identification of its pathways of infection and spread are of high importance to minimize the threat they pose to natural ecosystems. eDNA was extracted from soil and water from forests and plantations in the north of Spain. Phytophthora-specific primers were adapted for use in high-throughput Sequencing (HTS). Primers were tested in a control reaction containing eight Phytophthora species and applied to water and soil eDNA samples from northern Spain. Different score coverage threshold values were tested for optimal Phytophthora species separation in a custom-curated database and in the control reaction. Clustering at 99% was the optimal criteria to separate most of the Phytophthora species. Multiple Molecular Operational Taxonomic Units (MOTUs) corresponding to 36 distinct Phytophthora species were amplified in the environmental samples. Pyrosequencing of amplicons from soil samples revealed low Phytophthora diversity (13 species) in comparison with the 35 species detected in water samples. Thirteen of the MOTUs detected in rivers and streams showed no close match to sequences in international sequence databases, revealing that eDNA pyrosequencing is a useful strategy to assess Phytophthora species diversity in natural ecosystems.     

A Method for the Evaluation of Image Quality According to the Recognition Effectiveness of Objects in the Optical Remote Sensing Image Using Machine Learning Algorithm

Objective and effective image quality assessment (IQA) is directly related to the application of optical remote sensing images (ORSI). In this study, a new IQA method of standardizing the target object recognition rate (ORR) is presented to reflect quality. First, several quality degradation treatments with high-resolution ORSIs are implemented to model the ORSIs obtained in different imaging conditions; then, a machine learning algorithm is adopted for recognition experiments on a chosen target object to obtain ORRs; finally, a comparison with commonly used IQA indicators was performed to reveal their applicability and limitations. The results showed that the ORR of the original ORSI was calculated to be up to 81.95%, whereas the ORR ratios of the quality-degraded images to the original images were 65.52%, 64.58%, 71.21%, and 73.11%. The results show that these data can more accurately reflect the advantages and disadvantages of different images in object identification and information extraction when compared with conventional digital image assessment indexes. By recognizing the difference in image quality from the application effect perspective, using a machine learning algorithm to extract regional gray scale features of typical objects in the image for analysis, and quantitatively assessing quality of ORSI according to the difference, this method provides a new approach for objective ORSI assessment.     

Integrating Remote Sensing and GIS for Prediction of Winter Wheat (Triticum aestivum) Protein Contents in Linfen (Shanxi), China

In this study, relationships between normalized difference vegetation index (NDVI) and plant (winter wheat) nitrogen content (PNC) and between PNC and grain protein content (GPC) were investigated using multi-temporal moderate-resolution imaging spectroradiometer (MODIS) data at the different stages of winter wheat in Linfen (Shanxi, P. R. China). The anticipating model for GPC of winter wheat was also established by the approach of NDVI at the different stages of winter wheat. The results showed that the spectrum models of PNC passed F test. The NDVI_4.14 regression effect of PNC model of irrigated winter wheat was the best, and that in dry land was NDVI_4.30. The PNC of irrigated and dry land winter wheat were significantly (P<0.01) and positively correlated to GPC. Both of protein spectral anticipating model of irrigated and dry land winter wheat passed a significance test (P<0.01). Multiple anticipating models (MAM) were established by NDVI from two periods of irrigated and dry land winter wheat and PNC to link GPC anticipating model. The coefficient of determination R² (R) of MAM was greater than that of the other two single-factor models. The relative root mean square error (R_RMSE) and relative error (RE) of MAM were lower than those of the other two single-factor models. Therefore, test effects of multiple proteins anticipating model were better than those of single-factor models. The application of multiple anticipating models for predication of protein content (PC) of irrigated and dry land winter wheat was more accurate and reliable. The regionalization analysis of GPC was performed using inverse distance weighted function of GIS, which is likely to provide the scientific basis for the reasonable winter wheat planting in Linfen city, China.     

Modeling Spatial Patterns of Soil Respiration in Maize Fields from Vegetation and Soil Property Factors with the Use of Remote Sensing and Geographical Information System

To examine the method for estimating the spatial patterns of soil respiration (R_s) in agricultural ecosystems using remote sensing and geographical information system (GIS), R_s rates were measured at 53 sites during the peak growing season of maize in three counties in North China. Through Pearson''s correlation analysis, leaf area index (LAI), canopy chlorophyll content, aboveground biomass, soil organic carbon (SOC) content, and soil total nitrogen content were selected as the factors that affected spatial variability in R_s during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected R_s. Meanwhile, other factors indirectly affected R_s through LAI and SOC content. When three greenness vegetation indices were extracted from an optical image of an environmental and disaster mitigation satellite in China, enhanced vegetation index (EVI) showed the best correlation with LAI and was thus used as a proxy for LAI to estimate R_s at the regional scale. The spatial distribution of SOC content was obtained by extrapolating the SOC content at the plot scale based on the kriging interpolation method in GIS. When data were pooled for 38 plots, a first-order exponential analysis indicated that approximately 73% of the spatial variability in R_s during the peak growing season of maize can be explained by EVI and SOC content. Further test analysis based on independent data from 15 plots showed that the simple exponential model had acceptable accuracy in estimating the spatial patterns of R_s in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R² of 0.69 and root-mean-square error of 0.51 µmol CO₂ m⁻² s⁻¹. The conclusions from this study provide valuable information for estimates of R_s during the peak growing season of maize in three counties in North China.