scholarly journals The application of drones for mosquito larval habitat identification in rural environments: a practical approach for malaria control?

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Michelle C. Stanton ◽  
Patrick Kalonde ◽  
Kennedy Zembere ◽  
Remy Hoek Spaans ◽  
Christopher M. Jones
Keyword(s):  
Image Analysis ◽  
Malaria Control ◽  
Rural Areas ◽  
Near Infrared ◽  
Aquatic Vegetation ◽  
Temporal Trends ◽  
Scale Up ◽  
Larval Habitat ◽  
Habitat Characteristics ◽  
Rural Environments

Abstract Background Spatio-temporal trends in mosquito-borne diseases are driven by the locations and seasonality of larval habitat. One method of disease control is to decrease the mosquito population by modifying larval habitat, known as larval source management (LSM). In malaria control, LSM is currently considered impractical in rural areas due to perceived difficulties in identifying target areas. High resolution drone mapping is being considered as a practical solution to address this barrier. In this paper, the authors’ experiences of drone-led larval habitat identification in Malawi were used to assess the feasibility of this approach. Methods Drone mapping and larval surveys were conducted in Kasungu district, Malawi between 2018 and 2020. Water bodies and aquatic vegetation were identified in the imagery using manual methods and geographical object-based image analysis (GeoOBIA) and the performances of the classifications were compared. Further, observations were documented on the practical aspects of capturing drone imagery for informing malaria control including cost, time, computing, and skills requirements. Larval sampling sites were characterized by biotic factors visible in drone imagery and generalized linear mixed models were used to determine their association with larval presence. Results Imagery covering an area of 8.9 km2 across eight sites was captured. Larval habitat characteristics were successfully identified using GeoOBIA on images captured by a standard camera (median accuracy = 98%) with no notable improvement observed after incorporating data from a near-infrared sensor. This approach however required greater processing time and technical skills compared to manual identification. Larval samples captured from 326 sites confirmed that drone-captured characteristics, including aquatic vegetation presence and type, were significantly associated with larval presence. Conclusions This study demonstrates the potential for drone-acquired imagery to support mosquito larval habitat identification in rural, malaria-endemic areas, although technical challenges were identified which may hinder the scale up of this approach. Potential solutions have however been identified, including strengthening linkages with the flourishing drone industry in countries such as Malawi. Further consultations are therefore needed between experts in the fields of drones, image analysis and vector control are needed to develop more detailed guidance on how this technology can be most effectively exploited in malaria control.

scholarly journals The application of drones for mosquito larval habitat identification in rural environments: a practical approach for malaria control?

2020 ◽  
Author(s):  
Michelle C Stanton ◽  
Patrick Kalonde ◽  
Kennedy Zembere ◽  
Remy Hoek Spaans ◽  
Christopher M Jones
Keyword(s):  
Image Analysis ◽  
Malaria Control ◽  
Rural Areas ◽  
Aquatic Vegetation ◽  
Vegetation Type ◽  
Temporal Trends ◽  
Larval Habitat ◽  
Vegetation Coverage ◽  
List Type ◽  
Rural Environments

AbstractSpatial and temporal trends in mosquito-borne diseases are driven by the locations and seasonality of larval habitat. One method of disease control is to decrease the mosquito population by removing habitat and/or reduce the likelihood of larvae developing into adults, known as larval source management (LSM). In malaria control, LSM is currently considered impractical in rural areas due to perceived difficulties in identifying target areas. High resolution drone mapping is being considered as a practical solution to address this barrier. In this paper, we use our experiences of drone-led larval habitat identification in Malawi to assess the accuracy and practicalities of this approach.Drone imagery and larval surveys were conducted in Kasungu district, Malawi between 2018-2020. Water bodies and aquatic vegetation were identified in the imagery using both manual methods and geographical object-based image analysis (GeoOBIA) and the performance of the classifications were compared. Larval sampling sites were characterised by biotic factors visible in drone imagery (e.g. vegetation coverage, type), and generalised linear mixed models were used to determine their association with larval presence.Imagery covering an area of 8.9km2 across eight sites was captured. Characteristics associated with rural larval habitat were successfully identified using GeoOBIA (e.g. median accuracy = 0.98, median kappa = 0.96 using a standard RGB camera), with a median of 18.3% being classed as surface water, compared to 20.1% using manual identification. The GeoOBIA approach, however, required greater processing time and technical skills. Larval samples were captured from 326 sites, and a relationship was identified between larval presence and vegetation (log-OR=1.44, p=0.01). Vegetation type was also a significant factor when considering late stage anopheline larvae only.Our study demonstrates the potential for drone-acquired imagery as a tool to support the identification of mosquito larval habitat in rural areas where malaria is endemic. There are, however, technical challenges to overcome before it can be smoothly integrated into malaria control activities. Further consultations between experts and stakeholders in the fields of drones, image analysis and vector control are needed to develop more detailed guidance on how this technology can be most effectively exploited.

LARVAL HABITAT CHARACTERISTICS OF COQUILLETTIDIA PERTURBANS (DIPTERA: CULICIDAE) IN MINNESOTA

1986 ◽  
Vol 118 (11) ◽  
pp. 1193-1198 ◽  
Author(s):  
Darold P. Batzer ◽  
Robert D. Sjogren
Keyword(s):  
Dissolved Oxygen ◽  
Water Column ◽  
Plant Species ◽  
Aquatic Vegetation ◽  
Larval Habitat ◽  
Habitat Characteristics ◽  
Breeding Sites ◽  
Organic Debris ◽  
Coquillettidia Perturbans ◽  
Hennepin County

AbstractLarvae of Coquillettidia pertubans (Walker) are found in some marshes of permanent water with stands of aquatic vegetation. Eighty-six marshes, located within a 400-km2 area of Hennepin County, Minnesota, were examined in the fall of 1984 to determine factors that characterize C. perturbans breeding sites. We found that C. perturbans larvae attached to the roots of primarily Typha species although other plant species were also used. The water in sites with larvae was significantly deeper, lower in dissolved oxygen, and contained a significantly thicker layer of organic debris than sites without larvae. Larvae were associated with sites where Typha had specialized structures called water roots, which grow in the water column. Larvae inhabiting floating mats of vegetation were associated with interior openings within the mats.

scholarly journals Object-based analysis of UAS imagery to map emergent and submerged invasive aquatic vegetation: a case study

2017 ◽  
Vol 5 (1) ◽  
pp. 27-33 ◽  
Author(s):  
Dominique Chabot ◽  
Christopher Dillon ◽  
Oumer Ahmed ◽  
Adam Shemrock
Keyword(s):  
Image Analysis ◽  
Data Collection ◽  
Near Infrared ◽  
Aquatic Vegetation ◽  
Weather Conditions ◽  
Classification Performance ◽  
Unmanned Aircraft ◽  
Automated Image Analysis ◽  
Object Based

Small unmanned aircraft systems (UAS) combined with automated image analysis may provide an efficient alternative or complement to labour-intensive boat-based monitoring of invasive aquatic vegetation. A small mapping drone was assessed for collecting high-resolution (≤5 cm/pixel) true-colour and near-infrared imagery revealing the distribution of invasive water soldier (Stratiotes aloides) in the Trent–Severn Waterway, Ontario (Canada). We further evaluated the capacity of an object-based image analysis approach based on the Random Forests classification algorithm to map features in the imagery, chiefly emergent and submerged water soldier colonies. The imagery contained flaws and inconsistencies resulting from data collection in suboptimal weather conditions that likely negatively impacted classification performance. Nevertheless, our best-performing classification had a producer’s and user’s accuracy for water soldier of 81% and 74%, respectively, an overall accuracy of 78%, and a kappa value of 61%, indicating “substantial” accuracy. This trial provides an instructive case study on results achieved in a “real-world” application of a UAS for environmental monitoring, notably characterized by time constraints for data collection and analysis. Beyond avoiding data collection in unfavourable weather conditions, adaptations of the image segmentation process and use of a true discrete-band multispectral camera may help to improve classification accuracy, particularly of submerged vegetation.

scholarly journals Aedes albopictus Populations and Larval Habitat Characteristics across the Landscape: Significant Differences Exist between Urban and Rural Land Use Types

Insects ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 196
Author(s):  
Katie M. Westby ◽  
Solny A. Adalsteinsson ◽  
Elizabeth G. Biro ◽  
Alexis J. Beckermann ◽  
Kim A. Medley
Keyword(s):  
Land Use ◽  
Aedes Albopictus ◽  
Rural Areas ◽  
Mosquito Species ◽  
Larval Habitat ◽  
Habitat Characteristics ◽  
Water Evaporation ◽  
Global Changes ◽  
Local Conditions ◽  
Land Use Types

One of the most profound recent global changes has been the proliferation of urban metropolitan areas. A consequence of urbanization is a reduction in abundance, or diversity, of wildlife. One exception, is the proliferation of vectors of disease; recent years have seen the emergence and resurgence of diseases vectored by species closely associated with humans. Aedes albopictus, a mosquito with a near global range and broad ecological niche, has been described as an urban, suburban, or rural vector, or a forest edge species depending on local conditions. We tested the hypothesis that abundance and phenological patterns of this species vary among different land use types in a temperate city because of the variation in the biotic and abiotic conditions characteristic of those habitat types. A. albopictus populations in urban and suburban areas were an order of magnitude larger than in rural areas and were detected several weeks earlier in the season. Additionally, we found fewer overall mosquito species, higher temperatures, lower nitrogen, higher pH, and faster water evaporation in larval habitats in urban vs. rural areas. By understanding the ecological differences that facilitate a species in one habitat and not another, we can potentially exploit those differences for targeted control.

scholarly journals Fauna and Larval Habitat Characteristics of Mosquitoes (Diptera: Culicidae) in Kashan County, Central Iran, 2019

2021 ◽  
Author(s):  
Tahereh Sadat Asgarian ◽  
Seyed Hassan Moosa-Kazemi ◽  
Mohammad Mehdi Sedaghat ◽  
Rouhullah Dehghani ◽  
Mohammad Reza Yaghoobi-Ershadi
Keyword(s):  
Rural Areas ◽  
Mosquito Species ◽  
Morphological Characters ◽  
Larval Habitat ◽  
Habitat Characteristics ◽  
Larval Habitats ◽  
Vector Borne Diseases ◽  
Vector Borne ◽  
Urban And Rural Areas ◽  
First Time

Background: Mosquitoes are responsible for spreading devastating parasites and pathogens causing some important infectious diseases. The present study was done to better understand and update the fauna of Culicidae and to find out the distribution and the type of their larval habitats in Kashan County. Methods: This study was done in four districts of Kashan County (Central, Qamasr, Niasar and Barzok). Mosquito lar-vae were collected from 23 active larval habitats using a standard 350ml capacity mosquito dipper from April to late December 2019. The collected larvae were transferred to containers containing lactophenol, and after two weeks indi-vidually mounted in Berlese's fluid on a microscope slide and identified to species by morphological characters and valid keys. Results: In this study, a total of 9789 larvae were collected from urban and rural areas in Kashan County. The identified genera were Anopheles, Culiseta and Culex. In this study larvae of An. turkhudi, Cx. perexiguus, Cx. mimeticus, Cx. deserticola and Cs. subochrea were collected for the first time from Kashan County. Conclusion: The results of this study indicate the presence and activity of different mosquito species in Kashan County that some of them are vectors of arbovirus and other vector-borne diseases.

scholarly journals An Object-Based Image Analysis Workflow for Monitoring Shallow-Water Aquatic Vegetation in Multispectral Drone Imagery

2018 ◽  
Vol 7 (8) ◽  
pp. 294 ◽  
Author(s):  
Dominique Chabot ◽  
Christopher Dillon ◽  
Adam Shemrock ◽  
Nicholas Weissflog ◽  
Eric Sager
Keyword(s):  
Image Analysis ◽  
Near Infrared ◽  
Aquatic Vegetation ◽  
Supervised Machine Learning ◽  
Automated Classification ◽  
Infrared Band ◽  
Vegetation Monitoring ◽  
Object Based Image Analysis ◽  
Initial Separation ◽  
Object Based

High-resolution drone aerial surveys combined with object-based image analysis are transforming our capacity to monitor and manage aquatic vegetation in an era of invasive species. To better exploit the potential of these technologies, there is a need to develop more efficient and accessible analysis workflows and focus more efforts on the distinct challenge of mapping submerged vegetation. We present a straightforward workflow developed to monitor emergent and submerged invasive water soldier (Stratiotes aloides) in shallow waters of the Trent-Severn Waterway in Ontario, Canada. The main elements of the workflow are: (1) collection of radiometrically calibrated multispectral imagery including a near-infrared band; (2) multistage segmentation of the imagery involving an initial separation of above-water from submerged features; and (3) automated classification of features with a supervised machine-learning classifier. The approach yielded excellent classification accuracy for emergent features (overall accuracy = 92%; kappa = 88%; water soldier producer’s accuracy = 92%; user’s accuracy = 91%) and good accuracy for submerged features (overall accuracy = 84%; kappa = 75%; water soldier producer’s accuracy = 71%; user’s accuracy = 84%). The workflow employs off-the-shelf graphical software tools requiring no programming or coding, and could therefore be used by anyone with basic GIS and image analysis skills for a potentially wide variety of aquatic vegetation monitoring operations.

scholarly journals Desertification Risk and Rural Development in Southern Europe: Permanent Assessment and Implications for Sustainable Land Management and Mitigation Policies

Land ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 191 ◽  
Author(s):  
Rosanna Salvia ◽  
Gianluca Egidi ◽  
Sabato Vinci ◽  
Luca Salvati
Keyword(s):  
Land Use ◽  
Socioeconomic Factors ◽  
Land Degradation ◽  
Rural Areas ◽  
Agricultural Land ◽  
Temporal Trends ◽  
Economic Productivity ◽  
Landscape Degradation ◽  
Rural Environments ◽  
Patterns And Processes

The United Nations Convention to Combat Desertification defines ‘land degradation’ as a reduction or loss of the biological and economic productivity resulting from land-use mismanagement, or a combination of processes, such as soil erosion, deterioration of soil properties, and loss of natural vegetation and biodiversity. Land degradation is hence an interactive process involving multiple factors, among which climate, land-use, economic dynamics and socio-demographic forces play a key role. Especially in the Mediterranean basin, joint biophysical and socioeconomic factors shape the intrinsic level of vulnerability of both natural and agricultural land to degradation. The interplay between biophysical and socioeconomic factors may become extremely complex over time and space, resulting in specific patterns of landscape deterioration. This paper summarizes theoretical expectations and empirical knowledge in the field of soil and landscape degradation in Mediterranean Europe, evidencing the intimate relationship between agriculture and socio-demographic factors of growth (or decline) of rural areas. Understanding spatio-temporal trends of each factor underlying land degradation and the related background context is a key tool in the assessment of the spatial distribution of vulnerable and critical land to degradation. Empirical results of a permanent monitoring of land degradation contributes to delineate more effective conservation policies through identification of target areas requiring specific actions for biodiversity and landscape protection. With increasing human pressure on rural environments, a diachronic evaluation of patterns and processes of land degradation reveals particularly appropriate in a both positive and normative perspective, prefiguring new actions for soil conservation and landscape valorization under global change.

scholarly journals Predicting distribution of malaria vector larval habitats in Ethiopia by integrating distributed hydrologic modeling with remotely sensed data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ai-Ling Jiang ◽  
Ming-Chieh Lee ◽  
Guofa Zhou ◽  
Daibin Zhong ◽  
Dawit Hawaria ◽  
...  
Keyword(s):  
Malaria Vector ◽  
Malaria Control ◽  
Remotely Sensed ◽  
Larval Habitat ◽  
Validation Dataset ◽  
Significant Shift ◽  
Remotely Sensed Data ◽  
Larval Habitats ◽  
Common Land Model ◽  
The Impact

AbstractLarval source management has gained renewed interest as a malaria control strategy in Africa but the widespread and transient nature of larval breeding sites poses a challenge to its implementation. To address this problem, we propose combining an integrated high resolution (50 m) distributed hydrological model and remotely sensed data to simulate potential malaria vector aquatic habitats. The novelty of our approach lies in its consideration of irrigation practices and its ability to resolve complex ponding processes that contribute to potential larval habitats. The simulation was performed for the year of 2018 using ParFlow-Common Land Model (CLM) in a sugarcane plantation in the Oromia region, Ethiopia to examine the effects of rainfall and irrigation. The model was calibrated using field observations of larval habitats to successfully predict ponding at all surveyed locations from the validation dataset. Results show that without irrigation, at least half of the area inside the farms had a 40% probability of potential larval habitat occurrence. With irrigation, the probability increased to 56%. Irrigation dampened the seasonality of the potential larval habitats such that the peak larval habitat occurrence window during the rainy season was extended into the dry season. Furthermore, the stability of the habitats was prolonged, with a significant shift from semi-permanent to permanent habitats. Our study provides a hydrological perspective on the impact of environmental modification on malaria vector ecology, which can potentially inform malaria control strategies through better water management.

scholarly journals Monitoring the Efficacy of Crested Floatingheart (Nymphoides cristata) Management with Object-Based Image Analysis of UAS Imagery

2021 ◽  
Vol 13 (4) ◽  
pp. 830
Author(s):  
Adam R. Benjamin ◽  
Amr Abd-Elrahman ◽  
Lyn A. Gettys ◽  
Hartwig H. Hochmair ◽  
Kyle Thayer
Keyword(s):  
Image Analysis ◽  
Aquatic Vegetation ◽  
Accuracy Assessment ◽  
Unmanned Aerial Systems ◽  
Temporal Area ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Significant Difference ◽  
Additional Processing ◽  
Multispectral Sensors

This study investigates the use of unmanned aerial systems (UAS) mapping for monitoring the efficacy of invasive aquatic vegetation (AV) management on a floating-leaved AV species, Nymphoides cristata (CFH). The study site consists of 48 treatment plots (TPs). Based on six unique flights over two days at three different flight altitudes while using both a multispectral and RGB sensor, accuracy assessment of the final object-based image analysis (OBIA)-derived classified images yielded overall accuracies ranging from 89.6% to 95.4%. The multispectral sensor was significantly more accurate than the RGB sensor at measuring CFH areal coverage within each TP only with the highest multispectral, spatial resolution (2.7 cm/pix at 40 m altitude). When measuring response in the AV community area between the day of treatment and two weeks after treatment, there was no significant difference between the temporal area change from the reference datasets and the area changes derived from either the RGB or multispectral sensor. Thus, water resource managers need to weigh small gains in accuracy from using multispectral sensors against other operational considerations such as the additional processing time due to increased file sizes, higher financial costs for equipment procurements, and longer flight durations in the field when operating multispectral sensors.

scholarly journals Integrated TB and HIV care for Mozambican children: temporal trends, site-level determinants of performance, and recommendations for improved TB preventive treatment

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
W. Chris Buck ◽  
Hanh Nguyen ◽  
Mariana Siapka ◽  
Lopa Basu ◽  
Jessica Greenberg Cowan ◽  
...  
Keyword(s):  
Temporal Trends ◽  
Scale Up ◽  
Preventive Treatment ◽  
Preventive Therapy ◽  
Pediatric Hiv ◽  
Hiv Care ◽  
Pediatric Care ◽  
P Value ◽  
Missed Opportunities ◽  
Art Initiation

Abstract Background Pediatric tuberculosis (TB), human immunodeficiency virus (HIV), and TB-HIV co-infection are health problems with evidence-based diagnostic and treatment algorithms that can reduce morbidity and mortality. Implementation and operational barriers affect adherence to guidelines in many resource-constrained settings, negatively affecting patient outcomes. This study aimed to assess performance in the pediatric HIV and TB care cascades in Mozambique. Methods A retrospective analysis of routine PEPFAR site-level HIV and TB data from 2012 to 2016 was performed. Patients 0–14 years of age were included. Descriptive statistics were used to report trends in TB and HIV indicators. Linear regression was done to assess associations of site-level variables with performance in the pediatric TB and HIV care cascades using 2016 data. Results Routine HIV testing and cotrimoxazole initiation for co-infected children in the TB program were nearly optimal at 99% and 96% in 2016, respectively. Antiretroviral therapy (ART) initiation was lower at 87%, but steadily improved from 2012 to 2016. From the HIV program, TB screening at the last consultation rose steadily over the study period, reaching 82% in 2016. The percentage of newly enrolled children who received either TB treatment or isoniazid preventive treatment (IPT) also steadily improved in all provinces, but in 2016 was only at 42% nationally. Larger volume sites were significantly more likely to complete the pediatric HIV and TB care cascades in 2016 (p value range 0.05 to < 0.001). Conclusions Mozambique has made significant strides in improving the pediatric care cascades for children with TB and HIV, but there were missed opportunities for TB diagnosis and prevention, with IPT utilization being particularly problematic. Strengthened TB/HIV programming that continues to focus on pediatric ART scale-up while improving delivery of TB preventive therapy, either with IPT or newer rifapentine-based regimens for age-eligible children, is needed.

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