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Mapping Grassland Extent and Degradation in Azerbaijan

Asam, Sarah and Da Ponte, Emmanuel and Köstl, Tobias and Wuttej, Daniel and Abbasov's, Samir and Köppler, Markus (2019) Mapping Grassland Extent and Degradation in Azerbaijan. Living Planet Symposium - ESA 2019, 13 - 17 May 2019, Milan , Italy.

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Abstract

The grassland areas of Azerbaijan are under heavy anthropogenic pressure, including overgrazing, land use conversion to crop land, infrastructure projects, and climate change. To put a hold on further degradation of grasslands, to conserve and sustainably use winter, summer and common pastures as well as hay meadows, and to increase the productivity of those areas, consistent and up-to-date data on grassland extent and status is a prerequisite. In the GRAZE project of GIZ, DLR and E.C.O, we therefore aim at mapping the extent, productivity, and degradation status of all grassland areas in Azerbaijan based on current high spatial resolution remote sensing data. The derived nationwide maps should support decision-making processes as well as the development of pastures management policies, and improve the understanding of the condition of mountain and lowland grasslands. Two field campaigns were conducted in August and October 2018 in 22 Azerbaijani districts, following two north-south and east-west transects through the country and covering a wide range of landscape types as well as a height gradient from -50 m to 2280 m above sea level. In total, 296 field points in grassland, arable land, and shrub land areas were visited, for which land cover, vegetation type, coverage, management and erosion intensity were recorded. These field observations are used together with samples of urban areas, bare soil, water, cropland, and forested areas (about 100 samples per class) collected manually on the screen from high spatial resolution Sentinel-2 scenes. The classification is conducted then using a Sentinel-2 time series including all 28 tiles that cover Azerbaijan and the entire year of 2018 (about 2900 scenes in total) in a random forest classifier. For the random forest models, several statistical metrics (e.g. 10, 25, 50, 75, 90 percentiles) were derived from Sentinel-2 imagery based on the band reflectances and selected vegetation indices, such as NDVI. Furthermore differences between percentiles and seasonal metrics were estimated. Hence, a total of 500 independent models where built and combined based on over 70 temporal and spectral input features. While 50 % of the data points are used as training data, the other half is used for validation purposes. A special focus of the land cover mapping is the proper discrimination of meadows, winter pastures, summer pastures, and common pastures occurring in a large variety of grassland types (steppes, semi-deserts, mountainous areas etc.), as well as of perennial crops such as alfalfa, which are cultivated and used in a similar manner as grasslands. Relative grassland productivity is assessed in a next step for all identified grasslands areas based on seasonal intensity measures such as maximum and cumulative vegetation index (VI) metrics. The estimation of grassland condition is performed last through a multi-temporal comparison of the current grassland productivity measures with the same seasonal VI metrics derived from historical multi-temporal satellite imagery (Landsat). As reference periods, the years 1985, 1995, 2000, 2005, 2010, and 2015 have been selected in order to capture all grassland management and condition changes since the collapse of the soviet management regime. Through the assessment of relative changes in vegetation abundance and productivity, in combination with experiences from the field campaigns and from pilot projects on erosion control conducted in the Greater Caucasus, we aim at deriving nationwide indicator maps for the detection of degraded grassland areas in Azerbaijan.

Item URL in elib:https://elib.dlr.de/129215/
Document Type:Conference or Workshop Item (Speech)
Title:Mapping Grassland Extent and Degradation in Azerbaijan
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Asam, Sarahsarah.asam (at) dlr.deUNSPECIFIED
Da Ponte, EmmanuelEmmanuel.DaPonte (at) dlr.deUNSPECIFIED
Köstl, Tobiaskoestl (at) e-c-o.atUNSPECIFIED
Wuttej, Danielwuttej (at) e-c-o.atUNSPECIFIED
Abbasov's, SamirUNSPECIFIEDUNSPECIFIED
Köppler, Markusmarkus.koeppler (at) giz.deUNSPECIFIED
Date:May 2019
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Grasslands, remote sensing, azerbaijan, degradation
Event Title:Living Planet Symposium - ESA 2019
Event Location:Milan , Italy
Event Type:international Conference
Event Dates:13 - 17 May 2019
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Deposited By: Da Ponte, Emmanuel
Deposited On:07 Oct 2019 12:58
Last Modified:07 Oct 2019 12:58

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