| 摘要: | 本研究目的主要在藉由遙測技術，估算南庄事業區之森林淨初級生產力(NPP)。研究過程包括2003年SPOT衛星影像之植生指數計算、光合作用有效輻射分量(FPAR)和光合作用有效吸收輻射(APAR)之估算、森林淨初級生產力之估算、以及不同季節和不同林型之淨初級生產力的變化分析，進而探討影像陰影、林型最大光能利用率模擬、及影像獲取等問題對森林NPP估算之影響。研究結果指出，在考量影像陰影和林型最大光能利用率模擬的情況之下，SPOT乾季影像在陰影保留和陰影校正處理後所估算的NPP分別為361.22和293.19 g C m^(-2) yr^(-1)；溼季影像為545.07和572.45 g C m^(-2) yr^(-1)；而使用乾、溼二季影像所估算的NPP則為452.5和432.43 g C m^(-2) yr^(-1)。上述應用遙測方法估算的NPP，經與地面樣區調查方法所得的結果(430 g C m^(-2) yr^(-1))比較後顯示，使用乾、溼二季影像因差值最小，估算結果較佳，其中又以經陰影校正處理的結果最佳。同時，從NPP季節變化的分析結果得知，NPP的積累期主要發生在4~10月份，約占了年淨初級生產力總量的86%。其次，本研究在執行陰影處理過程中亦發現，使用陰影移除的作法並不適合於NPP之遙測估算，其原因主要是植生指數會因線性轉換而造成FPAR估算值變小，並導致NPP估算值偏低的現象。由上述研究結果可得結論如下：應用遙測技術估算森林NPP，除了具有即時、有效、經濟、可行和大尺度的特性之外，並可提供森林NPP時空動態變化分析之用。但因影像陰影和林型最大光能利用率問題會影響森林NPP之估算，因此在應用SPOT植生指數估算NPP時，必須考量其影響效應。此外，台灣因環境關係，較難獲取逐月的SPOT衛星影像，供森林NPP估算之用。針對此問題，本研究使用SPOT季節性影像進行NPP估算的作法，可提供影像獲取不易但乾、溼季分明的地區做為參考。
This study aimed to apply remote sensing to estimate the forest net primary productivity (NPP) of Nanzhuang National Forest in Taiwan. The research processes included calculating vegetation indices from SPOT images of 2 seasons in 2003, estimating the fraction of photosynthetically active radiation (FPAR) and photosynthetically active radiation absorbed by the different forest types (APAR), estimating the NPP, and finally analyzing NPP variations from different seasons and forest types. Furthermore, the shadow effect, simulation of the maximum light use efficiency for different forest types, and the problem of image acquisition for NPP estimation in Taiwan were also investigated. The results are as follows. Under the consideration of the shadow effect and simulation of the maximum light use efficiency for different forest types, the NPP estimation on the dry season image was 361.22 g C m^(-2) yr^(-1) with shadow retention and 293.19 g C m^(-2) yr^(-1) with shadow correction, while the wet season image was 545.07 g C m^(-2) yr^(-1) with shadow retention and 572.45 g C m^(-2) yr^(-1) with shadow correction. As for using dry- and wet-season images, NPP values were 452.5 and 432.43 g C m^(-2) yr^(-1) with shadow retention and shadow correction, respectively. A comparison between the estimated NPP and the field-measured carbon amount derived from forest inventory data (i.e., 430 g C m^(-2) yr^(-1)) indicated that the NPP estimated from 2 seasonal images had the best result because of the smallest bias. Meanwhile, the seasonal analysis of NPP variations was significant in the study area. The majority of NPP accumulation was about 86% of the annual NPP and was mainly distributed between April and October. In addition, we propose that among the 3 shadow processes, shadow removal cannot be applied to estimate the NPP because a lower FPAR was generated when estimating the FPAR due to the linear transformation of vegetation indices. We concluded that remote sensing is a timely, effective, feasible, and large-scale approach for estimating the forest NPP and provides the NPP for a spatiotemporal variation analysis. Meanwhile, the shadow effect and simulation of the maximum light use efficiency for forest types affect the estimation of forest NPP. Therefore, their effects should be considered when applying SPOT vegetation indices to estimate forest NPP. In addition, an alternative approach using seasonal images is also feasible to eliminate the problem with image acquisition in Taiwan. |
