Mapping Grassland Frequency Using Decadal MODIS 250 m Time-Series: Towards a National Inventory of Semi-Natural Grasslands

Abstract

Semi-natural grasslands are perennial ecosystems and an important part of agricultural landscapes that are threatened by urbanization and agricultural intensification. However, implementing national grassland conservation policies remains challenging because their inventory, based on short-term observation, rarely discriminate semi-natural permanent from temporary grasslands. This study aims to map grassland frequency at a national scale over a long period using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m satellite time-series. A three-step method was applied to the entire area of metropolitan France (543,940 km²). First, land-use and land-cover maps—including grasslands—were produced for each year from 2006–2017 using the random forest classification of MOD13Q1 and MYD13Q1 products, which were calibrated and validated using field observations. Second, grassland frequency from 2006–2017 was calculated by combining the 12 annual maps. Third, sub-pixel analysis was performed using a reference layer with 20 m spatial resolution to quantify percentages of land-use and land-cover classes within MODIS pixels classified as grassland. Results indicate that grasslands were accurately modeled from 2006–2017 (F1-score 0.89–0.93). Nonetheless, modeling accuracy varied among biogeographical regions, with F1-score values that were very high for Continental (0.94 ± 0.01) and Atlantic (0.90 ± 0.02) regions, high for Alpine regions (0.86 ± 0.04) but moderate for Mediterranean regions (0.62 ± 0.10). The grassland frequency map for 2006–2017 at 250 m spatial resolution provides an unprecedented view of stable grassland patterns in agricultural areas compared to existing national and European GIS layers. Sub-pixel analysis showed that areas modeled as grasslands corresponded to grassland-dominant areas (60%–94%). This unique long-term and national monitoring of grasslands generates new opportunities for semi-natural grassland inventorying and agro-ecological management.

Recurrent-based regression of Sentinel time series for continuous vegetation monitoring

“…Local temporal methods exploit the temporal evolution of the time series by using a sliding temporal window. Among these approaches, the classical linear interpolation method is the most well-known and used (Inglada et al, 2017;Defourny et al, 2019;Hubert-Moy et al, 2019;Bolton et al, 2020;Kamir et al, 2020). Polynomial-based strategies have been extensively proposed as alternatives.…”

Combined use of environmental and spectral variables with vegetation archives for large-scale modeling of grassland habitats

“…Study site location and reference data showing (a) the selection of a reference plot within a field habitat polygon and a 250 m MODIS pixel of permanent grasslands (Hubert-Moy et al, 2019), and (b) the distribution of the 1970 reference plots per 10 × 10 km grid (European Environmental Agency (EEA)) within the four European biogeographic regions in France.…”

“…However, this approach raises the problem of one-to-many correspondence (Kosmidou et al, 2014), as well as that of the transferability of decision rules related to variation in the quality, number of acquisitions and type of remote sensing data available among local sites (Corbane et al, 2015). A recent study accurately discriminated natural and artificial grasslands over the whole of metropolitan France using moderate-resolution imaging spectroradiometer (MODIS) decennial time series (Hubert-Moy et al, 2019), but did not discriminate grassland habitat types.…”

“…This study builds on the research initiated by Hubert-Moy et al (2019) and aims to discriminate and model the distribution of grassland habitats in France based on spectral and environmental variables according to the European Union Nature Information System (EUNIS; https://eunis.eea.europa.eu/). EUNIS is defined mainly on the basis of phytosociological classification systems (Rodwell et al, 2018) and has five nested hierarchal levels: levels 1–3 describe mostly natural habitats using physiognomic and environmental criteria and a few floristic criteria, while levels 4 and 5 use floristic criteria alone (Davies et al, 2004).…”

Knowledge-Based Classification of Grassland Ecosystem Based on Multi-Temporal WorldView-2 Data and FAO-LCCS Taxonomy

“…Some studies have addressed natural and semi-natural grassland ecosystems monitoring at medium-high spatial resolution, exploiting both optical and SAR data [20][21][22][23][24][25][26][27]. Among these data, the Landsat series (30 m spatial resolution) and the European Space Agency (ESA) optical Sentinel-2 (10-20 m) and Sentinel-1 data have been considered as quite reliable for global and national application [3,28].…”