Development of Forest Canopy Density Mapping and Monitoring Model using Indices of Vegetation, Bare soil and Shadow
A. Rikimaru, s. Miyatake*
College of Engineering, Hosei University
3-7-2, Kajino- Cho Koganei-shi, Tokyo 184, Japan
Tel: (81)-423-87-6115 Fax (81) -492-92-7414
E-mail: rikimaru@a1.mbn.or.jp
*Japan Overseas Forestry consulatants Association
rinyuu-Blgd:1-7-12 Koraku, Bunkyo-Ku, Tokyo 112, Japna
Tel: (81)-3-5689-3435 Fax (81)-3-5689-3439
E-mail: Jofca@alpha-web.or.jp
Abstract
Forest canopy density is one of the most useful parameters to consider in the planning and inplementation of rehabilitation program. This study is development of bio-phsycal analysis model for obtaining of forest Canopy Density (FCD) using LANDSAT TM data image analysis. The components FCD model are four factors; vegetation, bare soil, thermal and shadow. This work is implemented under the research project; PD32/93 Rev2(F) of International Tropical Timber Organization (ITTO).
1.Introduction
The Forest Canopy Density (FCD) Mapping and monitoring Model utilized forest canopy density as an essential parameter for characterization of forest conditions. FCD data indicates the degree of degradation, thereby also indication the intensity of rehabilitation treatment that may be required.
The source remote sensing data for FCD model is LANDSAT TM data. The FCD model comprises bio-physical phenomenon modeling and analyis utilizing data derived from four(4) indices: Advanced Vegetation Index (AVI), Bare Soil Index (BI), shadow Index or scaled Shadow Index (SI, SSI) and Thermal Index (TI). It determines FCD by modeling operation and obtaining from these indices.
The canopy density is calculated in parentage for each pixel. The FCD model requires less information of ground truth. Just for accuracy check and so on.
FCD model is based on the growth phenomenon of forests. Consequently, it also becomes possible to monitor transformation of forest conditions over time such as the progress of forestry activities.
The application test were implemented in these area. The evergreen forests are in the islands of Luzon (Philippines) and Sumatra (Indonesia); and for monsoon (subtropical deciduous) forest in Ching -Mai (Thailand) and Terai (Nepal).
2.Characteristics of Forest (4) Indices
The indices have some characteristics as below. The Forest Canopy Density Model combines data from the four (4) indices. Fig. 1 illustrates the relationship between forest conditions and the four indices (VI, BI, SI and TI). Vegetation index response to all of vegetation items such as the forest and the grass land. Advanced vegetation index AVI reacts sensitively for the vegetation quantity compared with NDVI. Shadow index increases as the forest density increases. Thermal index increase as the vegetation quantity increases. Black colored soil area shows a high temperature. Bare soil index increases as the bare soil exposure degrees of ground increase. These index values are calculated for every pixel. Fig. 1 shows the characteristics of four indices compared with forest condition.
Fig. 1 The Characteristics of four indeces for forest condition
Note that as the FCD value increase there is a corresponding increase in the SI value. In other words where there is more tree vegetation there is more shadow. Concurrently, if there is less bare soil (i.e. a lower BI value) there will be a corresponding decrease in the TI value. It should be noted that VI is "saturated" earlier than SI. This simply means that the maximum VI values that can be regardless of the density of the trees or forest. On the other hand, the SI values are primarily dependent on the amount of tall vegtation such as tree which cast a significant shadow.
Table.1 shows combination characteristics between four indices.
Table.1. Combination Characteristics between Four Indices
| |
Hi-FCD |
Low-FCD |
Grass-Land |
Bare Land |
| AVI |
Hi |
Mid |
Hi |
Low |
| BI |
Low |
Low |
Low |
Hi |
| SI |
Hi |
Mid |
Low |
Low |
| TI |
Low |
Mid |
Mid |
Hi |
