Mapping Grassland Frequency Using Decadal MODIS 250 m Time-Series: Towards a National Inventory of Semi-Natural Grasslands
| Autor: | Laurence Hubert-Moy, Clémence Rozo, Damien Arvor, Elodie Fabre, Thomas Corpetti, Jeanne Thibault, Sébastien Rapinel |
|---|---|
| Přispěvatelé: | Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Nantes (UN) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Mediterranean climate
grassland ecosystems 010504 meteorology & atmospheric sciences 0211 other engineering and technologies random forest classifier big EO data 02 engineering and technology Land Parcel Information System 01 natural sciences Grassland Urbanization Ecosystem ComputingMilieux_MISCELLANEOUS 021101 geological & geomatics engineering 0105 earth and related environmental sciences 2. Zero hunger geography geography.geographical_feature_category conservation [SHS.GEO]Humanities and Social Sciences/Geography 15. Life on land Threatened species [SDE]Environmental Sciences General Earth and Planetary Sciences Environmental science Satellite Physical geography Moderate-resolution imaging spectroradiometer France Scale (map) |
| Zdroj: | Remote Sensing Remote Sensing, MDPI, 2019, 11 (24), pp.3041. ⟨10.3390/rs11243041⟩ Volume 11 Issue 24 Pages: 3041 |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs11243041⟩ |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|