Differentiation of Six Grassland/Forage Types in Three Canadian Ecoregions Based on Spectral Characteristics

Frank, Thiago; Smith, A. M.; Houston, Bill; Lindsay, Emily; Guo, Xulin

doi:10.3390/rs14092121

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…On the other hand, training and validation data for non‐crop classes, such as grasslands and forests, were derived from past AAFC land cover and land use products, reflecting the notion of stability of these ecosystems (Agriculture and Agri‐Food Canada, 2021b). Additionally, studies have indicated that the ability of ACI to discriminate between native and tame grasslands had been notably inadequate (Frank et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Assessment of remotely sensed inventories for land cover classification of public grasslands in Manitoba, Canada

Encabo,

Cordeiro,

Badreldin

et al. 2023

Grass and Forage Science

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Land cover classification is one of the most common applications of remote sensing and is used for developing and modifying land management policies on agricultural landscapes to achieve conservation and economic goals, such as reducing grassland degradation and improving livestock and crop production. In this study, the grassland classification of the crown lands (public grasslands in Canada) from a newly developed remotely sensed dataset in the Prairie Province of Manitoba (i.e., the Manitoba Grassland Inventory, MGI) was assessed in terms of accuracy by comparison to non‐spatial government records. The analysis consisted of (i) converting non‐spatial records from the provincial crown land database to spatially‐defined parcels by performing parcel delineations using geographic information system (GIS) and R programming tools, (ii) summarising the MGI classification at the same spatial scale, and (iii) comparing the agreement between MGI and the crown land database. The most common land cover types identified were: forest (30%) and shrubland (25%), followed by native (10%) and tame (9%) grasslands. However, the class agreements between woody (i.e., forests and shrublands) and grassy (i.e., native and tame grasslands) vegetation classes were low between these datasets because of their spectral similarities. Based on these results, we suggest additional refinements on both sensor and ground data to improve the classification agreement between these datasets. This study is one of the first attempts to compare ground‐collected government records against a remotely sensed product in Manitoba.

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Section: Discussionmentioning

confidence: 99%

Assessment of remotely sensed inventories for land cover classification of public grasslands in Manitoba, Canada

Encabo,

Cordeiro,

Badreldin

et al. 2023

Grass and Forage Science

View full text Add to dashboard Cite

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“…Species composition is an important parameter that describes the structure of vegetation communities and indicates biodiversity and ecosystem health in a region ( Lyu et al, 2020 ), and species identification is crucial for environmental monitoring ( Zhang et al, 2020 ). Previous studies mostly selected characteristic bands from the period of vigorous vegetation growth to analyse and compare different grassland vegetation or transformed the characteristic bands and used quantitative spectral characteristic parameters and vegetation indices to distinguish different vegetation types, obtaining good research results ( Melville, Lucieer & Aryal, 2018 ; Yu et al, 2018 ; Frank et al, 2022 ). Spectral parameters can enhance the ability to separate plants ( Dmitriev et al, 2022 ; Soubry & Guo, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Classification of major species in the sericite–Artemisia desert grassland using hyperspectral images and spectral feature identification

Liu,

Han,

Jin

et al. 2024

PeerJ

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Background The species composition of and changes in grassland communities are important indices for inferring the number, quality and community succession of grasslands, and accurate monitoring is the foundation for evaluating, protecting, and utilizing grassland resources. Remote sensing technology provides a reliable and powerful approach for measuring regional terrain information, and the identification of grassland species by remote sensing will improve the quality and effectiveness of grassland monitoring. Methods Ground hyperspectral images of a sericite–Artemisia desert grassland in different seasons were obtained with a Soc710 VP imaging spectrometer. First-order differential processing was used to calculate the characteristic parameters. Analysis of variance was used to extract the main species, namely, Seriphidium transiliense (Poljak), Ceratocarpus arenarius L., Petrosimonia sibirica (Pall), bare land and the spectral characteristic parameters and vegetation indices in different seasons. On this basis, Fisher discriminant analysis was used to divide the samples into a training set and a test set at a ratio of 7:3. The spectral characteristic parameters and vegetation indices were used to identify the three main plants and bare land. Results The selection of parameters with significant differences (P < 0.05) between the recognition objects effectively distinguished different land features, and the identification parameters also differed due to differences in growth period and species. The overall accuracy of the recognition model established by the vegetation index decreased in the following order: June (98.87%) > September (91.53%) > April (90.37%). The overall accuracy of the recognition model established by the feature parameters decreased in the following order: September (89.77%) > June (88.48%) > April (85.98%). Conclusions The recognition models based on vegetation indices in different months are superior to those based on feature parameters, with overall accuracies ranging from 1.76% to 9.40% higher. Based on hyperspectral image data, the use of vegetation indices as identification parameters can enable the identification of the main plants in sericite–Artemisia desert grassland, providing a basis for further quantitative classification of the species in community images.

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A Hierarchical Machine Learning-Based Strategy for Mapping Grassland in Manitoba’s Diverse Ecoregions

Mousavi,

Biney,

Kishchuk

et al. 2024

Remote Sensing

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Accurate and reliable knowledge about grassland distribution is essential for farmers, stakeholders, and government to effectively manage grassland resources from agro-economical and ecological perspectives. This study developed a novel pixel-based grassland classification approach using three supervised machine learning (ML) algorithms, which were assessed in the province of Manitoba, Canada. The grassland classification process involved three stages: (1) to distinguish between vegetation and non-vegetation covers, (2) to differentiate grassland from non-grassland landscapes, and (3) to identify three specific grassland classes (tame, native, and mixed grasses). Initially, this study investigated different satellite data, such as Sentinel-1 (S1), Sentinel-2 (S2), and Landsat 8 and 9, individually and combined, using the random forest (RF) method, with the best performance at the first two steps achieved using a combination of S1 and S2. The combination was then utilized to conduct the first two steps of classification using support vector machine (SVM) and gradient tree boosting (GTB). In step 3, after filtering out non-grassland pixels, the performance of RF, SVM, and GTB classifiers was evaluated with combined S1 and S2 data to distinguish different grassland types. Eighty-nine multitemporal raster-based variables, including spectral bands, SAR backscatters, and digital elevation models (DEM), were input for ML models. RF had the highest classification accuracy at 69.96% overall accuracy (OA) and a Kappa value of 0.55. After feature selection, the variables were reduced to 61, increasing OA to 72.62% with a Kappa value of 0.58. GTB ranked second, with its OA and Kappa values improving from 67.69% and 0.50 to 72.18% and 0.58 after feature selection. The impact of raster data quality on grassland classification accuracy was assessed through multisensor image fusion. Grassland classification using the Hue, Saturation, and Value (HSV) fused images showed higher OA (59.18%) and Kappa values (0.36) than the Brovey Transform (BT) and non-fused images. Finally, a web map was created to show grassland results within the Soil Landscapes of Canada (SLC) polygons, relating soil landscapes to grassland distribution and providing valuable information for decision-makers and researchers. Future work may include extending the current methodology by considering other influential variables, like meteorological parameters or soil properties, to create a comprehensive grassland inventory across the whole Prairie ecozone of Canada.

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Differentiation of Six Grassland/Forage Types in Three Canadian Ecoregions Based on Spectral Characteristics

Cited by 3 publications

References 41 publications

Assessment of remotely sensed inventories for land cover classification of public grasslands in Manitoba, Canada

Assessment of remotely sensed inventories for land cover classification of public grasslands in Manitoba, Canada

Classification of major species in the sericite–Artemisia desert grassland using hyperspectral images and spectral feature identification

A Hierarchical Machine Learning-Based Strategy for Mapping Grassland in Manitoba’s Diverse Ecoregions

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