MICROWAVE REMOTE SENSING FOR THE MONITORING OF FOREST ECOSYSTEMS

Transcription

1 MICROWAVE REMOTE SENSING FOR THE MONITORING OF FOREST ECOSYSTEMS Simonetta Paloscia, Emanuele Santi, Simone Pettinato CNR-IFAC, Firenze (Italy)

2 Introduction & Outline Microwave sensors are well-know to be sensitive to water content in the observed bodies Both active and passive microwave sensors can be therefore used for obtaining information on several parameters of the hydrological cycle and in particular soil moisture, vegetation biomass, snow cover and depth, and so on. The sensitivity to these parameters changes according to the observation paramters (i.e. frequency, polarization, incidence angle) Usually, lower frequencies (i.e. P and L bands, corresponding to wavelenghts in the order of cm) are the most suitable for the estimate of forest biomass; whereas higher frequencies (C and X bands, 3-6 cm) are more suitable for indentifying surface features and obtaining leaf information. The retrieval of the geophysical parameters is a rather complicated matter, since many combinations of observation parameters can give the same results. In this work, a retrieval based on statistical methods by using Artificial Neural Networks will be presented for estimating forest biomass in two test areas in Italy

3 Hydrological Cycle Snow Cover Rainfall Evaporation/Evapotranspiration Erosion PWC/LAI Tsup SMC Runoff Infiltration

4 Interactions between e.m. wavelength and surface The interaction between SAR signal and the surface depends on: wavelength, incidence angle, polarization, surface type (forest, agricultural crops, etc.). C-, X-band L-band At C and X bands information on surface features can be obtained (first layers of vegetation and soil) At L-band information on thicker layers of vegetation and soil and also of soil under vegetation cover can be retrieved

5 Forest measurements using MW sensors Active Sensors (Radar) Satellite High/Medium resolution (10-30 m) Aircraft High Resolution (<10 m) Maps on local scale, hydrological cycle at a basin scale, identification of risk areas (landslides, flooding, etc.) Passive Sensors (Radiometers) Satellite Low resolution (40-50 Km) Maps on global scale (hydro-meteorological and climatological applications) Aircraft High/Medium resolution (50 m) Maps on local scale, hydrological cycle at a basin scale, identification of risk areas (landslides, flooding, etc.)

6 SIR-C/X-SAR images of Montespertoli area C-band L-band P-band

7 Sensitivity to forest biomass - AIRSAR data backscattering coefficient ( ) vs. Woody Volume

8 Deforestations and fires: USA (Yellowstone Park), 1994 In 1988, the park was burned by one of the most widespread fires in the Rocky Mountains in the last 50 years. Yellowstone Lake Blue: rivers/lakes Brown: non-forest areas with crown biomass < 4 t/ha Light brown: canopy burn (4-12 t/ha). Yellow: mixed burn canopy (12-20 t/ha) Light green is mixed burn forest (20-35 t/ha) Green is non-burned forest (> 35 t/ha) L-HV Map of biomass

9 SIR-C/X-SAR (L-band) RACO, MICHIGAN SUPERSITE Above-ground Biomass Map April 9, 1994

10 a Test sites (Italy) b San Rossore (a): Cost of Tuscany (4800 ha). The land cover is dominated by the presence of: Mediterranean Pine forest (Pinus pinaster Ait., P. pinea L.). Quercus ilex L. Broadleaved species (Mediterranean macchia ). Molise (b): South-West part of Molise Region (31950 ha). Forests cover about the 64% of the area. Presence of: Turkey oak (Quercus cerris) (29.81%) Downy oak (Quercus pubescens) (28.69%) Hop Hornbeam (Ostrya carpinifolia) (17.70%) Beech (Fagus sylvatica) (9.04%) Holm oak (Quercus Ilex) 1412 (6.88%)

11 Ground-truth data San Rossore: Woody volume map derived from lidar acquisition of June 2009 (L. Bottai, L. Arcidiaco, M. Chiesi and F. Maselli, "Application of a single-tree identification algorithm to LiDAR data for the simulation of stem volume current annual increment", J. Appl. Remote Sens. 7(1), (Sep 24, 2013). ; Molise: measurements carried out on 60 forest plots acquired in June 2009 (provided by Universtà del Molise, by courtesy of G. Chirici). 0 m3/ha 700 Woody biomass: m 3 /ha

12 PALSAR (L-band) 7/6/2009 San Rossore R:HH G:HV B:VV An RGB visualization of ALOS polarization bands allowed a preliminary classification of forest areas

13 PALSAR (L-band) 19/06/ Molise R:HH - G:HV - B:Empty The same procedure was applied to Molise area and, in spite of the complex orography, forest plots have been correctly identified.

14 Sensitivity to Biomass (Woody Volume, m3/ha) Experimental analysis (L-,C-Band) Algorithm implementation (ANN) Biomass retrieval

15 (L-band) Biomass Historical AirSAR + SIR-C + PALSAR

16 ANN Overview The ANN considered in this study are Multi-Layer Perceptrons (MLP) The algorithm chosen for the training phase is the back-propagation (BP) learning rule, an iterative gradient descent algorithm designed to minimize the mean square error between the desired target vectors and the actual output vectors. The optimal ANN architecture was defined after an optimization process: three hidden layers of neurons, inputs are the SAR acquisitions at the available frequencies and polarizations, the incidence angles and the ancillary data.

17 Retrieval examples (BIOSAR 2010: L+P bands) Data derived from the BioSAR airborne SAR acquisitions at P- and L-band, acquired in fall 2010 in Sweden by the airborne system ONERA-SETHI, was used ( earth.esa.int/web/guest/picommunity). Lidar measurements of forest height (Norway spruce, Scots pine and birch) and other ground measurements were available in the test site (Remningstorp). The results are compared with lidar biomass data with satisfactory agreement (RMSE = 22.4 m 3 /ha)

18 Application of ANN algorithm to San Rossore 2009 forest data: PALSAR + ASAR (L+C bands) Data collected on San Rossore PINE forest in June 2009 ½ dataset randomly sampled for training ½ dataset for test (~ samples)

19 San Rossore Biomass Maps ANN biomass retrieval map Ground truth biomass map (Lidar)

20 Molise Biomass maps ANN biomass retrieval map Ground truth biomass map (Lidar) The validation (R= 0.7, RMSE = 55.9 m3/ha) was affected by the limited biomass range and the complexity of the orography.

21 Conclusions A consistent dataset of SAR images at L,C and X-bands was collected over three test areas in Italy (S. Rossore and Molise). A first analysis based on multi polarization, multi-temporal images pointed out that L-band is able to identify forest surfaces, whereas C-band is more sensitive to forest features (presence/absence of leaves). An experimantal sensitivity of backscattering coefficient (L-band) to forest biomass was pointed out by archive data. This sensitivity was confirmed by new data. On the basis of this sensitivity an inversion algorithm base on Neural Networks was implemented. The algorithm was trained on San Rossore test site and tested on both san rossore and Molise with satisfactory results (RMSE=25.41 m3/ha on San Rossore and m3/ha on Molise). Further test and validation of the algorithm should be advisable by extending the investigation to other datasets ant test areas.

22 Microwave radiometers (airborne) The FORMON 1999 Campaign: Test Sites Six forest plots were selected in Tuscany (Italy)

23 Test Areas Beech Area Turkey Oak Main Tree Specie Altitude (m) Density (n/ha) Basal Area (m 2 /ha) Height (m) Crown Trans. (%) Teso Beech (100%) ,4 20,1 42,8 Vallombrosa Beech (100%) ,0 28,1 30,2 Colognole Holm Oak (40%) ,0 13,3 28,8 Ulignano Turkey Oak (80%) ,2 12,3 20,5 Amiata Turkey Oak (80%) ,7 15,6 27 Cala Violina Holm Oak (70%) ,7 13,9 36,1

24 MW Radiometers Installation (I) June 15-16, 1999 June 24-25, 1999 Aircraft Freq. (GHz) Bands Pol. Q ARAT Fokker & 37 Ku, Ka H, V, ARAT Fokker & 10 C, X H, V 30

25 Radiometers Installation (II) June 29-30, 1999 Aircraft Freq. (GHz) Band Pol. Q Zenair 1.42 L H or V 30 Antenna

26 Results: Classification Ka+X-bands

27 Sensitivity to LAI (Ka band) and Biomass (L band) Ka-band L-band LAI

28 Conclusions (MW radiometers) An experiment was carried out in Tuscany in 1999 by using airborne multi-frequency airborne MW radiometers Different types of forests were investigated (beech, fir, Turkey oak, and Holm oak) A first analysis based on multi frequency data pointed out that higher frequencies (i.e. Ka and X bands) were able to identify different forest types, whereas L band was definitely more sensitive to forest biomass Ka band data were found to be sensitive to LAI

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30 SAR data: PALSAR (L-band)+ASAR (Cband)+CosmoSkymed (X-band) Test site: San Rossore Group Sensor Date Time (UTC) Pass Inc. Ang. Pol PALSAR PALSAR PALSAR PALSAR PALSAR PALSAR PALSAR PALSAR PALSAR PALSAR PALSAR PALSAR 28/02/ /06/ /06/ /07/ /09/ /10/ /12/ /01/ /02/ /04/ /04/ /07/ :43:00 Asc 21:03:08 Asc 21:41:48 Asc 21:44:03 Asc 21:42:14 Asc 21:44:25 Asc 21:42:15 Asc 21:44:22 Asc 21:42:06 Asc 21:41:07 Asc 21:23:47 Asc 21:42:52 Asc 38 HH 22 HH/HV/VH/VV 38 HH/HV 38 HH/HV 38 HH/HV 38 HH/HV 38 HH 38 HH 38 HH 38 HH 38 HH 38 HH/HV ASAR ASAR ASAR ASAR ASAR ASAR ASAR ASAR ASAR ASAR ASAR ASAR 26/02/ /05/ /06/ /07/ /09/ /10/ /12/ /01/ /02/ /04/ /04/ /07/ :38:29 Desc 20:59:38 Asc 09:35:57 Desc 09:38:31 Desc 09:38:26 Desc 20:59:34 Asc 20:59:34 Asc 21:02:24 Asc 21:02:24 Desc 09:35:32 Desc 09:38:20 Desc 20:59:36 Asc 23 VV 23 VV 23 VV 23 VV 23 VV 23 VV 23 VV 23 VV 23 VV 23 VV 23 VV 23 VV Test site: Molise Group Sensor Date Time (UTC) Pass Inc. Ang. Pol. PALSA 1 R 01/02/ :27:06 Asc 34 HH ASAR 04/02/ :47:28 Asc 23 VV 2 PALSA R 19/06/ :28:18 Asc HH/H 34 V ASAR 18/06/ :19:02 Desc 23 VV 3 PALSA R 20/12/ :28:51 Asc 34 HH ASAR 16/12/ :47:19 Asc 23 VV Test site: Casentino Group Sensor Date Time (UTC) Pass Inc. Ang. Pol 1 CSK1 29/10/ :28:43 Desc 27VV/VH 2 CSK2 16/03/ :03:10 Desc 37VV/VH 3 CSK2 22/04/ :56:52 Desc 28VV/VH 4 CSK3 20/05/ :02:35 Desc 37HH/HV 5 CSK4 28/05/ :56:31 Desc 22VV/VH 6 CSK4 03/08/ :13:43 Desc 43VV/VH 7 CSK1 16/09/ :01:25 Desc 37VV/VH 8 CSK4 06/10/ :13:09 Desc 43VV/VH 9 CSK4 09/12/ :12:31 Desc 38VV/VH 10 CSK2 28/01/ :24:04 Desc 26VV 11 CSK3 07/04/ :17:18 Desc 32VV 12 CSK3 28/07/ :16:30 Desc 35VV

31 ALOS coherence (L-band) R: HV G: HH B: Coherence A further investigation was carried out by using interferometric techniques. Some features can be identified inside the forest areas. (Coherence generated by 29 jun -29 Sept 2009 (Bort. = 740 m)

32 San Rossore Histograms HH HV Coherence HV -19dB<HV<-11 db -14 db<hh<-6 db -19dB<HV<-11 db 0.3<CC<0.6

33 ASAR (C-band) 2009 San Rossore R: 26 Feb G: 16 Jul B: 24 Sept At C-band the temporal variability is highest on agricultural surfaces and very low on urban areas. On forest a moderate variability can be due to the presence/absence of leaves.

34 Forest biomass retrieval algorithm o o o o A retrieval algorithm based on ANN techniques has been developed The algorithm exploits the sensitivity of low frequencies (L and C bands) to forest biomass. Putting together all data at L-band, we obtain a confuse cluster. By adding the occurrence, a more evident sensitivity of HV pol to biomass was obtained. Scatterplot of San Rossore test site L-band data vs ground biomass data.

35 Sensitivity to slopes High sensitivity to water Visibility through clouds Why Microwaves? Penetration in soil and vegetation Night vision