Spatial Stratification Method for the Sampling Design of LULC Classification Accuracy Assessment: A Case Study in Beijing, China

Dong, Shiwei; Guo, Hui; Chen, Ziyue; Pan, Yuchun; Gao, Bingbo

doi:10.3390/rs14040865

Cited by 11 publications

(4 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Water bodies were additionally stratified by size and type to ensure that the assessment was representative of the diversity of water bodies present in the study area. Spatial stratification also improves the assessment of the classification accuracy of remote sensing data (Dong et al, 2022 ). Spatial autocorrelation examines how pixels that are close together are more similar than those that are far apart (Karasiak et al, 2022 ), resulting in falsely high precision metrics (Roberts et al 2017 ; Meyer et al 2019 ).…”

Section: Methodsmentioning

confidence: 99%

Performance of water indices for large-scale water resources monitoring using Sentinel-2 data in Ethiopia

Tesfaye,

Breuer

2024

Environ Monit Assess

View full text Add to dashboard Cite

Evaluating the performance of water indices and water-related ecosystems is crucial for Ethiopia. This is due to limited information on the availability and distribution of water resources at the country scale, despite its critical role in sustainable water management, biodiversity conservation, and ecosystem resilience. The objective of this study is to evaluate the performance of seven water indices and select the best-performing indices for detecting surface water at country scale. Sentinel-2 data from December 1, 2021, to November 30, 2022, were used for the evaluation and processed using the Google Earth Engine. The indices were evaluated using qualitative visual inspection and quantitative accuracy indicators of overall accuracy, producer’s accuracy, and user’s accuracy. Results showed that the water index (WI) and automatic water extraction index with shadow (AWEIsh) were the most accurate ones to extract surface water. For the latter, WI and AWEIsh obtained an overall accuracy of 96% and 95%, respectively. Both indices had approximately the same spatial coverage of surface water with 82,650 km2 (WI) and 86,530 km2 (AWEIsh) for the whole of Ethiopia. The results provide a valuable insight into the extent of surface water bodies, which is essential for water resource planners and decision-makers. Such data can also play a role in monitoring the country’s reservoirs, which are important for the country’s energy and economic development. These results suggest that by applying the best-performing indices, better monitoring and management of water resources would be possible to achieve the Sustainable Development Goal 6 at the regional level.

show abstract

Section: Methodsmentioning

confidence: 99%

Performance of water indices for large-scale water resources monitoring using Sentinel-2 data in Ethiopia

Tesfaye,

Breuer

2024

Environ Monit Assess

View full text Add to dashboard Cite

show abstract

“…Each sample size was 1024 × 1024 pixels. After gridding the sample area into squares, geospatially stratified and optimized sampling [50][51][52] was used to select sample areas, as shown in the "Sample area selection" in Figure 4. This sampling method yielded representative training samples and reduced the workload of the preparation of the benchmark dataset with annotations.…”

Section: Sample Area Selectionmentioning

confidence: 99%

Boundary Delineator for Martian Crater Instances with Geographic Information and Deep Learning

et al. 2023

View full text Add to dashboard Cite

Detecting impact craters on the Martian surface is a critical component of studying Martian geomorphology and planetary evolution. Accurately determining impact crater boundaries, which are distinguishable geomorphic units, is important work in geological and geomorphological mapping. The Martian topography is more complex than that of the Moon, making the accurate detection of impact crater boundaries challenging. Currently, most techniques concentrate on replacing impact craters with circles or points. Accurate boundaries are more challenging to identify than simple circles. Therefore, a boundary delineator for Martian crater instances (BDMCI) using fusion data is proposed. First, the optical image, digital elevation model (DEM), and slope of elevation difference after filling the DEM (called slope of EL_Diff to highlight the boundaries of craters) were used in combination. Second, a benchmark dataset with annotations for accurate impact crater boundaries was created, and sample regions were chosen using prior geospatial knowledge and an optimization strategy for the proposed BDMCI framework. Third, the multiple models were fused to train at various scales using deep learning. To repair patch junction fractures, several postprocessing methods were devised. The proposed BDMCI framework was also used to expand the catalog of Martian impact craters between 65°S and 65°N. This study provides a reference for identifying terrain features and demonstrates the potential of deep learning algorithms in planetary science research.

show abstract

“…They found that the WorldCover dataset showed the highest overall accuracy, but FROM-GLC10 posed advantages in the classes of farmland, water, and built-up areas. Nevertheless, these efforts are either focused on assessing the overall accuracy of the product [17][18][19][20][21][22] or are class-specific [23][24][25][26]. There is a lack of evaluations on forest-related materials to better understand and use them.…”

Section: Introductionmentioning

confidence: 99%

User-Aware Evaluation for Medium-Resolution Forest-Related Datasets in China: Reliability and Spatial Consistency

Peng

Wang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Forest cover data are fundamental to sustainable forest management and conservation. Available medium-resolution publicly shared forest-related datasets provide primary information on forest distribution. The evaluation of relevant datasets is of great importance to learn about the differences, characterize the accuracy, and provide a reference for rational use. This study presents an evaluation and analysis of the forest-related datasets in China around 2020, including TreeCover and the forest-related layer (latter referred to as the forest datasets) in WorldCover, Esri land cover, FROM-GLC10, GlobeLand30, and GLC_FCS30. These forest datasets, that are obtained by aggregating forest-related lasses based on the classification schemes, are analyzed from spatial consistency and accuracy comparison. The results illustrate that forest datasets with 10m resolution are generally more precise than those with 30m resolution in China. WorldCover shows the highest accuracy, with producer accuracy and user accuracy of 91.4% and 87.09%, respectively. These datasets exhibit high accuracy but great spatial inconsistency. The more consistent the regions are, the more accurate the accuracy is. High consistency (≥5, i.e., classified into forests by five datasets) areas account for 56.49% of areas of forest classified (AFC), while the area of low consistency (≤2) reach 25.51% of AFC. The analysis delves into the datasets, offering a reliable reference for the usage of these datasets.

show abstract

Spatial Stratification Method for the Sampling Design of LULC Classification Accuracy Assessment: A Case Study in Beijing, China

Cited by 11 publications

References 32 publications

Performance of water indices for large-scale water resources monitoring using Sentinel-2 data in Ethiopia

Performance of water indices for large-scale water resources monitoring using Sentinel-2 data in Ethiopia

Boundary Delineator for Martian Crater Instances with Geographic Information and Deep Learning

User-Aware Evaluation for Medium-Resolution Forest-Related Datasets in China: Reliability and Spatial Consistency

Contact Info

Product

Resources

About