Aggregation as a Means of Increasing Thematic Map Accuracy

Although land use/cover maps are widely used to support management and environmental policies, only some studies have reported their accuracy using sound and complete assessments. Thematic map accuracy assessment is typically achieved by comparing reference sites labeled with the “ground-truth” category to the ones depicted in the land use/cover map. A variety of sampling designs are used to select these references sites. The estimators for accuracy indices and the variance of these estimators depend on the sampling design. However, the tools used to assess accuracy available in the main program packages compute the accuracy indices without taking into account the sampling and give inconsistent estimates. As an alternative, we present free user-friendly tools that enable users beyond the Geographic Information Science Community to compute accuracy indices and estimate corrected areas of given categories with their respective confidence intervals. The tool runs in Dinamica EGO, a free platform for environmental spatial modeling as well as a Q-GIS plugin and a R package. Additionally, a practical application example is described using a case study area in central-west Mexico.

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Practical Implications of Design-Based Sampling Inference for Thematic Map Accuracy Assessment

Remote Sensing of Environment ◽

10.1016/s0034-4257(99)00090-5 ◽

2000 ◽

Vol 72 (1) ◽

pp. 35-45 ◽

Cited By ~ 73

Author(s):

Stephen V. Stehman

Keyword(s):

Accuracy Assessment ◽

Thematic Map ◽

Practical Implications ◽

Map Accuracy Assessment ◽

Map Accuracy

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

Forests ◽

10.3390/f10010024 ◽

2019 ◽

Vol 10 (1) ◽

pp. 24 ◽

Cited By ~ 3

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.

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