Mapping dominant leaf type based on combined Sentinel-1/-2 data – Challenges for mountainous countries

Waser‬, Lars T.; Rüetschi, Marius; Psomas, Achilleas; Small, David; Rehush, Nataliia

doi:10.1016/j.isprsjprs.2021.08.017

Cited by 39 publications

(27 citation statements)

References 60 publications

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“…Various crops, types of trees, and built areas can be classified using the sub-classes of LUCAS using the proposed methodology. For instance, spectral analysis of Sentinel-2 images integrated by Sentinel-1 time series [102,103] and dual-polarimetric ratios could significantly improve the types of trees detection and forest maps at a 10 m sampling. Frequent cropland mapping could also be performed through the proposed methodology.…”

Section: Discussionmentioning

confidence: 99%

ELULC-10, a 10 m European Land Use and Land Cover Map Using Sentinel and Landsat Data in Google Earth Engine

et al. 2022

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Land Use/Land Cover (LULC) maps can be effectively produced by cost-effective and frequent satellite observations. Powerful cloud computing platforms are emerging as a growing trend in the high utilization of freely accessible remotely sensed data for LULC mapping over large-scale regions using big geodata. This study proposes a workflow to generate a 10 m LULC map of Europe with nine classes, ELULC-10, using European Sentinel-1/-2 and Landsat-8 images, as well as the LUCAS reference samples. More than 200 K and 300 K of in situ surveys and images, respectively, were employed as inputs in the Google Earth Engine (GEE) cloud computing platform to perform classification by an object-based segmentation algorithm and an Artificial Neural Network (ANN). A novel ANN-based data preparation was also presented to remove noisy reference samples from the LUCAS dataset. Additionally, the map was improved using several rule-based post-processing steps. The overall accuracy and kappa coefficient of 2021 ELULC-10 were 95.38% and 0.94, respectively. A detailed report of the classification accuracies was also provided, demonstrating an accurate classification of different classes, such as Woodland and Cropland. Furthermore, rule-based post processing improved LULC class identifications when compared with current studies. The workflow could also supply seasonal, yearly, and change maps considering the proposed integration of complex machine learning algorithms and large satellite and survey data.

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Section: Discussionmentioning

confidence: 99%

ELULC-10, a 10 m European Land Use and Land Cover Map Using Sentinel and Landsat Data in Google Earth Engine

et al. 2022

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“…The satellites might be an enormously powerful tool since they allow for coverage of large geographical scales in a short period of time, having the potential for ecologists to provide a critical information about the drivers of the spatial and temporal distribution of biodiversity [34,44,45]. For example, S-2 imagery has provided valuable results in the monitoring of forest ecosystems distribution [46,47], plant species classification [48], mapping of forest vegetation dominant leaf types [49], monitoring plant phenology [50], predicting above-ground biomass [51,52] as well as in distinguishing temperate tree species [53]. However, the feasibility of all these vegetation parameter maps is often limited because large-wide estimations are particularly challenging in areas with irregular vegetation, complex terrain and frequent cloud shadows, such as subtropical mountainous areas [49,54].…”

Section: Introductionmentioning

confidence: 99%

“…For example, S-2 imagery has provided valuable results in the monitoring of forest ecosystems distribution [46,47], plant species classification [48], mapping of forest vegetation dominant leaf types [49], monitoring plant phenology [50], predicting above-ground biomass [51,52] as well as in distinguishing temperate tree species [53]. However, the feasibility of all these vegetation parameter maps is often limited because large-wide estimations are particularly challenging in areas with irregular vegetation, complex terrain and frequent cloud shadows, such as subtropical mountainous areas [49,54]. S-2 imagery data are affected by the biochemical composition of ground objects and imaging atmospheric conditions, while S-1 imagery data are susceptive to the geometrical structure of ground objects [48,55].…”

Section: Introductionmentioning

confidence: 99%

“…The S-1 Synthetic Aperture Radar (SAR) C-band data has the potential to overcome difficult problems of complex topography, shadows and atmospheric conditions [54]. For example, SAR data can use opposing seasonal backscatter behavior and long-wavelength microwave penetration capability to distinguish dominant leaf types in a center European country [49], to assess forest attributes and spatially map heterogeneous, fragmented croplands [33], to produce monthly maps of forest harvesting in two deforestation hotspots [56], and monitor tropical forest cover changes [57]. This is in contrast to assumptions made in other studies, where radar data was not applied to mountainous areas due to complex topography conditions [49].…”

Section: Introductionmentioning

confidence: 99%

“…For example, SAR data can use opposing seasonal backscatter behavior and long-wavelength microwave penetration capability to distinguish dominant leaf types in a center European country [49], to assess forest attributes and spatially map heterogeneous, fragmented croplands [33], to produce monthly maps of forest harvesting in two deforestation hotspots [56], and monitor tropical forest cover changes [57]. This is in contrast to assumptions made in other studies, where radar data was not applied to mountainous areas due to complex topography conditions [49]. Synergetic use of optical and radar data for predicting plant diversity in grassland [58] and more specific for identifying species and classifying vegetation types [55] is recently also found to provide more robust results.…”

Section: Introductionmentioning

confidence: 99%

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Mapping Plant Diversity Based on Combined SENTINEL-1/2 Data—Opportunities for Subtropical Mountainous Forests

et al. 2022

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Plant diversity is an important parameter in maintaining forest ecosystem services, functions and stability. Timely and accurate monitoring and evaluation of large-area wall-to-wall maps on plant diversity and its spatial heterogeneity are crucial for the conservation and management of forest resources. However, traditional botanical field surveys designed to estimate plant diversity are usually limited in their spatiotemporal resolutions. Using Sentinel-1 (S-1) and Sentinel-2 (S-2) data at high spatiotemporal scales, combined with and referenced to botanical field surveys, may be the best choice to provide accurate plant diversity distribution information over a large area. In this paper, we predicted and mapped plant diversity in a subtropical forest using 24 months of freely and openly available S-1 and S-2 images (10 m × 10 m) data over a large study area (15,290 km2). A total of 448 quadrats (10 m × 10 m) of forestry field surveys were captured in a subtropical evergreen-deciduous broad-leaved mixed forest to validate a machine learning algorithm. The objective was to link the fine Sentinel spectral and radar data to several ground-truthing plant diversity indices in the forests. The results showed that: (1) The Simpson and Shannon-Wiener diversity indices were the best predicted indices using random forest regression, with ȓ2 of around 0.65; (2) The use of S-1 radar data can enhance the accuracy of the predicted heterogeneity indices in the forests by approximately 0.2; (3) As for the mapping of Simpson and Shannon-Wiener, the overall accuracy was 67.4% and 64.2% respectively, while the texture diversity’s overall accuracy was merely 56.8%; (4) From the evaluation and prediction map information, the Simpson, Shannon-Wiener and texture diversity values (and its confidence interval values) indicate spatial heterogeneity in pixel level. The large-area forest plant diversity indices maps add spatially explicit information to the ground-truthing data. Based on the results, we conclude that using the time-series of S-1 and S-2 radar and spectral characteristics, when coupled with limited ground-truthing data, can provide reasonable assessments of plant spatial heterogeneity and diversity across wide areas. It could also help promote forest ecosystem and resource conservation activities in the forestry sector.

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Countrywide classification of permanent grassland habitats at high spatial resolution

Huber

Ginzler

Pazúr

et al. 2022

Remote Sens Ecol Conserv

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European grasslands face strong declines in extent and quality. Many grassland types are priority habitats for national and European conservation strategies. Countrywide, high spatial resolution maps of their distribution are often lacking. Here, we modelled the spatial distribution of 20 permanent grassland habitats at the level of phytosociological alliances across Switzerland at 10x10 m resolution. First, we applied ensemble models to provide distribution maps of the individual habitat types, using training data from various sources. Copernicus Sentinel satellite imagery and variables describing climate, soil and topography were used as predictors. The performance of these models was assessed based on the true skill statistics with a split-sampling of the data. Second, the individual maps were combined into countrywide maps of the most and second most likely habitat type, respectively, using an expert-based weighting approach. The performance of the combined map for the most likely habitat type was assessed via an independent testing dataset and a comparison of the predicted habitat-type proportions with extrapolations from field surveys. Most individual maps had useful to excellent predictive performance (TSS ≥ 0.6). For most grid cells in the combined maps, the most and second most likely habitat types were either ecologically closely related or representing two grassland types along a nutrient gradient. The same was true for omission errors. We found good agreement between the predicted and estimated proportions from field surveys. The area of raised bogs appears to be underestimated, while dry grasslands showed highest agreement. This work highlights the potential of earth observation data at fine spatial and temporal resolution to map habitats at broad scales, thereby providing the foundation for diverse conservation applications. A particular challenge remains in capturing the transition from nutrient-poor to nutrient-rich grasslands, which is highly important for biodiversity conservation.

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Mapping dominant leaf type based on combined Sentinel-1/-2 data – Challenges for mountainous countries

Cited by 39 publications

References 60 publications

ELULC-10, a 10 m European Land Use and Land Cover Map Using Sentinel and Landsat Data in Google Earth Engine

ELULC-10, a 10 m European Land Use and Land Cover Map Using Sentinel and Landsat Data in Google Earth Engine

Mapping Plant Diversity Based on Combined SENTINEL-1/2 Data—Opportunities for Subtropical Mountainous Forests

Countrywide classification of permanent grassland habitats at high spatial resolution

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