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

Abstract

Differentiation of grassland/forage types and accurate estimates of their location and extent are important for understanding their ecological processes and for applying appropriate management practices. We are aiming to reveal the different spectral characteristics of six grassland/forage land covers in three ecoregions located in the Canadian Prairies, based on field data and satellite images. Three spectral indices representing productivity (Normalized Difference Vegetation Index (NDVI)), moisture content (Normalized Difference Moisture Index (NDMI)), and plant photosynthetic activity (Plant Senescence Reflectance Index (PSRI)) were used for comparison of means, comparison of coefficient of variation (CV), and analysis of variance (ANOVA). The results indicated that different grassland types show distinguishable spectral characteristics in the Moist-Mixed and Mixed Ecoregions, while it was not possible to differentiate the classes in the Fescue Ecoregion. To further investigate the within-sites and between-sites heterogeneity, we calculated the CV in a 3 × 3 window and placed them in comparative triangles to demonstrate their potential separability. Results indicated that the triangles based on the CV offered greater class separability in the Fescue Ecoregion and in the Mixed Ecoregion.

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

“…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).…”

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

“…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 ).…”