High Resolution Satellite Data for Forest Management. - Algorithm for Tree Counting - Launch in IKONOS 1-meter Resolution

Transcription

1 High Resolution Satellite Data for Forest Management - Algorithm for Tree Counting - Kiyoshi HONDA ACRoRS, Asian Institute of Technology NASDA ALOS (NASDA JAPAN) 2.5m Resolution Launch in 2002 Panchromatic Remote sensing Instrument for Stereo Mapping (PRISM) It has three independent catoptric systems for nadir, forward and backward looking to achieve along-track stereoscope. Each telescope consists of three mirrors and several CCD detectors for push-broom scanning. Nadir-looking telescope provides 70 km width coverage and forward and backward 35 km. Forward and backward telescopes are inclined about +-24 degrees from nadir to realize base to height ratio = 1. Its 2.5-meter resolution data will be used for extracting highly accurate digital elevation model (DEM). Hishron Museum and Sculpture Garden Washington DC, USA 1999 IKONOS 1-meter Resolution Fulton County Stadium. Downtown Atlanta Georgia, USA SPIN-2 2 meter Resolution Source: Space Imaging Corp Source: SPIN-2 Corp Future Name Present IKONOS Present & Future High Resolution Satellites Country Launch Space Imaging / USA 1999 SPIN-2 Russia - Sensors Optical Telescope Assembly KVR Photograph Types channels (meters) Multispectral 4 4 Panchromatic 1 1 Panchromatic 1 2 ALOS NASDA / Japan 2002 PRISM Panchromatic Pushbroom Multispectral 4 4 Quick Bird Earth Watch / USA 2000 Linear Arrary Panchromatic 1 1 Multispectral 4 4 OrbView-3 Orbimage / USA 2000 Panchromatic 1 1 Multispectral 4 4 OrbView-4 Orbimage / USA 2001 Panchromatic 1 1 EROS-A West Indian Space, 2000 Panchromatic Ltd./ USA EROS-B1 West Indian Space, 2001 Panchromatic Ltd./ USA IRS-P5 ISRO / India 2000 LISS IV Panchromatic IRS-P6 ISRO / India 2001 LISS IV Panchromatic 1 6 SPOT-5 SPOT / France 2001 HRV Panchromatic 1 5 tree counting algorithm development Introduction Emerging Super High Resolution Satellites (1m ~ 6m) ALOS, IKNOS, SPIN2. Possibility for identifying each tree crown from space Getting more detail and accurate information of forest Tree Crown Identification, Number of Trees, Crown Size, Volume, Identify big trees. Usual Classification algorithms ( pixel wise approach ) are not applicable for tree crown identification -> Develop a new algorithm for identifying tree crown Source:

2 Flow of the development of the algorithm Japanese Cedar Tree Model I. Model image of high resolution satellite data using Japanese Cedar model and 3D computer graphics. ( Ideal Image ) II. Algorithm Development based on the model image. III. Simulation Image of high resolution satellite data from an aerial photograph by degrading the resolution IV. Accuracy Check α Cross section of a Tree Crown 0.5 y = αx = f ( Height, Density ) Height of tree 30(m) density of standing trees 494(n/ha) (K. TAKESHITA, 1985) tree crown curve y = 0.79x 0.5 α = 0.79 The 3D image of the Japanese Cedar model CG Image of the Japanese Cedar Forest Model Diameter: 4.6m Front view Perspective view Perspective view Top view Model image of the high resolution satellite image (CG) 3D image of the model image ( Height is a value of pixel) 1m Resolution

3 Identification of Tree Crown by Threshholding ( The threshold is average of pixel value) New Algorithm for Extracting Tree Crown Pixel value is more than average Pixel value is less than average High Pixel value Tree crown Tree crown Threshold Tree Crown Not Tree Crown Low P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 Pixel P:Target pixel P1:Neighbor 1 P2:Neighbor 2 Ravine Bottom Recognition using the difference in pixel value of neighbor Identification of Tree Crown by Ravine Bottom Recognition p(1) p(2) p(3) p(4) p(0) p(5) p(6) p(7) p(8) 8-neighbor of p(0) Difference between P(0) and P(n) q(1) = p(0) - p(1) q(2) = p(0) - p(2) q(3) = p(0) - p(3) q(4) = p(0) - p(4) q(5) = p(0) - p(5) q(6) = p(0) - p(6) q(7) = p(0) - p(7) q(8) = p(0) - p(8) The condition of bottom of a ravine in the image 1) {q(1)<0 & q(8)<=0} or {q(1)<=0 & q(8)<0} 2) {q(2)<0 & q(7)<=0} or {q(2)<=0 & q(7)<0} 3) {q(3)<0 & q(6)<=0} or {q(3)<=0 & q(6)<0} 4) {q(4)<0 & q(5)<=0} or {q(4)<=0 & q(5)<0} true Other Image value = 0 Image value = 1 Not the bottom of a ravine the bottom of a ravine Tree Crown Identification by New Algorithm ( Thresholding & Ravine Bottom Recognition ) Counting Tree Crown by Labeling Tree crow Not tree crown

4 Satellite Simulation Image from an Aerial Photograph of the high resolution satellite data (1) Target place Experimental forest of Mie university 975 trees per hectare Aerial photograph (21 cm/pixel) (80 cm/pixel) of the high resolution satellite data (2) 80 cm / pixel (1 m/pixel) (3 m/pixel) 1m / pixel 3m / pixel

5 Tree Crown Identification Result Visual identification result of each tree crown N = m/pixel 1m/pixel 3m/pixel Number of standing trees The accuracy in terms of number of tree crown in the image number of standing trees (visual) number of standing trees (suppositional) % % % Ground resolution (m/pixel) The accuracy in terms of identification of each tree crown 0.8m/pixel 1m/pixel 1)Identified 2)Not Identified 3)Combined 4)Divided Rate of number of tree crown(%) 100% 80% 60% 40% 20% 0% The accuracy in terms of identification of each tree crown at different diameter class all Diameter of tree crown(m) 0.8m/piexl Identified Combined Rate of number of tree crown(%) Not identified Divided % % % 20 40% % % all Diameter of tree crown(m) 1m/pixel Application to Teak Bearing Forest in Myanmar 1 m / Pixel 2 m / Pixel

6 Visual Identification of Crown The result image Crown Area were identified visually for accuracy check-up. These Crowns were digitised on the screen manually Colour assigned to appropriate size of crown Graph of accuracy in 1 meter Graph of accuracy in 2 meter Overall accuracy of 1 meter image is 72%. The composition of identified crown is 70 out of 78 (89%) Accuracy % m resolution Image Accuracy Crown Size Classes m. Overall accuracy of 2 meter image is 68%. The composition of identified crown is 66 out of 78 (84%) Accuracy % m resolution Image Accuracy Crown Size Classes m. Japanese Cedar Forest Conclusion The accuracy in terms of number of tree crown in the image Accuracy of identification of each tree crown at different diameter class Teak Bearing Forest 1m/pixel 72% 2m/pixel 68% 0.8m/pixel 87.6% 1m/pixel 69.7% 3m/pixel 16.8% 80cm/pixel 2m~4m 87.8% 4m~6m 92.5% 1m/pixel 2m~4m 72.1% 4m~6m 83.0% New algorithm makes it possible to apply High Resolution Satellite Image to Identify Each Tree Crown for the management of forest resources. Future work Conclusion - cont d Algorithm Development For local important species or forest. More detailed forest and tree structure ( Diameter, Height, Biomass. ) Combination with other instruments Developing Database of Forest Structure: Global Environment