Reclaimed Area Land Cover Mapping Using Sentinel-2 Imagery and LiDAR Point Clouds

Szostak, Marta; Pietrzykowski, Marcin; Likus‐Cieślik, Justyna

doi:10.3390/rs12020261

Cited by 15 publications

(6 citation statements)

References 59 publications

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“…The spatial parameters (2D and 3D) of the vegetation were derived from the ALS point clouds (Figure 8), the height of vegetation (95 th percentile), the standard deviation of height, and canopy cover (cover density, values of 0-100%) [42][43][44]. The parameters are presented as raster maps (pixel size: 1.0 m).…”

Section: Resultsmentioning

confidence: 99%

“…In total, forested grounds covered 95.19% of the res Other classes include mineral extraction sites and water. The spatial parameters (2D and 3D) of the vegetation were derived fro point clouds (Figure 8), the height of vegetation (95 th percentile), the standard of height, and canopy cover (cover density, values of 0-100%) [42][43][44]. The para presented as raster maps (pixel size: 1.0 m).…”

Section: Resultsmentioning

confidence: 99%

“…The former Jeziórko sulfur mine area showcases a dynamic increase in its forested areas. Previous studies [43,44,[63][64][65] based on analyses using Landsat 7 (2000), Landsat 8 (2013), and Sentinel-2 (2016) image classification, as well as orthophotomaps vectorization (2009), showed the general range and total value of forested grounds. Using the PlanetScope image analyses in this study, we obtained more precise information about the location, range, and percentage of the total value of forested areas.…”

Section: Discussionmentioning

confidence: 99%

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PlanetScope Imageries and LiDAR Point Clouds Processing for Automation Land Cover Mapping and Vegetation Assessment of a Reclaimed Sulfur Mine

2021

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The present research investigated the possibility of using PlanetScope imageries and LiDAR point clouds for land cover assessment, especially vegetation mapping, in degraded and reclaimed areas. Studies were carried out on the former sulfur mine of Jeziórko located in Southeast Poland. In total, more than ca. 2000 ha of this mine area were reclaimed after borehole exploitation and afforestation. We investigated a total area of 216.72 ha. Integration of PlanetScope imageries and LiDAR point clouds processing offers the ability to derive information about the LULC classes and vegetation growth in the analyzed area and indicate the forest succession progress as an effect of the reclamation treatments. In the Jeziórko area, we identified coniferous forest (90.84 ha, 41.91% of the research area), broad-leaved forest (44.02 ha, 20.31%), and transitional woodland shrub areas with herbaceous communities (77.96 ha, 35.97%). The analyses focused on the detection and monitoring of the forest succession processes and obtaining the tree canopy profiles and characteristics of vegetation, i.e., the height and cover density.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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PlanetScope Imageries and LiDAR Point Clouds Processing for Automation Land Cover Mapping and Vegetation Assessment of a Reclaimed Sulfur Mine

2021

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“…However, it does not have the spectral and texture information that Worldview-2 images can provide. In the future, researchers can integrate image and LiDAR [62] data sources to estimate the forest spatial structure to improve accuracy, but the cost of LiDAR needs to be considered. For example, unmanned aerial vehicle equipped with LiDAR [63][64][65] can conduct large-scale ecological evaluation but the cost is higher.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Estimation of the Restored Forest Spatial Structure in Semi-Arid Mine Dumps Using Worldview-2 Imagery

Xiao-xiao

Zhou

Yang

et al. 2020

Forests

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Forest monitoring is critical to the management and successful evaluation of ecological restoration in mined areas. However, in the past, available monitoring has mainly focused on traditional parameters and lacked estimation of the spatial structural parameters (SSPs) of forests. The SSPs are important indicators of forest health and resilience. The purpose of this study was to assess the feasibility of estimating the SSPs of restored forest in semi-arid mine dumps using Worldview-2 imagery. We used the random forest to extract the dominant feature factor subset; then, a regression model and mind evolutionary algorithm-back propagation (MEA-BP) neural network model were established to estimate the forest SSP. The results show that the textural features found using 3 × 3 window have a relatively high importance score in the random forest model. This indicates that the 3 × 3 texture factors have a relatively strong ability to explain the restored forest SSPs when compared with spectral factors. The optimal regression model has an R2 of 0.6174 and an MSRE of 0.1001. The optimal MEA-BP neural network model has an R2 of 0.6975 and an MSRE of 0.0906, which shows that the MEA-BP neural network has greater accuracy than the regression model. The estimation shows that the tree–shrub–grass mode with an average of 0.7351 has the highest SSP, irrespective of the restoration age. In addition, the SSP of each forest configuration type increases with the increase in restoration age except for the single grass configuration. The increase range of SSP across all modes was 0.0047–0.1471 after more than ten years of restoration. In conclusion, the spatial structure of a mixed forest mode is relatively complex. Application cases show that Worldview-2 imagery and the MEA-BP neural network method can support the effective evaluation of the spatial structure of restored forest in semi-arid mine dumps.

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“…In addition to optical remote sensing data, DSM acquired by LiDAR or some other sensors is also very helpful for land cover classification (Wai et al, 2015;Szostak et al, 2020.). From height information contained in DSM, extra information can be obtained, and this information can be obtained from spectral features.…”

Section: Introductionmentioning

confidence: 99%

Land Cover Classification Using Convolutional Neural Network With Remote Sensing Data and Digital Surface Model

Liu

Zhang

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Land cover map is widely used in urban planning, environmental monitoring and monitoring of the changing world. This paper proposes a framework with convolutional neural network (CNN), object-based voting and conditional random field (CRF) for land cover classification. Both very-high-resolution (VHR) remote sensing images and digital surface model (DSM) are inputs of this CNN model. To solve the “salt and pepper” effect caused by pixel-based classification, an object-based voting classification is performed. And to capture accurate boundary of ground objects, a CRF optimization using spectral information, DSM and deep features extracted through CNN is applied. Area one of Vaihingen datasets is used for experiment. The experimental results show that method proposed in this paper achieve an overall accuracy of 95.57%, which demonstrate the effectiveness of proposed method.

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Reclaimed Area Land Cover Mapping Using Sentinel-2 Imagery and LiDAR Point Clouds

Cited by 15 publications

References 59 publications

PlanetScope Imageries and LiDAR Point Clouds Processing for Automation Land Cover Mapping and Vegetation Assessment of a Reclaimed Sulfur Mine

PlanetScope Imageries and LiDAR Point Clouds Processing for Automation Land Cover Mapping and Vegetation Assessment of a Reclaimed Sulfur Mine

Estimation of the Restored Forest Spatial Structure in Semi-Arid Mine Dumps Using Worldview-2 Imagery

Land Cover Classification Using Convolutional Neural Network With Remote Sensing Data and Digital Surface Model

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