Biophysical Parameter Retrieval and Validation at the Speulderbos site

Transcription

1 Biophysical Parameter Retrieval and Validation at the Speulderbos site Benjamin Brede*, Jan Clevers, Jan Verbesselt, Martin Herold Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Wageningen, The Netherlands Dec 12, 2017

2 Overview 1. Speulderbos Cal/Val site, The Netherlands 2. Monitoring Canopy Phenology with PASTIS Discrete Forest Structure from UAV-lidar 2

3 Speulderbos Concept & Activities Sentinel-2A SPOT 6/7 Landsat 7/8 UK-DMC-2/ Deimos-1 HYMSY ALS: RIEGL RiCopter Leaf sampling: ASD Fieldspec Minolta SPAD Littertraps (true LAI) TLS: RIEGL VZ-400 PASTIS 57 USGS ESA Airbus DS RIEGL LMS Deimos Imaging 3

4 Speulderbos Concept & Activities 4

5 Monitoring Canopy Phenology with PASTIS 57 Motivation: LAI monitoring time-intense Goal: daily PAI monitoring Approach: field experiment Implementation in DART for sensitivity analysis Tom Schenkels Brede, B., Gastellu-Etchegorry, J.-P., Lauret, N., Baret, F., Clevers, J., Verbesselt, J., Herold, M.: Monitoring Forest Phenology and Leaf Area Index with an Autonomous, Lowcost Transmittance Sensor. Remote Sensing (in preparation). 5

6 Monitoring Canopy Phenology with PASTIS 57 Problem 1: in case DN below = 0, L = (theoretically not possible) Solution 1: Reject DN below = 0 Problem 2: Canopy gaps violate turbid medium assumption Solution 1: Reject DN > 95 th percentile Brede, B., Gastellu-Etchegorry, J.-P., Lauret, N., Baret, F., Clevers, J., Verbesselt, J., Herold, M.: Monitoring Forest Phenology and Leaf Area Index with an Autonomous, Lowcost Transmittance Sensor. Remote Sensing (in preparation). 6

7 Monitoring Canopy Phenology with PASTIS 57 Strongest noise during summer/full canopy Brede, B., Gastellu-Etchegorry, J.-P., Lauret, N., Baret, F., Clevers, J., Verbesselt, J., Herold, M.: Monitoring Forest Phenology and Leaf Area Index with an Autonomous, Lowcost Transmittance Sensor. Remote Sensing (in preparation). 7

8 Monitoring Canopy Phenology with PASTIS 57 Impact of quantization at high PAI Brede, B., Gastellu-Etchegorry, J.-P., Lauret, N., Baret, F., Clevers, J., Verbesselt, J., Herold, M.: Monitoring Forest Phenology and Leaf Area Index with an Autonomous, Lowcost Transmittance Sensor. Remote Sensing (in preparation). 8

9 Monitoring Canopy Phenology with PASTIS 57 System and retrieval robust to change in illumination angle (solar zenith angle) Blue band: leaves close to τ ρ 0 Leave angle distribution under investigation Brede, B., Gastellu-Etchegorry, J.-P., Lauret, N., Baret, F., Clevers, J., Verbesselt, J., Herold, M.: Monitoring Forest Phenology and Leaf Area Index with an Autonomous, Lowcost Transmittance Sensor. Remote Sensing (in preparation). 9

10 Monitoring Canopy Phenology with PASTIS 57 Conclusions: PASTIS robust instrument for phenology monitoring easy installation & operation Mostly robust retrieval principle Low-cost transmission sensors promising for deployment in measurement networks 10

11 Discrete Forest Structure from UAV-lidar Motivation: TLS campaigns time-intense (2-7 days/ha) Goal: investigate suitability of UAV-lidar for single tree assessment (compared to TLS) Approach: Combined field campaign TLS & ALS Brede, B., Lau, A., Bartholomeus, H., Kooistra, L.: Comparing RIEGL RiCOPTER UAV LiDAR Derived Canopy Height and DBH with Terrestrial LiDAR. Sensors 17 (10),

12 Discrete Forest Structure from UAV-lidar ALS (UAV-lidar) RIEGL RiCOPTER 2h preparation & flight 9 flight lines TLS (T-lidar) RIEGL VZ days preparation & scanning 58 TLS positions Brede, B., Lau, A., Bartholomeus, H., Kooistra, L.: Comparing RIEGL RiCOPTER UAV LiDAR Derived Canopy Height and DBH with Terrestrial LiDAR. Sensors 17 (10),

13 Discrete Forest Structure from UAV-lidar UAV-lidar TLS Brede, B., Lau, A., Bartholomeus, H., Kooistra, L.: Comparing RIEGL RiCOPTER UAV LiDAR Derived Canopy Height and DBH with Terrestrial LiDAR. Sensors 17 (10),

14 Discrete Forest Structure from UAV-lidar Extract 58 trees 39 suitable for modelling Fit circles at 1.3m above ground for Diameter at Breast Height (DBH) estimation r 2 = 0.98 RMSE = 4.24 cm Brede, B., Lau, A., Bartholomeus, H., Kooistra, L.: Comparing RIEGL RiCOPTER UAV LiDAR Derived Canopy Height and DBH with Terrestrial LiDAR. Sensors 17 (10),

15 Discrete Forest Structure from UAV-lidar Important scanner properties for stem detection: Side looking Multi-return Brede, B., Lau, A., Bartholomeus, H., Kooistra, L.: Comparing RIEGL RiCOPTER UAV LiDAR Derived Canopy Height and DBH with Terrestrial LiDAR. Sensors 17 (10),

16 Discrete Forest Structure from UAV-lidar Conclusions RiCopter integrated system guarantees fast, high quality point cloud production RiCopter UAV-lidar good agreement with TLS Next: test potential for whole tree modelling 16

17 Discrete Forest Structure from UAV-lidar Outlook: RiCopter vs VZ-400 (QSM 1 ) tree volume TLS optimisation based on point distance to QSM ALS optimisation based on closed ensemble to TLS 1 Raumonen, P., Kaasalainen, M., Åkerblom, M., Kaasalainen, S., Kaartinen, H., Vastaranta, M., Holopainen, M., Disney, M., Lewis, P., Fast Automatic Precision 17 Tree Models from Terrestrial Laser Scanner Data. Remote Sens. 5, doi: /rs

18 Next steps Continuation campaign 2018: TLS, PASTIS, HYMSY UAV lidar campaigns with full leaf canopy Trunk recognition feasible? How many flights needed? What are best scanner settings? Sun-induced Chlorophyll fluorescence from UAV Test prototype sensor 18

19 Thank you for your attention! Project Site WU UAV Facility WU Lidar Facility 19