Using Remotely Sensed Data and the FAREAST Forest Succession Model - PowerPoint PPT Presentation

Wildlife Conservation Society Using Remotely Sensed Data and the FAREAST Forest Succession Model to Estimate Biomass and Leaf Area Index (LAI) Across a Complex Landscape in the Russian Far East CITES-2009 NEESPI Workshop Krasnoyarsk, Russia July 15, 2009 N. Sherman, T. Loboda, H. Shugart, G. Sun, D. Miquelle, I. Csiszar

Project Purpose To evaluate and characterize the effects of disturbance such as fire, human activities and climate change on the habitat of the endangered Amur (or Siberian) tiger and Amur leopard in the Russian Far East using remote sensing, computer modeling and field validation.

Tiger range, 1900 and today (current or potential)

Tiger and leopard habitat Varied topography, climate and vegetation

Tiger and leopard habitat in the Russian Far East: A vast landscape shaped by disturbance. Tigers and leopards generally avoid conifer forests, burned areas and human activity ( Miquelle et al 2005). Logging, both small scale and commercial . Seasonal fires in agricultural areas Anthropogenic development Highways and railroads cross wildlife areas.

Amur (Siberian) tigers ( Panthera tigris altaica ) + 331-393 adults and sub- adults (20 – 36 mos.) in the Russian Far East (2005 census (Miquelle et al 2007)). + No more than 15 – 20 Amur tigers in China (Chinese gov’t 2007, Yang et al 1998, Sun et al 1998) + Endangered species (IUCN/World Conservation Union 2008) + Numbers are stable to slightly decreasing (Miquelle et al 2005) + Greatest threats – loss of habitat, insufficient prey, poaching (primarily for bones and fur).

Relationship of wildlife to vegetation Amur leopard (left) and Amur tiger (right) photographed by infrared camera trap in Primorskiy Krai, RFE. Photo – Wildlife Conservation Society The principle prey of Amur tiger and Amur leopard are ungulates – red deer ( Cervus elaphus ), sika deer ( C. nippon ), wild boar ( Sus scrofa ), and roe deer ( Capreolus capreolus pygargus ).

Tiger Prey – Red deer ( Cervus elaphus ) + Primary prey of the Amur tiger (Miquelle et al ,1999) + Similar to the elk (or wapiti) in North America + Most often found in riverine areas (Korean pine, oak, birch, other deciduous vegetation) + Avoid spruce/fir and larch forests + Eat stems, twigs, leaves of broadleaved trees and shrubs, herbs and sedges, forbs, lichens, fruits, fungi. Also eat willows, poplar, mountain ash, oak, cowberries, and blackberries

Tiger & leopard prey – wild pig ( Sus scrofa ) +Tigers range wherever wild pigs are found (Miquelle et al 1999) + Pig numbers may be declining because of poaching ( Stephens et al 2006 ) + Mostly found in Korean pine forests + Eat Korean pine nuts, plant roots, acorns, soil invertebrates, carrion + Range is limited by deep snow

Tiger & leopard prey – Sika deer ( Cervus nippon ) + May be expanding range northward and displacing red deer as climate warms (Stephens et al 2006) + Range limited by deep snow + Approx 117 kg (male) & 73 kg (female), i.e. 1/2 - 2/3 the size of red deer + Found in oak forests. + Eat bark, twigs, buds, acorns. In summer, eat fungi, herbs

Tiger and leopard prey – Siberian Roe deer ( Capreolus capreolus pygargus ) • Small (about 28 kg) (Pasternak, 1955), wide-ranging, ecologically adaptable. • Eat leaves and green shoots in summer; buds, branches, twigs, dry leaves, pine needles, later juniper, algae (for salt). • Found in sparse forests with young deciduous trees and dense undergrowth and in clearings (Heptner et al , 1988). C. Capreolus pygargus by Komarov in A.N. Heptner 1988

Tiger prey – Moose ( Alces alces ) + Ecologically very adaptable to different habitats. + Mostly range north of tiger habitat + In RFE, presence is associated with fir and larch forests, which tigers avoid

Ungulate (hooved animals) presence is associated with Korean pine ( Pinus korainsis ), and broadleaf temperate forests, especially those including oak ( Quercus mongolica ). (Miquelle et al 1999) Alexander Omelko, PhD, Institute of Biology and Soil Science, Far Eastern Branch of the Russian Academy of Sciences, Pine nuts and acorns are important food sources for ungulates and small mammals, especially during harsh winters.

Summary of relationships between prey presence and forest type, based on track encounter rate. “+” = tracks encountered most frequently “ – “ = habitats avoided ( Blank space means forest type is used in accordance with its availability (Stephens et al 2006 )). Prey Species – Vegetation Relationship Vegetation type Red Wild Sika Roe Moose deer boar deer deer Riverine (oak-birch, Korean + + - pine-deciduous, spruce-fir) Oak + + - Birch/aspen - Pine-deciduous - - - Korean pine + - - - Larch - - - - + Fir - - - - +

Key Year 2 Project Activities • Expanding the FAREAST model across a ~300,000 km 2 landscape representing Amur tiger and Amur leopard habitat. • Validating FAREAST model output against MODIS- and lidar- based interpretations of forest types, forest structure (LAI), biomass and canopy height. • Analyzing climate scenarios and data sets to determine which are best suited for climate change simulation in the study area. • Refining Amur tiger and Amur leopard habitat boundaries and assessing predator-prey-vegetation relationships using resource selection function analysis. • Building computer model to identify areas and nature of past disturbance based on current forest composition and characteristics.

FAREAST model (Yan & Shugart 2005) • An individual-tree, gap-based model that simulates growth in a single location and demonstrates forest succession leading to mature tree stands. • Incorporates: – Characteristics and requirements related to growth, mortality and regeneration for 44 tree species – Site characteristics, such as elevation, soil moisture and nutrients – Climate parameters, such as temperature and precipitation • Successfully simulated forest composition in terms of basal area across an elevational gradient at Changbai Mountain in northeastern China and simulated forest composition and successional patterns in terms of biomass at 23 of 31 sites in Russia. Was able to simulate net primary production (kg C m -2 yr -1 )(NPP) • versus observed NPP at 593 Forest Survey Stations in China.

FAREAST: A Boreal Forest Simulator Sub-models: Regeneration: Growth: •Available Light •Available Light •Soil Moisture •Soil Moisture •Site Quality •Site Quality •Depth of Thaw •Growing-Degree •Seed Bed Days •Seed Availability •Depth of Thaw •Sprouting •Diameter •Layering •Age •Height Environment: Mortality: •Temperature •Stress •Precipitation •Fire •Insects •Age

For each year at one site or point, 200 plots of 0.05 hectares (500 m 2 ) are run. Temperature and precipitation vary randomly within constraints of average monthly standard deviation for these characteristics. 200 plots of 0.05 ha 1 site

Model was developed based on the Changbai Shan mountain, China, vegetation gradient

Tests of the FAREAST Model on Changbai Mountain gradients Actual Versus Observed Basal Area by Species at Four Elevations 35 y = 0.8546x 30 R 2 = 0.8539 Model Prediction 25 1 to 1 line 20 15 10 5 0 0 5 10 15 20 25 30 35 Actual Data

End of 20th century modeled and observed monthly temperature (C) at Primorskiy Krai weather stations . (T. V. Loboda, University of MD, Dept. of Geography, 2009) 25 20 15 10 Temperature (C) 5 0 -5 -10 -15 -20 -25 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Observed GFCM21 MPEH5 NCCCSM HADCM3

End of 20th century modeled and observed precipitation (mm) (T. V. Loboda, University of MD, Dept. of Geography, 2009) 160 140 120 Precipitation (mm) 100 80 60 40 20 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Observed GFCM21 MPEH5 NCCCSM HADCM3

By running the FAREAST model (200 simulated plots for 700 years starting with an open plot) for 234 weather stations in the NEESPI region, one obtains the expected mature forest composition. J. Shumann, Univ. of Virginia Size of circles indicates the biomass of mature (700-year- old) forests across the NEESPI region.

By running the FAREAST model (200 simulated plots for 700 years starting with an open plot) for 234 weather stations in the NEESPI region, one obtains the expected mature forest composition. Legend J. Shumann, Univ. of Virginia Size of pie slices indicates the biomass composition of mature forests across the NEESPI region.

Fig 1. Two InSAR images developed from Dual-pol L-band Synthetic Aperture Radar (SAR) data for the same field study location show the rough terrain in the region, which causes difficulties in digital image classification. SRTM DEM data, and the DEM generated from the InSAR data, are being used to make terrain corrections and perform geo-coding of the radar data. (G. Sun, Univ. of Maryland) Fig. 1-B - false color (bands 4,3,2) Landsat Fig. 1-A Interferometry Thematic Mapper image Land Use (ILU) data Fig. 2. Land covers of study area (MODIS land cover product: IGBP classification. (Loboda & Csiszar, 2007))