scholarly journals Evaluating the Effectiveness of Unmanned Aerial Systems (UAS) for Collecting Thematic Map Accuracy Assessment Reference Data in New England Forests

Forests ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 24 ◽  
Author(s):  
Benjamin T. Fraser ◽  
Russell G. Congalton
Keyword(s):  
New England ◽  
Reference Data ◽  
Accuracy Assessment ◽  
Unmanned Aerial Systems ◽  
Complex Environments ◽  
Thematic Map ◽  
Object Based ◽  
Aerial Systems ◽  
Map Accuracy Assessment ◽  
Map Accuracy

Thematic mapping provides today’s analysts with an essential geospatial science tool for conveying spatial information. The advancement of remote sensing and computer science technologies has provided classification methods for mapping at both pixel-based and object-based analysis, for increasingly complex environments. These thematic maps then serve as vital resources for a variety of research and management needs. However, to properly use the resulting thematic map as a decision-making support tool, an assessment of map accuracy must be performed. The methods for assessing thematic accuracy have coalesced into a site-specific multivariate analysis of error, measuring uncertainty in relation to an established reality known as reference data. Ensuring statistical validity, access and time constraints, and immense costs limit the collection of reference data in many projects. Therefore, this research proposes evaluating the feasibility of adopting the low-cost, flexible, high-resolution sensor-capable Unmanned Aerial Systems (UAS, UAV, or Drone) platform for collecting reference data to use in thematic map accuracy assessments for complex environments. This pilot study analyzed 377.57 ha of New England forests, over six University of New Hampshire woodland properties, to compare the similarity between UAS-derived orthomosaic samples and ground-based continuous forest inventory (CFI) plot classifications of deciduous, mixed, and coniferous forest cover types. Using an eBee Plus fixed-wing UAS, 9173 images were acquired and used to create six comprehensive orthomosaics. Agreement between our UAS orthomosaics and ground-based sampling forest compositions reached 71.43% for pixel-based classification and 85.71% for object-based classification reference data methods. Despite several documented sources of uncertainty or error, this research demonstrated that UAS are capable of highly efficient and effective thematic map accuracy assessment reference data collection. As UAS hardware, software, and implementation policies continue to evolve, the potential to meet the challenges of accurate and timely reference data collection will only increase.

Design and Analysis for Thematic Map Accuracy Assessment

1998 ◽  
Vol 64 (3) ◽  
pp. 331-344 ◽  
Author(s):  
Stephen V. Stehman ◽  
Raymond L. Czaplewski
Keyword(s):  
Accuracy Assessment ◽  
Thematic Map ◽  
Map Accuracy Assessment ◽  
Map Accuracy

scholarly journals AN ASSESSMENT OF CITIZEN CONTRIBUTED GROUND REFERENCE DATA FOR LAND COVER MAP ACCURACY ASSESSMENT

2015 ◽  
Vol II-3/W5 ◽  
pp. 219-225 ◽  
Author(s):  
G. M. Foody
Keyword(s):  
Best Practices ◽  
Land Cover ◽  
Reference Data ◽  
Accuracy Assessment ◽  
Good Practice ◽  
Thematic Mapping ◽  
Gold Standard Reference ◽  
Land Cover Map ◽  
Map Accuracy Assessment ◽  
Map Accuracy

It is now widely accepted that an accuracy assessment should be part of a thematic mapping programme. Authoritative good or best practices for accuracy assessment have been defined but are often impractical to implement. Key reasons for this situation are linked to the ground reference data used in the accuracy assessment. Typically, it is a challenge to acquire a large sample of high quality reference cases in accordance to desired sampling designs specified as conforming to good practice and the data collected are normally to some degree imperfect limiting their value to an accuracy assessment which implicitly assumes the use of a gold standard reference. Citizen sensors have great potential to aid aspects of accuracy assessment. In particular, they may be able to act as a source of ground reference data that may, for example, reduce sample size problems but concerns with data quality remain. The relative strengths and limitations of citizen contributed data for accuracy assessment are reviewed in the context of the authoritative good practices defined for studies of land cover by remote sensing. The article will highlight some of the ways that citizen contributed data have been used in accuracy assessment as well as some of the problems that require further attention, and indicate some of the potential ways forward in the future.

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 Species-Level Vegetation Mapping in a Himalayan Treeline Ecotone Using Unmanned Aerial System (UAS) Imagery

2018 ◽  
Vol 7 (11) ◽  
pp. 445 ◽  
Author(s):  
Niti Mishra ◽  
Kumar Mainali ◽  
Bharat Shrestha ◽  
Jackson Radenz ◽  
Debendra Karki
Keyword(s):  
Extreme Environments ◽  
Species Level ◽  
Future Research ◽  
Unmanned Aerial Systems ◽  
Object Based ◽  
Ecological Patterns ◽  
Geographic Object ◽  
The Cost ◽  
Aerial Systems ◽  
The Himalaya

Understanding ecological patterns and response to climate change requires unbiased data on species distribution. This can be challenging, especially in biodiverse but extreme environments like the Himalaya. This study presents the results of the first ever application of Unmanned Aerial Systems (UAS) imagery for species-level mapping of vegetation in the Himalaya following a hierarchical Geographic Object Based Image Analysis (GEOBIA) method. The first level of classification separated green vegetated objects from the rest with overall accuracy of 95%. At the second level, seven cover types were identified (including four woody vegetation species). For this, the suitability of various spectral, shape and textural features were tested for classifying them using an ensemble decision tree algorithm. Spectral features alone yielded ~70% accuracy (kappa 0.66) whereas adding textural and shape features marginally improved the accuracy (73%) but at the cost of a substantial increase in processing time. Contrast in plant morphological traits was the key to distinguishing nearby stands as different species. Hence, broad-leaved versus fine needle leaved vegetation were mapped more accurately than structurally similar classes such as Rhododendron anthopogon versus non-photosynthetic vegetation. Results highlight the potential and limitations of the suggested UAS-GEOBIA approach for detailed mapping of plant communities and suggests future research directions.

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