HIARC kick off meeting St. Petersburg, June 29-30 1 To provide - PowerPoint PPT Presentation

HIARC kick off meeting St. Petersburg, June 29-30 1

 To provide indicators characterizing current condition and dynamics of Arctic and sub-Arcrtic ecosystems  Indicators of Urban Effect  Indicators with good spatial coverage and temporal resolution  Indicators of quantity and quality assessment to be comparable with other data in the project  Indicators derived from satellite remotely sensed data Vegetation - indicator of natural or anthropogenic changes, including UHIE in the Arctic and sub-Arctic. 2

 WP1 – Documenting the climate and societal changes. Task 1.2 – Satellite imagery and products.  WP2 – Understanding the micro-climate and urban development. Task 2.2 – Building-up statistical support. Task 2.3 – Process understanding Task 2.4 – Environmental impact of urbanization Vegetation - indicator of natural or anthropogenic changes, including UHIE in the Arctic and sub-Arctic. 3

Proxy of vegetation productivity NIR = spectral reflectance in the near- infrared band - light scattering from the cell-structure of the healthy leaves RED = reflectance in the visible, chlorophyll-absorbing portion of the spectrum Normalized Difference Vegetation Index NDVI=(NIR-RED)/(NIR+RED) C. Tucker 1977 4

Plot-scale biomass vs. AVHRR 5 Bhatt, 2003

• 88% of the region shows Green: increasing NDVI no significant trends in NDVI. Rust: decreasing NDVI White: no trend • 3% have decreasing trends, and 9% have increasing trends. • Most of the positive changes are in tundra areas, particularly in North America. • Forest areas are showing an overall decline in NDVI Tundra Tundra Annual variations in estimates of vegetation net primary production (NPP) are shown with relative summertime stresses on vegetation for tundra and boreal forest. Rising temperatures and associated relaxation of low-temperature constraints to productivity drove a generally Boreal Forest Boreal Forest Boreal Forest increasing trend in tundra NPP over the 24-year period, whereas increasing drought conditions after 2000 contradict the potential benefits of warmer temperatures and led to a large drop in NPP for boreal forest regions 6

NDVI: integrates many factors affecting vegetation change A wide variety of social factors affect many vegetation disturbance Regimes Climate change is one of several disturbance factors affecting vegetation productivity and NDVI patterns. Immediate plant environment controls plant production and composition. A wide variety of vegetation-related factors affect NDVI Walker et al., 2009 7

 Land-surface temperature ( LST ) - key parameter of land- surface physics and processes at local and up to global scales.  It is the consequence of direct and indirect energy fluxes of the sun and atmosphere with the ground.  Hence it is a vital parameter for the changes in biogeo- chemical cycles, ecosystems, energy-heat-mass budgets and cycles, meteorology and climate across the spectrum of temporal scales from the diurnal to annual and longer. To analyze the feedback between land surface temperature and vegetation indices 8

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 LST is the brightness temperature of land surface. It's not the real temperature on the surface but has strong relationship with air temperature. Thus, LST could be a indicator for Urban Heat Island . UHI indicator is the temperature in the city being higher than that in the outside of city 10

Schwarz, et al , 2011 11

1 2 ( LST ) ( NDVI ) Evaluation of the extent Evaluation of state and and effects of UHI dynamics of vegetation Assessment relationships between 3 NDVI & LST , development of a strategy for their combined application/products 12

Remote sensing data and products 13

Moderate Resolution Imaging Spectroradiometer  Launched 1999  Terra and Aqua spacecraft  Global coverage  Views the entire surface of the Earth every one to two days  36 spectral bands  Three spatial resolutions -- 250m, 500m, and 1,000m.  Free and easy download MODIS  Ready to use products Sinusoidal Tiling System. Tiles are 10 by 10 degrees 14

 MODIS level 1 data, geolocation, cloud mask, and atmosphere products: http://ladsweb.nascom.nasa.gov/  MODIS land products: https://lpdaac.usgs.gov/  MODIS cryosphere products: http://nsidc.org/daac/modis/index.html  MODIS ocean color and sea surface temperature products: http://oceancolor.gsfc.nasa.gov/ 15

Radiation Budget Variables  Land Surface Reflectance  Land Surface Temperature (LST) and Emissivity  Albedo Ecosystem Variables  Vegetation Indices (NDVI and EVI)  Fraction of Photosynthetically Active Radiation (FPAR)/Leaf Area Index (LAI)  Net Primary Productivity (NPP) Land Cover Characteristics  Thermal Anomalies and Fire  Land Cover Type and Dynamics  Vegetation Continuous Cover 16

 Launched 1999/2013  7 /12 bands (1-5 VIS & NIR, 9 deep blue band for coastal/aerosol, 6-7 shortwave IR band for cirrus detection, 8 panchromatic bans & 10-11 Thermal IR (TIRS), 12 Quality Assessment band )  30 m spatial resolution , & 100 m for band 10-11  Scene size is 170 X 183 km  every 16 days  Easy and free downloading NDVI=(B5-B4)/(B5+B4) Few steps to convert TIRS data to the at-satellite brightness temperature 17

 Northwest Siberia YNAO SA  Fennoscandia Barents SA  Alaska Alaska SA 18

The main steps are to: 1. Determine regional vegetation trends • Develop spatial time series NDVI max • Calculate 15-year trends of NDVI max around entire northwest Siberia; and • Compare NDVI and NDVI changes in different bioclimatic zone around northwest Siberia • Percent of NDVI max area change for different forest type classes 19

2. Determine vegetation trend in urban areas • Create a 40-km buffer zone with 8 (5 km wide) sub-buffer zones, around the city-core • Calculate annual mean NDVI max inside of each sub- buffer and the city-core zone; • Calculate temporal and spatial trends of NDVI max inside of 40 km buffer zone • Compare NDVI trends in the buffer zone with the city- core zone trend ; 20

 Core city zone Nadum 5 10 15 20 25 30 35 40 40 km 21

3. Analyse LST indicators :  Spatial Extent of Surface Urban Heat Islands,  Urban heat island intensity (UHII) - Difference in mean LST between urban (administrative area) and rural (buffer around the city) areas &  Magnitude (maximum minus mean) 4. To analyze the feedback between surface temperature and vegetation indices 22

Ü Evergreen Dark Needle-leaf Forest Evergreen Light Needle-leaf Forest Deciduous Broadleaf Forest Deciduous Needle-leaf Forest Deciduous Needle-leaf Forest Gydan Yamal Evergreen Needle-leaf Shrub peninsula peninsula Mixed Forest I Unforest area Water bodies II III IV I – Tundra, II-Forest – Tundra, III-Northern Taiga, IV- Middle Taiga. 0 125 250 Kilometers 23

Forest type fraction in different Evergreen Dark Needle-leaf Forest Evergreen Light Needle-leaf Forest bioclimaic zones Deciduous Broadleaf Forest Deciduous Needle-leaf Forest 100,00 % Deciduous Needle-leaf Forest Mixed Forest 80,00 % Unforest area I 60,00 % 40,00 % II 20,00 % III 0,00 % Tundra Forest-Tundra Northern Taiga Middle Taiga IV Evergreen Dark Needle-leaf Forest Evergreen Light Needle-leaf Forest Deciduous Broadleaf Forest Deciduous Needle-leaf Forest Deciduous Mixed needleleaf majority forest Mixed Forest Unforest area 30% cover by forest, more then 24 50% cover by wetlands

MODIS NDVI 250 m spatial resolution, 16-day composites Processing steps: 1. images were mosaicked and re-projected 2. quality-filtered excluding snow- and cloud covered pixels. 3. 0.3-1 threshold to exclude water, bare soil and other non- vegetated pixel from the analysis. 4. compile growing season (JJA) maximum NDVI (NDVI max ) 5. define NDVI max trends for each pixel 25

The 15-year mean NDVImax NDVImax trend 2000-2014 NDVI max pattern have large meridional gradient. The highest The analysis of the NDVI max trends reveals greening over the tundra zones. The taiga is browning. The areas with highest NDVI, NDVI max values concentrated in the western part of the region particularly along the Ob River, show strong negative trend. This is and tend to cluster along rivers where hydrological drainage in general agreement with the trends reported in previous NDVI provides better conditions for trees to grow. 26 studies (Beck, P. & Goetz, S., 2011)

NDVI max trend (p<0,05) in NW SIberia bioclimatic zones Tundra Forest- Northern Middle I Tundra II Taiga III Taiga IV Negative 3,96 % 1,97 % 30,97 % 63,10 % Positive 42,75 % 18,39 % 23,19 % 15,66 % 100,00 % 80,00 % I 60,00 % 40,00 % 20,00 % II 0,00 % Tundra Forest-Tundra Northern Taiga Middle Taiga Negative Positive III 84% of the territory shows no IV significant trends in NDVI 27

NDVI max trend (p<0,05) in NW Sib forest cover map NW Sib bioclimatic zones Evergreen Dark Needle-leaf Forest Evergreen Light Needle-leaf Forest Deciduous Broadleaf Forest Deciduous Needle-leaf Forest Deciduous Needle-leaf Forest Evergreen Needle-leaf Shrub Mixed Forest Unforest area I II III IV http://smislab.ru/default.aspx?page=356 28