Presented on International Conference on Sustainability Study (ICSS), Bali, January 11, 2012

Transcription

1 Quantitative Measurement from Unifying Field and Airborne Hyperspectral (HyMap) for Diagnosing Peat Forest Degradation in Central Kalimantan, Indonesia by Ohki Takahashi (MRI), Tomomi Takeda (ERSDAC), Muhammad Evri (BPPT) Osamu Kashimura (ERSDAC), Mitsuru Osaki (HU), Kazuyo Hirose (HU), Hendrik Segah (HU) Presented on International Conference on Sustainability Study (ICSS), Bali, January 11, 2012

2 Joint Research Research Project of Hyperspectral Technology for Tropical Peat-Forest Mapping in Indonesia(Hyper PF MRV) Budget METI BPPT ERSDAC Entrustment MRI Collaboration Hokkaido Univ.

3 Rationale Statement of President Susilo Bambang Yudoyono in the summit of G-20 in Pittsburgh (USA) on September 2009; to reduce greenhouse gases (GHG) 26% through National Appropriate Mitigation and Adaptation (NAMA) up to 2020 and become 41% with international support. Moratorium Signed a decree (Inpres no. 10, 2011) on May 19, 2011; suspending new concession permits and to improve good governance on primary forest and peatland in Indonesia. Suspension of all new concessions will be enforced for 2 years, and will be effective immediately.

4 Indonesia is the third largest GHG emitter in the world. It is estimated as about 2.1 GtCO2e in % from Deforestation and Degradation Agriculture: 6% LULUCF: 37% Peat : 41% (DNPI, 2010)

5 Intergovenmental Panel on Climate Change To measure changes in carbon stocks caused by forest degradation, IPCC (2006) recommends two approach method: Stock-Difference Gain-Loss C = C t2 C t1 (t2 t1) Where : C = Annual carbon stock change in pool (tc/yr) C t1 = Carbon stock in pool at time t 1 (tc) C t2 = Carbon stock in pool at time t 2 (tc) To estimate sequestration or emissions. To measure the actual stock of biomass in each carbon pool at two moments in time. Suitable for estimating emissions caused by both deforestation and degradation. Can be applied to all carbon pools. C = C gain C loss Where : C = Annual carbon stock change in pool (tc/yr) C gain = Annual gain in Carbon (tc) C loss = Annual loss in in Carbon (tc) To estimate the net balance of additions to and removals from a carbon pool. Used when annual data on information such as growth rates and wood harvest are available.

6 Hyperspectral for Forest Degradation 6

7 Huge dimension of hyperspectral data Definition Acquisition of images in hundreds of calibrated, contiguous spectral bands, such that for each picture element it is possible to derive a complete reflectance spectrum Hyperspectral : excessiveness of the number of band being employed in its sensor Hyper-cube data Advantages Unique discriminative power Free band selection Conducive for interdisciplinary collaboration Number of bands

8 > 600 km Landsat Hyperion GOSAT MODIS ASTER ALOS PALSAR Terrain analysis (Airborne) 2,000 m Airbornehyperspectral LiDAR UAV Supersite Supersite Supersite Supersite Biometric work Soil Repiration Ecophisiology Flux tower Obsv Tower Survey DGPS

9 Airborne and Sensors

10 Sensor specification Bands : provides 126 bands across the reflective solar wavelength region of nm with contiguous spectral coverage (except in the atmospheric water vapour bands) and bandwidths between nm. Platform : Light, twin engine aircraft,unpressurized Altitudes : m ALG Ground speed : knots IFOV : 2.5 mr along track 2.0 mr aross track FOV : 620 degrees (512 pixel) Swath : 2.3 km at 5 m IFOV (along track) 4.6 km at 10 m IFOV (along track) Typical operational parameters Spatial configuration 10

11 Central Kalimantan, Indonesia Two test sites Hyperspectral sensor observation by aircraft Test Site1 City of Palangkaraya Test Site2 11

12 Peatland is rich soil carbon storage. It will become a large CO2 emission source Factor: Hydrological environment change Decrease of groundwater level and drying peat Forest degradation in peatland forest Dissolved organic carbon from artificial canals, Poor growth vegetation, Forest disturbance, etc Applying Hyperspectral sensor Developing technology to assess forest degradation in peatland forest Contributing MRV development of REDD+ activity in peatland forest Healthy Forest Poor growth Disturbance by fire Dying Degraded Forest Biomass degradation Reduction of CO2 sink capacity Analysis of forest degradation condition Development of forest degradation monitoring method by satellite image C C C Groundwater level C drying Peatland C Decrease C Canal Emission of DOC Spectral analysis of dissolved organic carbon (DOC) in canal Development of DOC assessment method in canal by satellite image 12

13 Analysis of forest degradation condition and spectral characteristics Identify the forest degradation condition Find the appropriate index Relation with soil moisture / underground water level Spectral analysis of DOC in canal Measurement of water quality and spectral near water surface Analyze spectral characteristic of CDOM CDOM:Colored Dissolved Organic Matter Field data analysis Development of forest degradation monitoring methods, focusing following indices Water stress Species Stand structures Biomass Development of DOC assessment method in canal. Qualitatively and quantitatively assessing CDOM in canal Estimate DOC from CDOM analysis Image analysis Consideration of MRV system using Hyperspectral Sensor Role of hyper spectral sensor (from monitoring target, area, frequency ) 13

14 Airborne campaign July 2011 Hyperspectral sensor : HyMAP (400 to 2500nm) Field campaign July 2011 Ground reference measurement using FieldSpec Water quality measurement CDOM (Carbon Dissolve Organic Matter) Spectral near water surface using FieldSpec Forest Survey Tree and soil within the 20m 20m quadrat, point Parameters Species DBH Tree Height Canopy Cover Soil Moisture Ground water level 14

15 Quadrat structure Quadrat size are 20m : base area 10m sub-quadrat: A,B,C,D 5m sub-quadrat: 1,2,3,4 NW B C NE Setting the quadrat Set 4 side of quadrat and 10m sub-quadrat, along the azimuth direction Set GPS point of 4 tips of quadrat Quadrat condition HU1: Condition of the disturbance with forest fire and artificial logging 1 SW 5m A 4 1 D 10m 4 SE HU2: Condition of the drainage and decreasing under ground water level 20m

16 DBH & Tree species Target tree For 20m quadrat : all trees of 10cm =< DBH For A1,B1,C1,D1 : all trees of 5cm =< DBH < 10cm DBH are measured with caliper Tree species are identified by LIPI expert Genus, Family, and Local name Tree Height Target tree 20 trees are selected in the quadrat Select 5 trees in each 10m sub-quadrat Select the trees in a random manner to cover the variety of DBH and species. Measured with VERTEX (Haglof Company Group) Tree height model are made with DBH-Height relationship of 20 samples => All tree height in the quadrat are estimated with the model from DBH.

17 Soil moisture Measured in 1 point per each quadrat Data each point (depth) : 5cm, 15cm and 30cm. Measured with HydroSense (Campbell Scientific Australia Pty. Ltd.) Under ground water level Measured in 1 point per each quadrat Equipment : PVC pipe Measurement : (1) the length from pipe edge to under ground water level with plastic hose, (2) the length from pipe edge to ground level. Measurement day are passed more than 2 days from setting day to balance the water level of inside pipe and outside. Crown Cover The picture of canopy taken with fish-eye camera, at the center of each 10m sub-quadrat Total pictures : 4 points in the quadrat Calculate the crown cover rate from the picture image with LIA for Win32 software Under story Vegetation Measurement : coverage and height of under story vegetation

18 Water sampling Tree sampling Blue sheet For correction Soil sampling 18

19 FieldSpec Water body Reference target

20 Extremely High degree of natural Shorea, and Calophyllum are dominant High diversity of tree species A lot of trees with large-diameter Impact of Human activity Shorea is dominant High diversity of tree species Few large-diameter tree Analyzed Area High degree of natural Shorea, and Acronychia are dominant High diversity of tree species There are trees with large or moderate diameter Plenty substances with DBH 10cm or less Impact of Humanty activity Shorea is dominant High diversity of tree species Few trees with large-diameter Impact of forest fire Tumih is dominant Small number of trees Impact of forest fire Tumih is dominant but other tree species exist. There are a lot of substances with DBH 10cm or less

21 Classification of forest based on field survey : Tree species structure, Stand structure, Growth status, etc. High degree of natural Cratoxylum, Tristaniopsis are dominant High diversity of tree species Large number of trees Small number of trees with largediameter Analyzed Area Impact of forest fire Tumih is dominant Low diversity of tree species Small number of trees Lower canopy cover Impact of drain Shorea is dominant High diversity of tree species Large number of trees A lot of dead trees Small number of trees with large-diameter Impact of forest fire Tumih is dominant Low diversity of tree species Large number of trees A lot of thin tree 221 has tree species diversity

22 The number of Samples Use the same size of four areas abutting on quadrat, which are assumed to have the same tree species. Use HyMAP images (Atmospheric and geometric correction) The number of Bands Use 86 bands of 126 band of 450nm-2490nm except : O 2,H 2 O and CO 2 absorption band, Area of low S/N: nm Classification Model Classification model based on Sparse analysis Optimize parameters by conducting 5-fold cross-validation :Tumih :Secondary Forest Test Site1 Field survey quadrat 20m 22 20m Test Site2 (Setia Alam) Test Site2

23 Band used for the classification model Model s Expressiveness test True Value Secondary Tumih Prediction Secondary 21 0 Tumih 0 21 The number of samples: 42 Correct answer rate: 100% Model s prediction capability test Prediction The number of samples: 28 Correct answer rate: 96.4% True Value Secondary Tumih Secondary 14 1 Tumih 0 13 Band Wave Length [nm] Coefficient B B B B Band Wave Length [nm] Coefficient B B B B

24 Band used for the classification model The classification : high accuracy Model s Expressiveness True Value test Secondary Tumih Secondary 24 0 Prediction Tumih 0 9 The number of samples: 33 Correct answer rate: 100% Model s prediction True Value capability test Secondary Tumih Secondary 24 0 Prediction Tumih 0 9 The number of samples: 22 Correct answer rate: 100% Band Wave Length [nm] Coefficient B B B B

25 Data used The number of samples Total: 26 points (Test Site1: 14 points, Test Site 2: 12 points) HyMAP data (atmospheric and geometric correction) The number of bands Use 86 bands selected from 126 bands of nm by eliminating the large S/N bands below.» O 2 absorption band: nm» H 2 O absorption band: , , , nm» CO 2 absorption band: , nm» Area of low S/N: nm Analysis Modeling Test Site-1 and Test Site-2 respectively. As for Test Site-2, data of Setia Alam area eliminated because of different acquired date. Create estimation model from LASSO regression Optimize parameters from 3-fold cross-validation

26 Estimated value Estimation of canopy cover with several percent order. Test Site1 Test Site2 Eliminate SetiaAlam Test Item Result Test Item Result Model s Expressiveness test (Closed test) RMSE 2.78 [%] Model s Expressiveness test (Closed test) RMSE 5.57 [%] Model s prediction capability test (Open test) (CV average 1000 times) RMSE 6.51[%] Model s prediction capability test (Open test) (CV average 1000 times) RMSE 9.98[%] Measured value 26

27 Bands used for estimation models Band Coefficient B B B B B Test Site1 Test Site2 Band Coefficient B B

28 Data used The number of samples Total: 21 points (Test Site1: 13 points, Test Site2: 8 points)» Refer the data observed by Prof. Inoue at Hokkaido University for Test Site2 s 206 and 214 observation points» Except the above, obtain the groundwater level data from field survey. Conduct atmospheric correction, geometric correction, and equalization of reflectance average between observation lines of HyMAP images Analysis Mapping the below water stress index and the groundwater level results of field survey. Water Band Index(WBI)= R900/R970 Normalized Difference Water Index(NDWI)=(R857- R1241)/(R857+R1241) Modeling Test Site-1 and Test Site-2 respectively.

29 Both WBI and NDWI may express groundwater level by using logarithmic function. Peat forest in the low groundwater level area has thick leaves with large moisture content due to tackle drying stress. The efficiency of water usage is increased by closing (not completely) pore. The water index is high (which means large moisture content) when the ground water level is low. This corresponds with the above mentioned trend.

30 Test Site2 can t express relationships between water index and groundwater level. One factor of this is the limitation of correction of reflectance between observation lines. Test Site2 isn t suitable for water index analysis because there are great variability of reflectance and extremely low values within the same observation lines.

31 Constructing forest degradation monitoring system using Hyperspectral sensor. Forest degradation monitoring are difficult using existing satellite data and method. REDD+ targets monitoring; forest enhancement, sustainable forest management etc. Potential to be applied for the change in the forest cover. Degradation; change worse Enhancement; change better Construct the new approach to assess the carbon emission from peat-land soil to the river. It is limited approach to evaluate the amount of such a carbon using satellite. 31

32 Hyperspectral remote sensing technology significantly improves the resolving power of remote sensing technology from discrimination to identification oriented problem solving. Development of spectral library based on tree species is important as a baseline for further classification process in hyperspectral application for forest monitoring Potential prediction for species classification, crown cover and water index 32

33 Adopting some techniques such as SAM (Single Angle Mapper), SVM (Support Vector Machine), Neural Network based classification, GA-PLSR (Genetic Algorithm-Partial Least Square Regression) to yield more detail classification of species, crown cover and water index Carbon Accounting on peat-forest area 33

34 Hyper-spectrum Sensor Characteristics (draft) Spatial Parameter Resolution Swath Width Bands Requirement 30 m 30 km ~ 185 bands Hyper-spectral sensor Multi-spectral sensor Spectral Range Resolution Signal to Noise Ratio (S/N) 0.4 ~ 2.5μm 10 nm (VNIR) 12.5 nm (SWIR) MTF 0.2 Dynamic Range Multi-spectrum Parameter 10 bits Requirement 30 km 30 km 30 km Spatial Spectral Resolution 5 m Swath Width 90 km Bands 4 Range 0.42~0.90μm Signal to Noise Ratio (S/N) 200 MTF 0.3 Dynamic Range 8 bits 34

35 Thank You