High Resolution Imagery for the Validation of Moderate Resolution Canopy & Biomass Maps from NASA Satellite Remote Sensing!

Transcription

1 High Resolution Imagery for the Validation of Moderate Resolution Canopy & Biomass Maps from NASA Satellite Remote Sensing! Mark Chopping! Department of Earth & Environmental Studies, Montclair State University, Montclair, NJ 07043! Montclair State University Sustainability Seminar Series, January 21, 2014

2 High Resolution Imagery for the Validation of Moderate Resolution Canopy & Biomass Maps from NASA Satellite Remote Sensing! Acknowledgments!! Malcolm North (USFS, Davis, CA) provided the Teakettle Ecosystem Experiment field inventory database and 2012 remeasure update).! Rocio Duchesne (PhD candidate, Montclair State University Env. Man. Program) applied a version of CANAPI for numerous sites in Arctic tundra.! Wayne Rasband, National Institutes of Health, Bethesda, MD: ImageJ author.! Xiaohong Chopping contributed early suggestions on the CANAPI method.! This research was supported by awards NNX08AE71G (NASA Earth Observing System), NNX09AL03G (Terrestrial Ecology) and NNX11AF90G (Science of Terra and Aqua) and JPL subcontract # to MC.!

3 High Resolution Imagery for the Validation of Moderate Resolution Canopy & Biomass Maps from NASA Satellite Remote Sensing! Additional Acknowledgments! MISR data: NASA Langley Research Center Atmospheric Science Data Center! and Michael Bull (NASA/JPL).! CLPX data: National Snow & Ice Data Center/CLPX (Miller, S.L CLPX- Airborne: Infrared Orthophotography & LIDAR Topographic Mapping) Boulder, CO.! US-FIA Maps and FIA data for the Interior West: US Forest Service, Rocky Mountain Research Station, Ogden, UT (thanks to Ron Tymcio).! VCF, Science Data Buy, and SRTM data: Matt Smith and the Global Land Cover Facility, University of Maryland, College Park, MD.!

4 Overview! 1. Science Goals and the Need for Validation Scientific Motivation Some Mapping Results Comparisons (not validation!): NASA Vegetation Continuous Fields (MODIS VCF) US Forest Service Interior West (MODIS+other data) 2. Validation Work 2.1 Colorado/Wyoming: CLPX lidar+airphoto 2.2 California Sierra Nevada NF: LVIS+CANAPI 2.3 Arctic tundra (N. Slope of Alaska): CANAPI 2.4 Sierra Nevada: Aboveground Biomass: CANAPI 3. Conclusions

5 Big Science Questions:! THE ICE SHEETS ARE SHRINKING! Etc! SURFACE AIR TEMPERATURE CHANGE PER DECADE, (NASA)!

6 Forest Loss from Pine Beetle: Out West! Is the Carbon Sheet Shrinking?! Bark beetle in Western forests (and elsewhere)! coming to a forest near you soon? Absolutely!

7 Forest Loss from Pine Beetle: now Out East too! An aerial view of Union Lake in Millville, NJ. The yellowing trees show the extent of the damage done by southern pine beetles

8 The Big C Sheet Question! NASA Carbon Cycle and Ecosystems & Terrestrial Ecology Programs Science Question: How are the Earth s carbon cycle and ecosystems changing and what are the consequences for the Earth s carbon budget, ecosystem sustainability, and biodiversity? To address these questions, we must be able to map the distribution of aboveground woody carbon stocks. Today, we use satellite-based observations to estimate annual vegetation net primary production (NPP) and trends.

9 The Big C Sheet Question! NPP from NASA s! MODIS sensors! Source: Zhao M and Running S W 2010 Drought-induced reduction in global terrestrial net primary production from 2000 through 2009 Science !

10 The Big C Sheet Question! Trends?!

11 The Big C Sheet Question!??! State of the C Cycle Report (SOCCR)!??! Forest and shrubs have the largest magnitudes & absolute uncertainties as carbon sinks after fossil fuel emissions.! Forest currently a sink but that can change!

12 The Big C Sheet Question! Emissions from forest degradation are very uncertain and vary from 0 to > 100% of emissions from deforestation Emissions from forest degradation & growth could offset the emissions from deforestation or more than double them. -Houghton & Goetz, EOS Oct 21, 2008 Highly relevant to e.g., REDD+ (a hotly-contested policy instrument)

13 The Big C Sheet Question! BUT: we cannot currently say what fraction of the landscape is in recently disturbed or rapidly regrowing stands. Currently, we use remotely-sensed metrics of vegetation function, i.e. photosynthesis (NVDI, EVI) and structure: Leaf Area Index, fraction of tree cover (VCF). Vegetation structure is a key indicator of successional status. Two important and distinct metrics of vegetation structure are canopy height and canopy biomass (Wofsy and Moorcroft, NASA Veg3D & Biomass Workshop, March 2008, University of Virginia, Charlottesville).

14 Validation: The C Source/Sink Q Just Got A Boost*! NASA s Orbiting Carbon Observatory will help on the function side: it will help map C sources and sinks at moderate resolution (~3 km)! *Launched July 2, 2014!

15 Canopy Structure & Biomass Mapping is Useful! Canopy cover, crown height, and aboveground woody biomass are important parameters relevant to numerous science questions: carbon storage and cycling, susceptibility to wildfire, gaseous / particulate C losses from wildfire / pathogens / infestations, species change, ecological modeling & prediction, forest management, hydrology, biodiversity, wildlife habitat, surface roughness. Together with tree density and mean crown radius, knowledge of these canopy parameters is also needed for testing canopy reflectance models. Lidar and multi-angle remote sensing can help to map them.!

16 Initial Motivation: Validation of Forest Canopy Retrievals Sunlit background! Shaded background! Shaded crown! Sunlit crown! QuickBird 0.6 m pan image! Typical NASA Satellite Observation ~250 m! Trees and shrubs cast shadows and introduce reflectance anisotropy, providing an opportunity to map canopy structural information from NASA MODIS and MISR imagery via inversion of canopy reflectance models (e.g., average crown radius, tree height, % canopy cover, aboveground woody biomass). CR models predict what the sensor would see.

17 Comparison of MISR Cover vs NASA VCF % Tree Cover Maps! SHRUBS! 0.89! 0.29! VCF: NO SHRUBS! 0.27! tree! shrub! 0.01! 0.30! 0.00! 0.02! Fractional! Cover! Fractional! Cover! MISR/GO! 10 km! VCF! Rio Grande! riparian zone! Summerford!!!San Andres Mountains! Mountain! Left: MISR/GO Woody Plant Cover Right: MODIS Vegetation Continuous Fields % Tree Cover for the USDA, ARS Jornada Experimental Range!

18 Compositing on Model-Fitting min(rmse) Weeds Clouds/Cloud Shadows! Regional Aboveground Biomass! Regional Mean Canopy Height! Regional Forest Crown Cover! 100 km! Aboveground Biomass! 0.00! 6.70! >50.0! Tons/acre! 0.00! 15.0! >110.0! Mg ha -1! 100 km! Mean Canopy Height! < 1.00! 1.00! >20.00! meters! Fractional Crown Cover! 0.00! 0.29! Shrubs! 0.30! 0.90! Forest!

19 MISR/GO Forest Structure Mapping: USFS Comparison! TOPOGRAPHIC SHADING! MISR Crown Cover xxxxxx MISR Crown Cover xxxxxx R 2 = 0.68 (a)! R 2 = 0.33 (b)! R 2 = 0.74 (c)! OK! USFS Crown Cover USFS Weighted Height (m) USFS Biomass (tons/acre) R 2 = 0.78 R 2 = 0.69 R 2 = (d)! MISR Mean Canopy Height (m) MISR Mean Canopy Height (m) (e)! MISR Biomass (tons/acre) MISR Biomass (tons/acre) (f)! USFS Crown Cover USFS Weighted Height (m) USFS Biomass (tons/acre) (a)-(c) Retrievals vs. US Forest Service Map Data. The upper cluster of data points in (a) corresponds to locations affected by severe topography (d)-(f) Retrievals with screening for topographic effects using a Digital Elevation Model from the Shuttle Radar Topography Mission. Points with RMSE >= 0.01 and outliers ±2 st. devs. from the mean of crown cover were discarded, retaining 576 points (54%).

20 Southwestern US MISR/GO Maps for 2000 & 2009! MISR/GO AG Biomass 2000! USFS AG Biomass ~2002! MISR/GO AG Biomass 2009! 150! ~0.0! Mg ha -1! The USFS Aboveground Live Biomass Map, 2002 is based on Forest Inventory Analysis, MODIS, and other geospatial data.! The MISR/GO Aboveground Biomass Maps, 2000 and 2009 are from GO model inversion against red band BRFs.!

21 Rodeo-Chediski Fire, Arizona, 2002! Change in Aboveground Live Biomass from MISR (a)! MISR/GO! Biomass Change! ! (b)! Landsat Burn! Severity Map! +80! Mg ha -1! - 80! 5 km! N! Missing! data! White: Unburned Red: High! Orange: Moderate Green: Low! The MISR/GO biomass change map also matches Landsatbased Monitoring Trends in Burn Severity (MTBS) fire perimeters by burn severity OK, lovely. BUT

22 How Good are the Maps, Really? We Need to do Validation! Science is largely a process of not believing your results, We must try to break our ideas and models. In our 2009 Colorado Rocky Mountain Model Validation Study we used NASA Cold Land Processes Experiment (CLPX) data for forest and grasslands in the Colorado Rockies, via the National Snow & Ice Data Center (NSIDC):! High resolution discrete return lidar! High resolution aerial photographs!! However, these kinds of data are not widely available!

23 How Good are the Maps, Really? CLPX Validation Study!

24 How Good are the Maps, Really? Sierra NF Validation Study! Sierra Nevada National Forest Validation Study (2012) we used:!! NASA Laser Vegetation Imaging Sensor (LVIS), a full waveform lidar instrument (~20 m footprint)! High resolution QuickBird imagery, using only the panchromatic band, interpreted with CANAPI (Chopping, 2011) to extract canopy statistics: #density, crown radius, canopy fractional cover, tree height (where possible).!

25 How Good are the Maps, Really? Sierra NF Validation Study! The location of the study area in the Sierra Nevada National Forest in S. California (a) over a crown cover map (b) MISR BRDF model kernel weight composite (RGB = vol, geo, iso). The irregular area indicates the area surveyed by LVIS on day 4 of the September! 2008 campaign. The red rectangle shows the extent of the QuickBird image area for which we obtained high quality reference data. Black=missing data (clouds).!

26 How Good are the Maps, Really? Sierra NF Validation Study! Sierra Nevada National Forest Validation Study:! March 08: we hoped to use an algorithm developed by Dr Michael Palace developed for delineating crowns in closed tropical rainforest (Biotropica, 2008, link on website).! December 09: Dr Palace had the opportunity to apply this with our QuickBird pan imagery for the Sierra National Forest, CA. Unfortunately the results were poor: the crown maps output showed trees in clearings, on roads, clearly out of alignment with trees (see overleaf).! Reason: the method seeks crowns on the assumption that very bright pixels are probably crown centers (because the solar zenith angle is low in the tropics). This assumption is poor for mid-latitudes where bright pixels yield a crescent.!

27 Better Reference Data from High Resolution Imagery? Source: Gonzalez, P., G.P. Asner, J.J. Battles, M.A. Lefsky, K.M. Waring, and M. Palace Forest carbon densities and uncertainties from Lidar, QuickBird, and field measurements in California. Remote Sensing of Environment 114: Similar results were obtained for our area. So in early Jan 2010 I was thinking: now what? -- then set about trying to develop an alternative.!

28 CANAPI: Better Reference Data from High Resolution Imagery Rotated! QuickBird 0.6 m pan image! CANAPI is implemented as a macro in the NIH ImageJ package. It assumes oblique illumination of crowns resulting in sunlit crescents that can be isolated using convolution. The ImageJ particle analysis routine fits the smallest possible rectangle that encloses a crescent, giving the location of the crown center.!

29 Validation Data from High Resolution Panchromatic Imagery! 50 m! N! Scatter plot: CANAPI cover estimates vs Teakettle Experiment field estimates!

30 Validation Data from High Resolution Panchromatic Imagery! lo hi! LVIS (lidar) 20 m mean RH100* (interpolated) September, 2008! Tree heights indicated by points at crown centers! QuickBird 0.6 m pan image, 5 June, 2003! * RH100 = Relative Height at 100% of laser energy return = maximum height within the footprint.!

31 Validation Data from High Resolution Panchromatic Imagery! Relationship of CANAPI maximum values to LVIS RH100 values, excluding RH100 values < 3.0 m. The CANAPI heights are the means for each RH100 value (10 cm binning).!! If the range is restricted to! 3 < RH100 < 60 m (61% of the observations) then the R 2 is 0.94, significant at the 99% level, with a relative RMSE of 0.25 m.!! Is bias owing to lidar overestimation w/slope?!

32 CANAPI: Better Reference Data from High Resolution Imagery CANAPI Demo in the! Sierra Nevada National Forest! using QuickBird 0.6 m spatial resolution panchromatic image! (>400 MB file)!

33 2012 Sierra Nevada National Forest Study! RTLS! Operations to retrieve and validate fractional crown cover and mean canopy height via GO model inversion against MISR 672 nm band BRFs!

34 Sierra Nevada NF: CANAPI-driven Simulations! Red Band BRF MISR BRF Simulations!! Observed MISR and simulated 672 nm BRFs, indicating the contributions from the trees and the background. The GO model was driven by CANAPI tree number density and mean crown radius, LVIS RH100 (!b/ r), and the RossThick-LiSparse BRDF model representing the background (model kernel weights were PREDICTED via regression equations).!! Results were obtained for 1048 sites: 97% provided R 2 > 0.7 with RMSE = 0.02.! View Zenith Angle ( )

35 2012 Sierra Nevada National Forest Study!

36 CANAPI: Application in Northern High Latitudes! We used a second version of CANAPI to obtain statistics (mainly radii and fractional cover) for tall shrubs in Arctic Tundra and Taiga.! The main application was for our tundra study sites on the North Slope of Alaska, using QuickBird panchromatic imagery.! The second application was for part of the Kola Peninsula, Russia (taiga-tundra interface), using Google Earth screen-dumps.! A third version was developed for Siberian taiga.! Here I will show some results from Alaska and Siberia.!

37 CANAPI: Application in Northern High Latitudes! N. Slope of Alaska! 1893_ )) CANAPI runs by Rocio Duchesne, PhD candidate in the Env. Man. Program!

38 CANAPI: Application in Northern High Latitudes! N. Slope of Alaska! 1893_ ) CANAPI runs by Rocio Duchesne, PhD candidate in the Env. Man. Program!

39 CANAPI: Application in Northern High Latitudes! N. Slope of Alaska! 1884_ )) CANAPI runs by Rocio Duchesne, PhD candidate in the Env. Man. Program!

40 CANAPI: Application in Northern High Latitudes! N. Slope of Alaska! 1884_ )) CANAPI runs by Rocio Duchesne, PhD candidate in the Env. Man. Program!

41 CANAPI: Application in Northern High Latitudes! N. Slope of Alaska! 1884_ )) CANAPI runs by Rocio Duchesne, PhD candidate in the Env. Man. Program!

42 CANAPI: Application in Northern High Latitudes! N. Slope of Alaska! 1884_ ) CANAPI runs by Rocio Duchesne, PhD candidate in the Env. Man. Program!

43 CANAPI: Application in Northern High Latitudes! N. Slope of Alaska! 1884_ )) CANAPI runs by Rocio Duchesne, PhD candidate in the Env. Man. Program!

44 CANAPI: Application in Northern High Latitudes! N. Slope of Alaska! 1880_ )) CANAPI runs by Rocio Duchesne, PhD candidate in the Env. Man. Program!

45 CANAPI: Application in Northern High Latitudes! N. Slope of Alaska! 1880_ )) CANAPI runs by Rocio Duchesne, PhD candidate in the Env. Man. Program!

46 CANAPI: Application in Northern High Latitudes! N. Slope of Alaska! 1880_ )) CANAPI runs by Rocio Duchesne, PhD candidate in the Env. Man. Program!

47 CANAPI: Application in Northern High Latitudes! N. Slope of Alaska! 1880_ )) CANAPI runs by Rocio Duchesne, PhD candidate in the Env. Man. Program!

48 CANAPI: Application in Northern High Latitudes! N. Slope of Alaska! 1880_ )) CANAPI runs by Rocio Duchesne, PhD candidate in the Env. Man. Program!

49 CANAPI: Application in Northern High Latitudes! N. Slope of Alaska! 1901_ )) CANAPI runs by Rocio Duchesne, PhD candidate in the Env. Man. Program!

50 CANAPI: Application in Northern High Latitudes! N. Slope of Alaska! 1893_ )) CANAPI runs by Rocio Duchesne, PhD candidate in the Env. Man. Program!

51 CANAPI: Application in Northern High Latitudes! N. Slope of Alaska! 1880_ ) CANAPI runs by Rocio Duchesne, PhD candidate in the Env. Man. Program!

52 CANAPI: Application in Northern High Latitudes! CANAPI Demo in the! Siberian Taiga using WV-1 1 m spatial resolution panchromatic image! (developed for GSFC-led CCS project under PI Jon Ranson).!

53 Sierra NF: Using CANAPI to Test Biomass Mapping! The idea was to use estimates for the 200 x 200 m Teakettle Ecosystem Experiment plots -- based on Jenkins et al. (2003) allometric relationships -- to validate a CANAPI-based biomass prediction method.! If this works out CANAPI could then be applied over larger areas, allowing evaluation of other biomass maps (radar-, MODIS-, lidar-, and MISRbased estimates).!

54 Using CANAPI to Test Biomass Mapping - Data Sets! LIDAR: NASA LVIS RH50 returns (Chopping et al. 2012)! RADAR: National Biomass & C Dataset (Kellndorfer et al. 2013)! MODIS: USFS MODIS-based biomass map (Blackard et al. 2008)! MISR/BRT: RTLS kernel weights + An BRFs! biomass (Boosted Regression Trees)! MISR/GO: Red band BRFs/Geometric Optical model inversion to retrieve crown cover and mean canopy height " biomass! CANAPI/QB: Tree number density, mean radius, and crown cover derived from QuickBird 0.6 m spatial resolution panchromatic imagery using the CANAPI algorithm (Chopping 2010)! GROUND: Teakettle Ecosystem Experiment (TEE) database from field inventory (North et al. 2005)!!All estimates were averaged for the TEE plots and a 32 row x 34 column MISR 250 m cell study region in the Sierra Nevada National Forest (southern California).!

55 Example Teakettle Ecosystem Experiment (TEE) Plot TEE 200 x 200 m plots! 3 km! TEE plot us2!

56 How Well Can We Map Biomass?! Per-tree biomass estimates for TEE 200 x 200 m plots vs mean NBCD biomass: (a) TEE-based (Jenkins) (b) CANAPI-based (Jenkins, with TEE-based dbh prediction using crown radius) (c) CANAPI-based (Bar Massada et al. (2006) method based on crown radius and tree heights) (a)! Uh-Oh! NOTE Y-axis SCALES (b)! (c)!

57 How Well Can We Map Biomass?! Biomass mapped at larger scales: the spatial correspondence is clear but magnitudes differ! MISR/GO NBCD MISR/BRT MODIS CONUS

58 Best matches with NBCD: LVIS RH50 and MISR/BRT! MISR/BRT! LVIS RH50! MISR/GO! (a)! (b)! (c)! Relationships between the NBCD 2000 biomass data, v.2 (file: NBCD_MZ06_FIA_ALD_biomass) and MISR, LVIS, and CANAPIbased estimates, for a 6800 ha area in the Sierra Nevada national Forest (32 x 34 cells of 250 m 2 ). CANAPI estimates of tree density and crown radius were obtained using 0.6 m panchromatic QuickBird imagery. (d)! CANAPI! (Paine & Hann/ Jenkins)! (e)! MODIS! (USFS)!

59 Vs Teakettle Remeasure Database! Relationship between the NBCD 2000 biomass data, v.2 (file: NBCD_MZ06_FIA_ALD_b iomass) and Teakettle Remeasure (2012) -based estimates.

60 Vs Teakettle Remeasure Database! Relationship between the Teakettle Remeasure (2012)-based estimates and CANAPI/Jenkins-based estimates (allometry based on dbh).

61 Vs Teakettle Remeasure Database! Relationship between the Teakettle Remeasure (2012) -based estimates and the CANAPI/ BarMassada-based estimates (allometry based on radii).

62 Conclusions 1/2! Validation data are essential: for understanding the quality of mapped estimates of some vegetation canopy parameter (cover, LAI, tree density, mean tree height, radius ) for testing CR models in forward mode. CANAPI is a semi-automated method that was designed to provide bulk canopy density, crown radius, and cover statistics for the validation of moderate resolution products (VCF, GO, USFS).

63 Conclusions 2/2! CANAPI can also be used to validate other, higher resolution products or to make high resolution maps over large areas, given sufficient resources. CANAPI has limitations; it: Makes mistakes! Requires some human input/supervision Currently runs only under ImageJ Does not yet output vector data but it can easily be extended/modified We ve made a lot of progress but still have work to do. ~ END ~