Landscape-Ecological Map using Three Dimensional Vegetation Structure and Micro Landform Classification detected by LIDAR Survey Data ICC 2009 Nov. 18.

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Landscape-Ecological Map using Three Dimensional Vegetation Structure and Micro Landform Classification detected by LIDAR Survey Data ICC 2009 Nov. 18 th 2009 Escuela Militar, Santiago, Chile Mamoru KOARAI, Takayuki NAKANO, Junko IWAHASHI and Hiroshi P. SATO (Geigraphical Survey Institute, JAPAN)

Content Introduction Studied area (Mt. Rausu, Shiretoko Peninsula) and LIDAR survey Three dimensional vegetation map using LIDAR data DEM analysis of LIDAR survey data Overlay analysis between micro topography and vegetation Conclusion

/The authors have been doing the project of trial produce of landscape-ecological map of Shiretoko Peninsula, where is Natural Heritage Area of Japan, Hokkaido Island. /Basic legend of landscape-ecological maps consists of the combination of vegetation classification and landform classification. /In this presentation, the authors introduce three dimensional vegetation structure map using LIDAR survey data for legend of landscape-ecological map. /This research project have been promoting by the budget of the Ministry of Environment. Introduction

Background (characteristics of LIDAR survey data) /Last pulse LIDAR data in winter season is useful for detection of micro landform under forest area /Vegetation classification has been down using three dimensional vegetation structure detected by the difference between LIDAR data in two seasons summer winter

Mt. Rausu, Shiretoko Peninsula World Natural Heritage Area of Japan Studied area and LIDAR survey

●Studied area South-East foots of Mt. Rausu, Shiretoko Peninsula (1km×4km) ●Airborne laser survey (LIDAR Survey) 0.5m grid DSM and DEM in summer season (Sept.5 th, 2008) 2m grid DSM and DEM in spring or autumn season (June 6 th,2004,Oct.19 th,2004) Haimatsubara profile1 profile2 Satomidai Byobuiwa Studied area LIDAR survey area site4 Tomariba

Byobuiwa Betula ermanii Tomariba Pinus pumila , Betula ermanii , low tree

Satomidai Abies sachalinensis, Quercus crispula Blume , Betula ermanii , Sasa Quercus crispula Blume , Pinus pumila, Sasa Haimatsubara

profile 1 profile 2 profile 3 Three dimensional vegetation structure by LIDER data pinus pumila single layer study area profile 1profile 2profile 3

Summer DSM ( left ), DEM ( right ) ( 0.5m grid )

histogram of vegetation height on each colony vegetation height ( m ) grid number Acer pictum Thunb - Quercus crispula Blume Sasa - betula ermanii Cham betula ermanii Cham Abies sachlinesis - Quercus crispula Blume over 1.5m under 6m -- low tree over 6m under 10m -- middle tree over 10m ------- high tree Three dimensional vegetation map using LIDAR data

Grass, pinus pumila, bare If Hs ≧ 7m, crown: Dw ≧ 10m, thick, Dw<10m, thin. Evergreen trees HsHs HwHw Hs-Hw HsHs HwHw HsHs Ds Dw HwHw Hs<1.5m, Grass, pinus pumila, bare; Hs ≧ 1.5m, Trees Hw ≧ 5m, Multiple layer Hs-Hw =3m, Deciduous trees Deciduous trees (Single layer)Deciduous trees (Multiple layer) Hs ≧ 10 m, High 10m>Hs ≧ 7 m, Medium If Hs ≧ 10m, crown: Ds ≧ 10m, thick; Ds<10m, thin. Hs ≧ 10 m, H. 10m>Hs ≧ 6m, Med. Hw<5m, Single layer 10m>Hs ≧ 1.5 m, Med. & Low Hs<6m, Low Hs ≧ 10 m, High Hs-Hw

LIDAR vegetation map of Mt. Rausu Vegetation classification using three dimensional structure detected by LIDAR data, pinus pumila deciduous or evergreen deciduous (single layer or multiple layers) three categories × vegetation height deciduous (single) --- three categories (low, medium, high) deciduous (multiple) --- two categories (low/medium, high) evergreen --- two categories (medium, high) + crown thickness of high tree (thick, thin) three categories = ten categories + bare/grass/pinus pumila = eleven categories

Relationship between LIDAR vegetation map and Actual Vegetation Map published by Ministry of Environment , pinus pumila area ( ㎡ ) vegetation classification by LIDER Vaccinium - Pinus pumila Betula ermanii Cham Alnus crispa Sasa - Betula ermanii Cham Abies sachalinensis - Quercus crispula Blume Acer pictum Thunb - Quercus crispula Blume

Evergreen Deciduous ( multi ) deciduous ( single ) bare , grass, pinus pumila Evergreen Deciduous ( multi ) deciduous ( single ) bare , grass, pinus pumila Acer pictum Thunb-Tilia japonica community Alnus crispa- Betula ermanii community Sasa spp.-Betula ermanii community overlay of 1/50,000 actual vegetation map and 1m grid LIDAR vegetation map ratio of category of 1m grid LIDAR vegetation map on each colony of 1/50,000 actual vegetation map

ratio of colony of 1/50,000 actual vegetation map on each category of 1m grid LIDAR vegetation map bare , grass , pinus pumila Deciduous ( single ) Deciduous ( multi ) evergreen bare , grass , pinus pumila Deciduous ( single ) Deciduous ( multi ) evergreen Acer pictum Thunb-Tilia japonica community Alnus crispa- Betula ermanii community Sasa spp.-Betula ermanii community overlay of 1m grid LIDAR vegetation map and 1/50,000 actual vegetation map

position of trees corner of survey site LIDER vegetation map bare , grass , pinus pumila deciduous (single) deciduous (multiple) evergreen species Abies sachalinensis Quercus crispula blume Sorbus commixta hedl Acer japonicum Thunb Betula ermanii Cham others Projected plan of the crowns

Cross section on the plot

histogram of gradient , convexity and roughness of each grid size DEM (0.5m, 2m and 50m ) (b) convexity (a) gradient (c) roughness × DEM analysis of LIDAR survey data

0.5m grid2m grid50m grid automatic land form classification using each grid size DEM s ・ cv ・ sm s ・ cv ・ ro s ・ cc ・ sm s ・ cc ・ ro m ・ cv ・ sm m ・ cv ・ ro m ・ cc ・ sm m ・ cc ・ ro g ・ cv ・ sm g ・ cv ・ ro g ・ cc ・ sm g ・ cc ・ ro automatic land form classification using 0.5m grid DEM s ・ cv ・ sm s ・ cv ・ ro s ・ cc ・ sm s ・ cc ・ ro m ・ cv ・ sm m ・ cv ・ ro m ・ cc ・ sm m ・ cc ・ ro g ・ cv ・ sm g ・ cv ・ ro g ・ cc ・ sm g ・ cc ・ ro automatic land form classification using 2m grid DEM s ・ cv ・ sm s ・ cv ・ ro s ・ cc ・ sm s ・ cc ・ ro m ・ cv ・ sm m ・ cv ・ ro m ・ cc ・ sm m ・ cc ・ ro g ・ cv ・ sm g ・ cv ・ ro g ・ cc ・ sm g ・ cc ・ ro automatic land form classification using 50m grid DEM convexity cc : concave cv : convex gradient g : gentle m : middle s : steep roughness ro : rough sm : smooth

overlay of 0.5m grid and 2m grid automatic landform classification ratio of each automation landform classification ( 0.5m grid and 2m grid ) 0.5m ( g ・ cc ・ ro ) 0.5m ( g ・ cc ・ sm ) 0.5m ( g ・ cv ・ ro ) 0.5m ( g ・ cv ・ sm ) 0.5m ( m ・ cc ・ ro ) 0.5m ( m ・ cc ・ sm ) 0.5m ( m ・ cv ・ ro ) 0.5m ( m ・ cv ・ sm ) 0.5m ( s ・ cc ・ ro ) 0.5m ( s ・ cc ・ sm ) 0.5m ( s ・ cv ・ ro ) 0.5m ( s ・ cv ・ sm ) 2m ( s ・ cv ・ sm ) 2m ( s ・ cv ・ ro ) 2m ( s ・ cc ・ sm ) 2m ( s ・ cc ・ ro ) 2m ( m ・ cv ・ sm ) 2m ( m ・ cv ・ ro ) 2m ( m ・ cc ・ sm ) 2m ( m ・ cc ・ ro ) 2m ( g ・ cv ・ sm ) 2m ( g ・ cv ・ ro ) 2m ( g ・ cc ・ sm ) 2m ( g ・ cc ・ ro ) 0.5m ( g ・ cc ・ ro ) 0.5m ( g ・ cc ・ sm ) 0.5m ( g ・ cv ・ ro ) 0.5m ( g ・ cv ・ sm ) 0.5m ( m ・ cc ・ ro ) 0.5m ( m ・ cc ・ sm ) 0.5m ( m ・ cv ・ ro ) 0.5m ( m ・ cv ・ sm ) 0.5m ( s ・ cc ・ ro ) 0.5m ( s ・ cc ・ sm ) 0.5m ( s ・ cv ・ ro ) 0.5m ( s ・ cv ・ sm ) 2m ( s ・ cv ・ sm ) 2m ( s ・ cv ・ ro ) 2m ( s ・ cc ・ sm ) 2m ( s ・ cc ・ ro ) 2m ( m ・ cv ・ sm ) 2m ( m ・ cv ・ ro ) 2m ( m ・ cc ・ sm ) 2m ( m ・ cc ・ ro ) 2m ( g ・ cv ・ sm ) 2m ( g ・ cv ・ ro ) 2m ( g ・ cc ・ sm ) 2m ( g ・ cc ・ ro ) g : gentle m : middle s : steep cc : concave cv : convex ro : rough sm : smooth

2m ( s ・ cv ・ sm ) 2m ( s ・ cv ・ ro ) 2m ( s ・ cc ・ sm ) 2m ( s ・ cc ・ ro ) 2m ( m ・ cv ・ sm ) 2m ( m ・ cv ・ ro ) 2m ( m ・ cc ・ sm ) 2m ( m ・ cc ・ ro ) 2m ( g ・ cv ・ sm ) 2m ( g ・ cv ・ ro ) 2m ( g ・ cc ・ sm ) 2m ( g ・ cc ・ ro ) 50m ( g ・ cc ・ ro ) 50m ( g ・ cc ・ sm ) 50m ( g ・ cv ・ ro ) 50m ( g ・ cv ・ sm ) 50m ( m ・ cc ・ ro ) 50m ( m ・ cc ・ sm ) 50m ( m ・ cv ・ ro ) 50m ( m ・ cv ・ sm ) 50m ( s ・ cc ・ ro ) 50m ( s ・ cc ・ sm ) 50m ( s ・ cv ・ ro ) 50m ( s ・ cv ・ sm ) 2m ( s ・ cv ・ sm ) 2m ( s ・ cv ・ ro ) 2m ( s ・ cc ・ sm ) 2m ( s ・ cc ・ ro ) 2m ( m ・ cv ・ sm ) 2m ( m ・ cv ・ ro ) 2m ( m ・ cc ・ sm ) 2m ( m ・ cc ・ ro ) 2m ( g ・ cv ・ sm ) 2m ( g ・ cv ・ ro ) 2m ( g ・ cc ・ sm ) 2m ( g ・ cc ・ ro ) 50m ( g ・ cc ・ ro ) 50m ( g ・ cc ・ sm ) 50m ( g ・ cv ・ ro ) 50m ( g ・ cv ・ sm ) 50m ( m ・ cc ・ ro ) 50m ( m ・ cc ・ sm ) 50m ( m ・ cv ・ ro ) 50m ( m ・ cv ・ sm ) 50m ( s ・ cc ・ ro ) 50m ( s ・ cc ・ sm ) 50m ( s ・ cv ・ ro ) 50m ( s ・ cv ・ sm ) overlay of 2m grid and 50m grid automatic landform classification ratio of each automation landform classification ( 50m grid and 2m grid ) g : gentle m : middle s : steep cc : concave cv : convex ro : rough sm : smooth

50m ( g ・ cc ・ ro ) 50m ( g ・ cc ・ sm ) 50m ( g ・ cv ・ ro ) 50m ( g ・ cv ・ sm ) 50m ( m ・ cc ・ ro ) 50m ( m ・ cc ・ sm ) 50m ( m ・ cv ・ ro ) 50m ( m ・ cv ・ sm ) 50m ( s ・ cc ・ ro ) 50m ( s ・ cc ・ sm ) 50m ( s ・ cv ・ ro ) 50m ( s ・ cv ・ sm ) 50m ( g ・ cc ・ ro ) 50m ( g ・ cc ・ sm ) 50m ( g ・ cv ・ ro ) 50m ( g ・ cv ・ sm ) 50m ( m ・ cc ・ ro ) 50m ( m ・ cc ・ sm ) 50m ( m ・ cv ・ ro ) 50m ( m ・ cv ・ sm ) 50m ( s ・ cc ・ ro ) 50m ( s ・ cc ・ sm ) 50m ( s ・ cv ・ ro ) 50m ( s ・ cv ・ sm ) g : gentle m : middle s : steep cc : concave cv : convex ro : rough sm : smooth overlay of 50m grid automatic landform classification and 1/50,000 actual vegetation map ratio of 50m grid automatic landform classification on each colony of 1/50,000 actual vegetation map Overlay analysis between micro topography and vegetation

2m ( g ・ cc ・ ro ) 2m ( g ・ cc ・ sm ) 2m ( g ・ cv ・ ro ) 2m ( g ・ cv ・ sm ) 2m ( m ・ cc ・ ro ) 2m ( m ・ cc ・ sm ) 2m ( m ・ cv ・ ro ) 2m ( m ・ cv ・ sm ) 2m ( s ・ cc ・ ro ) 2m ( s ・ cc ・ sm ) 2m ( s ・ cv ・ ro ) 2m ( s ・ cv ・ sm ) bare , grass , Pinus pumila deciduous ( single ) deciduous ( multi ) evergreen 2m ( g ・ cc ・ ro ) 2m ( g ・ cc ・ sm ) 2m ( g ・ cv ・ ro ) 2m ( g ・ cv ・ sm ) 2m ( m ・ cc ・ ro ) 2m ( m ・ cc ・ sm ) 2m ( m ・ cv ・ ro ) 2m ( m ・ cv ・ sm ) 2m ( s ・ cc ・ ro ) 2m ( s ・ cc ・ sm ) 2m ( s ・ cv ・ ro ) 2m ( s ・ cv ・ sm ) bare , grass , Pinus pumila deciduous ( single ) deciduous ( multi ) evergreen g : gentle m : middle s : steep cc : concave cv : convex ro : rough sm : smooth overlay of 2m grid automatic landform classification and 1m grid LIDAR vegetation map ratio of 2m grid automatic landform classification on each category of 1m grid LIDAR vegetation map

The authors produce three dimensional vegetation map using LIDAR data on south-east foots of Mt. Rausu on Shiretoko Peninsula. LIDAR vegetation map is correspond to Actual Vegetation Map and ground survey data. Result of overlay between automatic landform classification and vegetation classification shows vegetation on Mt. Rausu depend on elevation. The authors will produce legend of landscape-ecological map combined with three dimensional vegetation structure and micro landform classification for the evaluation of biodiversity in Natural Heritage Area. Conclusion