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Open AccessFeature PaperArticle

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

LETG UMR CNRS 6554, University of Rennes, place du recteur Henri Le Moal, 35000 Rennes, France
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Remote Sens. 2019, 11(24), 3041; https://doi.org/10.3390/rs11243041
Received: 7 October 2019 / Revised: 13 December 2019 / Accepted: 15 December 2019 / Published: 17 December 2019
(This article belongs to the Special Issue Advances of Remote Sensing in Pasture Management)
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. View Full-Text
Keywords: conservation; grassland ecosystems; random forest classifier; Land Parcel Information System; big EO data; France conservation; grassland ecosystems; random forest classifier; Land Parcel Information System; big EO data; France
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MDPI and ACS Style

Hubert-Moy, L.; Thibault, J.; Fabre, E.; Rozo, C.; Arvor, D.; Corpetti, T.; Rapinel, S. Mapping Grassland Frequency Using Decadal MODIS 250 m Time-Series: Towards a National Inventory of Semi-Natural Grasslands. Remote Sens. 2019, 11, 3041.

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