Forests in a dynamic world

Transcription

1 SE Region Global Dynamics Monitoring and assessment Predictions of future productivity Forests in a dynamic world Forest ecosystem services Modeling influences of silviculture on productivity Disturbance and recovery Role of forests in influencing radiative forcing

2 The VT Team Field Studies Forest Science Modeling Remote Sensing

3 36.5 m 34.4 m 26 m Thermistor/hygristor Eddy covariance I R G A Net radiometer Diffuse radiometer Thermistor/hygristor 20 m Rain Gauge Anemometer Phenocam Aerosol Inlet Anemometer Quantum Sensors Gas Inlet Sweet Briar College Land-Atmosphere Research Station (SBC-LARS) Thermistor/hygristor 2 m Thermistor/hygristor Soil temp., moisture, heat flux (3x) Biomet logger Quantum Sensors OPS inlet Computer & logger Barometer NO x SO 2 O 3 Aerosol SMPS Quantum Sensor 2-D sonic Rain Gauge

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5 Carbon storage response to environmental change and forest management Change in ecosystem carbon ( )

6 NLCD Tree Canopy Cover and Change 2011 map completed in 2014 Current research focus Develop approach for 2016 tree canopy cover map Understand the capacity to responsibly identify Subtle change Abrupt change Test a suite of Landsat time series approaches to predict 2016 status and change. Examples: VCT Time series control charts Examine the potential role of lidar for post 2016 efforts.

7 Pilot Areas NLCD Tree Canopy Cover Western Study Area Southern Study Area

8 Percent TCC Uncertainty

9 Disturbance: Example EWMA Flagged Pixels EWMA value, z n = (1 ʎ) z n 1 + ʎx n where ʎ = tuning parameter between 0 and 1 x n = pixel value at time nand z n 1 = EWMA value at time n 1

10 Growth and Loss Detection EWMACD/LCMS Dynamic retraining functionality added Code actively being developed and distributed (Creative Commons) doi: /W4WD3 XHK Source: Brooks, E. B., Thomas, V. A., Wynne, R. H., Blinn, C. E., and Coulston, J. W. (2014) On the fly massively multitemporal change detection using statistical quality control charts and Landsat data. IEEE Transactions on Geosciences and Remote Sensing, 52(6),

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12 Forest Conversion Hotspots by County

13 Trajectory based methods to characterize the temporal dynamics of vegetation (distrubance and regrowth) Vegetation Index Vegetation index trajectory 13

14 Legend Non-mine

15 NAIP

16 SE Region Global Dynamics Monitoring and assessment Predictions of future productivity Forests in a changing world Forest ecosystem services Modeling influences of Silviculture on productivity Disturbance and Recovery Role of forests in climate change mitigation

17 Ecosystem Services Open Lands and Air Quality Also Added inforest.frec.vt.edu

18 Forest Carbon Stocking, current age, and dominant height required for Fastlob; potential for remotelysensed inputs FVS uses basal area and age Stands can be grown for userspecified number of years Supported treatments include N and P fertilization as well as thinning No current way to handle disturbance risks No H 2 O quality & C costs links No soil respiration yet

19 SE Region Global Dynamics Monitoring and assessment Predictions of future productivity Forests in a changing world Forest ecosystem services Modeling influences of Silviculture on productivity Disturbance and Recovery Role of forests in climate change mitigation

20 Prediction of Canopy Heights over a Large Region Using Heterogeneous Lidar Datasets: Efficacy and Challenges

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22 Signal noise Lots of data Ice SAT II 22

23 Top of Canopy and Ground 23

24 SE Region Global Dynamics Monitoring and assessment Predictions of future productivity Forests in a changing world Forest ecosystem services Modeling influences of Silviculture on productivity Disturbance and Recovery Role of forests in climate change mitigation

25 Lidar/Hyperspectral for Forest Physiology

26 Ecosystem Nitrogen Retention

27 Canopy Biochemistry

28 Leaf Area Index From Landsat Best Combined (L7 L8) LAI Model Ground LAI = SR R sq = 78.7 RMSE = Blue circles are the minimum LAI and the red triangles are the peak LAI data. Simple ratio based on surface reflectance.

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30 SE Region Global Dynamics Monitoring and assessment Predictions of future productivity Forest ecosystem services Forests in a changing world Modeling influences of Silviculture on productivity Socioeconomics Disturbance and Recovery Role of forests in climate change mitigation

31 Regionally Specific Drivers of Land Use Transitions and Future Scenarios: A Synthesis Considering the Land Management Influence in the Southeastern US VT Team + D. Wear and B. Mei

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35 Regional Model Upscaling PINEMAP 2014 Annual Meeting Currently, there are three forest ecosystem modeling efforts underway in the PINEMAP Modeling Integration Platform Empirical growth and yield model (G&Y) Loblolly Brief summary pine specific of status version of integration of the physiological platform, including principles successes for and predicting challenges growth and future model directions (3PG or plans. lob ) Water supply and stress index model (WaSSI) For model comparison, all outputs were structured and aggregated around the 12 digit hydrological watershed units (HUC 12) Input climate data thus far has included daily outputs of CanESM2 climate models, downscaled to 1/16 th degree resolution Input data range from Two different future scenarios Climate RCP45 projection RCP85 projection (business as usual) CO 2 fertilization effect Under development Basic Operating Level Inputs Table 1. Comparison of models. 3PG lob G&Y WaSSI Stand (physiological) Stand (statistical) Watershed Climate MACA, two scenarios MACA, two scenarios MACA, two scenarios Soil SSURGO SSURGO STATSGO Site Index FIA Dynamic -- Other Species parameters -- NLCD Most regional modeling was computed at the HUC 12 and/or MACA climate spatial scale Figure 1. Intersection of a HUC 12 watershed with MACA climate raster data. Pine Integrated Network: Education, Mitigation, and Adaptation Project (PINEMAP) is a Coordinated Agricultural Project funded by the USDA National Institute of Food and Agriculture, Award #

36 Regional Model Upscaling PINEMAP 2014 Annual Meeting Growth and Yield (G&Y) is an empirically fitted model based on plot measurements across most of the PINEMAP region Two step process Estimate site index from soil and climate inputs (Sabatia and Burkhart, 2014) Brief summary of status of integration platform, including Estimate productivity from site index, basal area, successes and challenges and future directions or plans. and planting dentisy (Gyawali and Burkhart, 2014) Figure 1. Sites used to calibrate the site index estimation process. (from Sabatia and Burkhart, 2014) Soil Data Soil Data Aggregation to HUC 12 features Regionalizati on Process Hydrological Units (HUC 12) HUC/Climate Feature Intersection Site Index Output Growth & Yield Climate Data (Yearly) Figure 2. Methods applied to regionalize the G&Y model. Figure 3. Checking whether aggregation to HUC 12 could be justifiably achieved before site index estimation. Pine Integrated Network: Education, Mitigation, and Adaptation Project (PINEMAP) is a Coordinated Agricultural Project funded by the USDA National Institute of Food and Agriculture, Award #

37 Regional Model Upscaling PINEMAP 2014 Annual Meeting 3PG is a simple, process based model to predict growth and development of even aged stands Runs on monthly time step Uses basic mean monthly climatic data, and simple site factors and soil descriptors Brief summary of status of integration platform, including Note successes that site and index challenges for use in and 3PG future is derived directions from FIA or plans. data There are approximately 13,000 FIA plots listed as artificially planted loblolly pine stands Approximately 3,000 of these are listed as having been recently commercially thinned These provide the basis for estimating site index across the region Soil Data Soil Data Aggregation to HUC 12 features Regionalizati on Process Figure 1. Volume outside bark, predicted by 3PG lob for a range of sites. 3 PG lob Output Hydrological Units (HUC 12) HUC/Climate/FR Feature Intersection Climate Data (Yearly) Site Index, converted to Fertility Rating (FIA, interpolated to HUC) Physiological parameters (optimized for loblolly pine) Figure 2. Methods applied to regionalize the 3PG lob model. Figure 3. Distribution of site index, from unthinned FIA based commercial plots. Pine Integrated Network: Education, Mitigation, and Adaptation Project (PINEMAP) is a Coordinated Agricultural Project funded by the USDA National Institute of Food and Agriculture, Award #

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39 Inputs: Soil Soils data USGS Soil Survey data (SSURGO), 2013 edition Fine spatial resolution (some polygons as narrow as tens of meters) Available water in the soil was derived from the added value table and joined directly to mapunit polygon features Soil texture component polygon features were aggregated by weighted area to mapunit polygon features Available Water in the Soil Percent Sand 0 100

40 Inputs: Climate Climate Data MACA downscaled climate data from Daily temporal resolution, 1/16 degree spatial resolution Temperature (Mean, 2005, Celsius) Precipitation (Total, 2005, mm)

41 Inputs: Soil and Climate For efficient processing of G&Y and 3PG lob, the soil HUC features were intersected with an index grid for the climate data Each sub feature thus had a unique combination of soil and climate data These sub features were the primary input units for G&Y Will be similar for 3PG lob Sub features associated with an example HUC

42 Inputs: Climate

43 Region wide observations

44 Data Assimilation of Pine Plantation Ecosystem Research (DAPPER) System PINEMAP stand biomass estimates Control plots Fertilization plots Duke FACE productivity &canopy transpiration Drought x Fertilization plots Drought plots Non PINEMAP data US NC2 Ameriflux Gross Ecosystem Productivity & ET Data uncertainty (allometric relationships) Data assimilation (MCMC MH) Parameter Priors Posterior parameters distributions SSURGO Soils (texture, ASW) Observed climate dataset (Idaho group) Model uncertainty 3PG Model (Includes carbon and water) User site of interest or regional grid Downscaled future climate data (RCP 4.5 & 8.5) 3PG G&Y Forecast of future growth

45 Known Unknowns Parameter uncertainty (data assimilation) Climate uncertainty (multiple climate models) G&Y or 3PG Predictions Model uncertainty (data assimilation) Climate scenario (multiple climate scenarios) Data uncertainty (data assimilation)

46 Forecasts

47 Annika Jersild Masters Thesis Forecasts

48 Parting thoughts Dynamic economic and biophysical environment, with new opportunities and risks, requires careful thought to what new data are needed and what modeling approaches are best Improved monitoring and modeling with explicit uncertainty improves decision making and the bottom line

49 The VT Team Field Studies Forest Science Modeling Remote Sensing

50 Forecasts: RCP 8.5