1,512 research outputs found
Unmanned Aerial Vehicles (UAVs) in environmental biology: A Review
Acquiring information about the environment is a key step during each study in the field of environmental biology at different levels, from an individual species to community and biome. However, obtaining information about the environment is frequently difficult because of, for example, the phenological timing, spatial distribution of a species or limited accessibility of a particular area for the field survey. Moreover, remote sensing technology, which enables the observation of the Earthâs surface and is currently very common in environmental research, has many limitations such as insufficient spatial, spectral and temporal resolution and a high cost of data acquisition. Since the 1990s, researchers have been exploring the potential of different types of unmanned aerial vehicles (UAVs) for monitoring Earthâs surface. The present study reviews recent scientific literature dealing with the use of UAV in environmental biology. Amongst numerous papers, short communications and conference abstracts, we selected 110 original studies of how UAVs can be used in environmental biology and which organisms can be studied in this manner. Most of these studies concerned the use of UAV to measure the vegetation parameters such as crown height, volume, number of individuals (14 studies) and quantification of the spatio-temporal dynamics of vegetation changes (12 studies). UAVs were also frequently applied to count birds and mammals, especially those living in the water. Generally, the analytical part of the present study was divided into following sections: (1) detecting, assessing and predicting threats on vegetation, (2) measuring the biophysical parameters of vegetation, (3) quantifying the dynamics of changes in plants and habitats and (4) population and behaviour studies of animals. At the end, we also synthesised all the information showing, amongst others, the advances in environmental biology because of UAV application. Considering that 33% of studies found and included in this review were published in 2017 and 2018, it is expected that the number and variety of applications of UAVs in environmental biology will increase in the future
Earth observation in support of malaria control and epidemiology: MALAREO monitoring approaches
Malaria affects about half of the world's population, with the vast majority of cases occuring in Africa. National malaria control programmes aim to reduce the burden of malaria and its negative, socioeconomic effects by using various control strategies (e.g. vector control, environmental management and case tracking). Vector control is the most effective transmission prevention strategy, while environmental factors are the key parameters affecting transmission. Geographic information systems (GIS), earth observation (EO) and spatial modelling are increasingly being recognised as valuable tools for effective management and malaria vector control. Issues previously inhibiting the use of EO in epidemiology and malaria control such as poor satellite sensor performance, high costs and long turnaround times, have since been resolved through modern technology. The core goal of this study was to develop and implement the capabilities of EO data for national malaria control programmes in South Africa, Swaziland and Mozambique. High-and very high resolution (HR and VHR) land cover and wetland maps were generated for the identification of potential vector habitats and human activities, as well as geoinformation on distance to wetlands for malaria risk modelling, population density maps, habitat foci maps and VHR household maps. These products were further used for modelling malaria incidence and the analysis of environmental factors that favour vector breeding. Geoproducts were also transferred to the staff of national malaria control programmes in seven African countries to demonstrate how EO data and GIS can support vector control strategy planning and monitoring. The transferred EO products support better epidemiological understanding of environmental factors related to malaria transmission, and allow for spatio-temporal targeting of malaria control interventions, thereby improving the cost-effectiveness of interventions
Shrub of a thousand faces: an individual segmentation from satellite images using deep learning
Monitoring the distribution and size structure of long-living shrubs, such as
Juniperus communis, can be used to estimate the long-term effects of climate
change on high-mountain and high latitude ecosystems. Historical aerial
very-high resolution imagery offers a retrospective tool to monitor shrub
growth and distribution at high precision. Currently, deep learning models
provide impressive results for detecting and delineating the contour of objects
with defined shapes. However, adapting these models to detect natural objects
that express complex growth patterns, such as junipers, is still a challenging
task.
This research presents a novel approach that leverages remotely sensed RGB
imagery in conjunction with Mask R-CNN-based instance segmentation models to
individually delineate Juniperus shrubs above the treeline in Sierra Nevada
(Spain). In this study, we propose a new data construction design that consists
in using photo interpreted (PI) and field work (FW) data to respectively
develop and externally validate the model. We also propose a new shrub-tailored
evaluation algorithm based on a new metric called Multiple Intersections over
Ground Truth Area (MIoGTA) to assess and optimize the model shrub delineation
performance. Finally, we deploy the developed model for the first time to
generate a wall-to-wall map of Juniperus individuals.
The experimental results demonstrate the efficiency of our dual data
construction approach in overcoming the limitations associated with traditional
field survey methods. They also highlight the robustness of MIoGTA metric in
evaluating instance segmentation models on species with complex growth patterns
showing more resilience against data annotation uncertainty. Furthermore, they
show the effectiveness of employing Mask R-CNN with ResNet101-C4 backbone in
delineating PI and FW shrubs, achieving an F1-score of 87,87% and 76.86%,
respectively.Comment: 39 pages, 20 figure
LST-R: A method for assessing land surface temperature reduction in urban, hot and semi-arid Global South
Over the next 30 years, temperatures are expected to increase in hot semi-arid zones. Despite increasing studies on urban heat, cooling measures suitable for this climate zone remain poorly investigated. The proposed method is innovative because it focuses on significant landscape metrics for determining the land surface temperature (LST) and evaluating cooling measures. Recurrence of warm spells was identified analysing the daily air temperatures. Daytime and night-time LST data acquired from space were correlated with landscape metrics extracted from very high-resolution satellite imagery. Stepwise linear regression was used to identify the significant metrics that affected it. Cooling measures were selected considering implementation leeway; performance of existing measures; strengths, weaknesses, opportunities, and threats, equity analyses. Although the method was tested in Niamey, Niger, it can be applied to any city or town in hot semi-arid Global South, requiring decision-making support on cooling policies.
âą Landscape metrics are consistent with development standard and general requirements
âą Evaluation of measures to reduce land surface temperature includes expertsâ advice
âą Equity of measures to reduce land surface temperature is considere
Linking agents, patterns and outcomes of forest disturbances to understand pathways of degradation in the Argentine Dry Chaco
Tropische TrockenwĂ€lder sind von groĂer Bedeutung fĂŒr das Klima, die biologische Vielfalt und den Lebensunterhalt von Millionen von Menschen. Die Walddegradation bedroht die tropischen TrockenwĂ€lder, aber es fehlt an Wissen ĂŒber ihre Muster, ihr AusmaĂ und ihre Ursachen. Ziel dieser Arbeit war es, das derzeitige VerstĂ€ndnis der Walddegradation im argentinischen Dry Chaco mit Hilfe der Fernerkundung zu verbessern. Mithilfe des Landsat-Archivs habe ich die Störungsgeschichte des verbleibenden Waldes charakterisiert, die rĂ€umlichen und zeitlichen Muster der Störungsfaktoren bewertet und die langfristigen Auswirkungen der verschiedenen Faktoren auf die Waldstruktur untersucht. Die Ergebnisse zeigen, dass ĂŒber 30 Jahre hinweg groĂe Gebiete des argentinischen Dry Chaco (etwa 8 %) von Störungen betroffen waren. Meine Ergebnisse zeigen einen anthropogenen Zusammenhang mit den meisten Störungsarten, deuten aber auch auf einen komplexen indirekten Einfluss von Niederschlagsmustern hin, wobei Waldstörungen in DĂŒrrejahren besonders verbreitet sind. Die Analyse der zeitlichen Muster der verschiedenen Einwirkungen zeigt Trends in der Landnutzung im Laufe der Zeit, wobei neue Landnutzungsformen wie silvopastorale Systeme entstehen und alte Praktiken wie die Abholzung jedes Jahr einen relativ stabilen Anteil der FlĂ€chen betreffen. Die Ergebnisse zu den langfristigen Auswirkungen von Störungen zeigen, dass sich die Waldstruktur bei den am weitesten verbreiteten Störungen ĂŒber drei Jahrzehnte kaum oder gar nicht erholt, was auf eine groĂflĂ€chige Walddegradation schlieĂen lĂ€sst. Diese Arbeit zeigt das Potenzial von Satellitenzeitreihen fĂŒr eine robuste Charakterisierung der Walddynamik im Zusammenhang mit der Degradation auch in tropischen TrockenwĂ€ldern. Die aus dieser Arbeit resultierenden Karten, AnsĂ€tze und Erkenntnisse tragen zu einem besseren VerstĂ€ndnis der Walddegradation im Dry Chaco bei und können zu einem wirksameren Schutz der tropischen TrockenwĂ€lder beitragen.Tropical dry forests are of great importance for climate regulation, harbour biodiversity and sustain the livelihood of millions of people. Deforestation and degradation threaten tropical dry forests but whereas our understanding of tropical deforestation has increased tremendously over the last decades, knowledge of the patterns, extent and drivers of forest degradation is lacking. This thesis aimed to advance the current understanding of forest degradation in the Dry Chaco by means of remote sensing. Using the Landsat archive, I characterized the disturbance history of the remaining Argentine Dry Chaco forest, assessed spatial and temporal patterns of disturbance agents, and investigated the long-term effect of different agents on forest structure. Results show that over 30 years large areas of the Argentine Dry Chaco (about 8%) were affected by disturbances. My findings reveal an anthropogenic link to most types of disturbances, while also suggesting complex indirect influence of precipitation patterns, with forest disturbances being particularly widespread during drought years. The analyses of temporal patterns of different agents reveals trends in land-use practices over time, with new land uses emerging, such as silvopastoral systems, and old practices such as logging, affecting a fairly stable share of areas every year. Findings on the long-term impact of disturbances indicate that for the most widespread disturbances, forest structure shows little or no recovery over three decades, which suggests forest degradation affecting large areas. This thesis demonstrates the potential of satellite time series for robust characterization of forest dynamics related to degradation also in tropical dry forests, despite the complex conditions these systems represent. The maps, approaches and knowledge resulting from this thesis contribute to a better understanding of forest degradation in the Dry Chaco and can inform more effective conservation of tropical dry forests
The potential of historical spy-satellite imagery to support research in ecology and conservation
Remote sensing data are important for assessing ecological change, but their value is often restricted by their limited temporal coverage. Major historical events that affected the environment, such as those associated with colonial history, World War II, or the Green Revolution are not captured by modern remote sensing. In the present article, we highlight the potential of globally available black-and-white satellite photographs to expand ecological and conservation assessments back to the 1960s and to illuminate ecological concepts such as shifting baselines, time-lag responses, and legacy effects. This historical satellite photography can be used to monitor ecosystem extent and structure, speciesâ populations and habitats, and human pressures on the environment. Even though the data were declassified decades ago, their use in ecology and conservation remains limited. But recent advances in image processing and analysis can now unlock this research resource. We encourage the use of this opportunity to address important ecological and conservation questions
Assessing the performance of machine learning algorithms in Google Earth Engine for land use and land cover analysis: A case study of MuÄla province, TĂŒrkiye
Regions with high tourism density are very sensitive to human activities. Ensuring sustainability by preserving the cultural characteristics and natural structure of these regions is of critical importance in order to transfer these assets to the future world heritage. Detecting and mapping changes in land use and land cover (LULC) using innovative methods within short time intervals are of great importance for both monitoring the regional change and making administrative planning by taking necessary measures in a timely manner. In this context, this study focuses on the creation of a 4-class LULC map of MuÄla province over the Google Earth Engine (GEE) platform by utilizing three different machine learning algorithms, namely, Support Vector Machines (SVM), Random Forest (RF), and Classification and Regression Tree (CART), and on comparison of their accuracy assessments. For improved classification accuracy, as well with the Sentinel-2 and Landsat-8 satellite images, the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) are also derived and used in classification of the major land use classes, which are âbuilt-up area & barren landâ, âdense vegetationâ, âwater surfaceâ, and âshrub, grassland & sparse vegetationâ. Experimental results show that the most relevant algorithm is RF with 0.97 overall accuracy and 0.96 Kappa value, followed by SVM and CART algorithms, respectively. These results indicate that the RF classifier outperforms both SVM and CART classifiers in terms of accuracy. Moreover, based on the results of the RF classifier, 19% (2,429 km2) of the study region is classified as built-up area & barren land, 48% (6,135 km2) as dense vegetation, 2% (301 km2) as water surface and 30% (3,832 km2) as shrub, grassland & sparse vegetation class
Mapping historical forest biomass for stock-change assessments at parcel to landscape scales
Understanding historical forest dynamics, specifically changes in forest
biomass and carbon stocks, has become critical for assessing current forest
climate benefits and projecting future benefits under various policy,
regulatory, and stewardship scenarios. Carbon accounting frameworks based
exclusively on national forest inventories are limited to broad-scale
estimates, but model-based approaches that combine these inventories with
remotely sensed data can yield contiguous fine-resolution maps of forest
biomass and carbon stocks across landscapes over time. Here we describe a
fundamental step in building a map-based stock-change framework: mapping
historical forest biomass at fine temporal and spatial resolution (annual, 30m)
across all of New York State (USA) from 1990 to 2019, using freely available
data and open-source tools.
Using Landsat imagery, US Forest Service Forest Inventory and Analysis (FIA)
data, and off-the-shelf LiDAR collections we developed three modeling
approaches for mapping historical forest aboveground biomass (AGB): training on
FIA plot-level AGB estimates (direct), training on LiDAR-derived AGB maps
(indirect), and an ensemble averaging predictions from the direct and indirect
models. Model prediction surfaces (maps) were tested against FIA estimates at
multiple scales. All three approaches produced viable outputs, yet tradeoffs
were evident in terms of model complexity, map accuracy, saturation, and
fine-scale pattern representation. The resulting map products can help identify
where, when, and how forest carbon stocks are changing as a result of both
anthropogenic and natural drivers alike. These products can thus serve as
inputs to a wide range of applications including stock-change assessments,
monitoring reporting and verification frameworks, and prioritizing parcels for
protection or enrollment in improved management programs.Comment: Manuscript: 24 pages, 7 figures; Supplements: 12 pages, 5 figures;
Submitted to Forest Ecology and Managemen
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