Moderate- and high-resolution Earth Observation data based forest and agriculture monitoring in Russia using VEGA Web-Service

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Camron Harrington
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using VEGA Web-Service Sergey BARTALEV and Evgeny

1 Moderate- and high-resolution Earth Observation data based forest and agriculture monitoring in Russia using VEGA Web-Service Sergey BARTALEV and Evgeny LOUPIAN Space Research Institute, Russian Academy of Sciences

2 VEGA - Web-based Vegetation Monitoring Service vega.smislab.ru VEGA (in operation since May 2011) is developed by the Space Research Institute of the Russian Academy of Sciences to provide vegetation status analysis tools based on multi-annual and near-real-time EO data

Research Institute of the Russian Academy of Sciences to provide

3 Main component of VEGA Service (I) Multi-annual of automatic near-real-time update EO data archive, including: a. MODIS Surface Reflectance MOD09 standard products ( ongoing) b. Landsat-TM/ETM+ automatic download ( ongoing) (II) Automated EO data processing chains, including: a. EO data pre-processing (cloud/shadow screening, image compositing, vegetation indexes generation, data time-series reconstruction and etc) b. Thematic products generation (land cover/land use, active fires, burnt area and severity, crop masks and etc) (III) Web-based Users Interface with data analysis tools

gov, 2001-ongoing) (II) Automated EO data processing chains, including: a.

4 MODIS data cloud/cloud-shadow screening R 0.4 NDSI Snow Daily data masks creation: 1) Snow and clouds detection Clear surface Clouds R3 2) Shadows detection ) Statistical filtering Clear surface Semi clouds A B C Clear surface 0,35 satellite Sun 0,3 0,25 0,2 cloud 0,15 x North h B - shadow y А cloud pixel East 0,1 0, pixen number Geometry of shadow line median filter original series Shadow line analysis

0.2 0.05 3) Statistical filtering Clear surface - 0.

5 MODIS time-series reconstruction 0,35 PVI 0,3 исходный Original временной PVI data ряд 0,25 0,2 0,15 0,1 0,05 0 moving time window for polynomial approximation t Multi-temporal synthesis of original data 0,35 0,3 PVI исходный Original временной PVI data ряд сглаженный Reconstructed и интерполированный time-series ряд 0,25 0,2 0,15 0,1 0,05 0 t Multi-temporal synthesis of reconstructed data

original data 0,35 0,3 PVI исходный Original временной PVI data ряд сглаженный Reconstructed и

6 Cloud-free summer MODIS mosaic

7 Cloud-free winter MODIS mosaic

8 Landsat-TM clouds and shadow masking Landsat-TM Clouds detection (in red) Geometric shadow belt (in black) 1. Clouds detection 2. Geometric modelling of shadows belts 3. Filtering of the geometric shadows 4. Spatial filtering of clouds and shadows Detected clouds (red) and shadows (green)

9 Main principals for land cover products generation chains Geographical focus on national level (entire Russia) with potential expansion Primary use of open sources of EO data, such as moderate res. (MODIS, SPOT-VGT, VIIRS and future Proba-V, Sentinel-3) and high-res. (Landsat-TM/ETM+ and future LCM and Sentinel-2) instruments Long-term time-series data analysis for land cover mapping and monitoring Spatially and temporally adaptive algorithms to ensure globally consistent land cover mapping and monitoring Technological focus on fully automated EO data processing chains to perform land monitoring in routine and repeatable manner

(Landsat-TM/ETM+ and future LCM and Sentinel-2) instruments Long-term time-series data analysis for land cover mapping and monitoring Spatially and temporally

10 Main thematic focuses Generic land cover mapping Near-real time wildfire monitoring and assessment of fire consequences Forest cover mapping, characterisation and change monitoring Agricultural monitoring, including arable land dynamic, crop types mapping and status assessment

mapping, characterisation and change monitoring Agricultural

11 EO data based land cover products Generic land cover SPOT-Vegetation (1,15 km) MODIS (250 m) Landsat-TM/ETM+ (in development) Burnt area SPOT-Vegetation (1,15 km) MODIS (250 m) Landsat-TM/ETM+ Burnt severity / Trees mortality MODIS, 250 m Landsat-TM/ETM+ Arable lands (MODIS, 250 m) Crop types (MODIS, 250 m)

SPOT-Vegetation (1,15 km) MODIS (250 m) Landsat-TM/ETM+ Burnt severity /

12 TerraNorte RLC Map The land cover map for Russia based on MODIS 250 m

13 Tree species mapping with MODIS reflectance in NIR ( nm) band oak birch 0,35 maple aspen linden 0,3 0,25 0,019 0,024 0,029 0,034 0,039 0,044 0,049 reflectance in RED ( nm) band MODIS winter composite oak birch aspen lime maple dark coniferous pine Forest Map of USSR (1990, 1:2,5 mln) Forest species mapping using MODIS

RED (620 670 nm) band MODIS winter composite - - - - - - - oak birch aspen lime maple

14 Forest Stock Volume data Forest Stock Volume, m3/ha The forest stock volume is estimated based o combined use of MODIS (250 m) derived land cover map and national forest statistics for regions Dmitry Ershov et al., 2011

use of MODIS (250 m) derived land cover map and

15 Burnt area mapping with MODIS

16 Burnt area mapping using MODIS and Landsat-TM/ETM Forest burnt area of fire season 2011 detected with MODIS

17 Burnt area map for year 2011 based on Landsat-TM/ETM Burnt area mapping technology using Landsat-TM data is in operation (> 3500 burs were mapped in Russia for 2011 fire season)

18 Annual mapping of arable lands using MODIS data time-series

Crop types classification using MODIS PVI 0,35 0,3 0,25 0,2 Peas Melilot Potato Alfalfa Perennials Spring crops Fallow Rape Winter rye Barley+Peas mixture 0,15 0,1 0,05 0 17-Apr 7-May 27-May 16-Jun

19 Crop types classification using MODIS PVI 0,35 0,3 0,25 0,2 Peas Melilot Potato Alfalfa Perennials Spring crops Fallow Rape Winter rye Barley+Peas mixture 0,15 0,1 0, Apr 7-May 27-May 16-Jun 6-Jul 26-Jul 15-Aug 4-Sep 24-Sep Ground-truth Omission (%) , , ,1 Classification , , , , , , ,7 Commission (%) 3,3 3,1 9,8 3,7 0,9 6,5 4,5 9,8 11,1 9,8 93

34 0 0 3 1 24,4 3 0 0 101 0 0 10 7 1 0 0 15,1 Classification 4 0 0 0 26 0 0 0 0 0 0 0,0 5 0 0 1 1 797 116 17 8 1 0 15,3 6 2 3 3 0 2 5822 9 6 4 4 0,6 7 0 1 7 0 2

20 VEGA Map Interface: Main Sections High-resolution satellite images Agriculture Forest Wild Fires Meteorology Generic map layers Basket for HR Images Map navigation tools

21 VEGA Map Interface The VEGA map interface provides easy access to Landsat-TM/ETM (and potentially other HR satellite data, including Sentinel-2) archive Operationally updating archive of Landsat-TM/ETM data; About 140,000 scenes are collected for the period ; Automated clouds/shadow detection and cloud-free compositing chains

22 MODIS and Landsat-TM/ETM+ data synergy in the VEGA Map Interface Multi-annual weekly update MODIS based NDVI profiles can be onthe-fly derived for any area of interest selected with Landsat-TM/ETM

23 Analysis of Forest Disturbances using VEGA Service web-tools August 7, 2009 June 10, 2011 Analysis of forest disturbances caused by hurricane wind in July 2010 with combine use of MODIS and Landsat-TM data

24 Analysis of logging activity using VEGA Service web-tools July 18, 2010 June 12, 2011

25 Crop status assessment at field level Assessment of winter crop status in spring Assessment of impact of drought on crop status Crop status assessment based on deviation of Vegetation Index seasonal development from multiannual average for same crop type

26 Crop status assessment at regional level

27 VEGA Service Users (I) Insurance Companies (II) Forest Industry (logging and paper companies) (III) Regional Administrations (IV) Public and Research Organisations

28 VEGA Service Perspectives (I) Integration of new EO data: a. Moderate resolution data (Sentinel-3, Proba-V, NPP) b. High resolution data (LDCM, Sentinel-2) (II) Integration of new thematic products: a. SAR data derived biomass b. Crop yield c. Crop-types and tree-species d. Biophysiscal characteristics (e.g. green vegetation area fraction) (III) Modelling component assimilated with RS products: a. Fire propagation b. Crop grow

Monitoring Overview with a Focus on Land Use Sustainability Metrics

Monitoring Overview with a Focus on Land Use Sustainability Metrics Canadian Roundtable for Sustainable Crops. Nov 26, 2014 Agriclimate, Geomatics, and Earth Observation Division (ACGEO). Presentation