| Forest resource inventory known as the secondary inventory is fundamental butsignificant in thoroughly investigating real-time situation of forest. And it is the base ofsilviculture, reasonable management and operation. It helps in establishing forest planning,designing, forest production plan, and forest capitalization administrative as well asevaluating the implements effect of policies, guidelines and laws. Depending on this work, theforest administrative could be healthy and sustainable. In the whole inventory process, themost elementary but important work is forest delineation which divided the forest area intosmall parts---the unit of inventory system and is also the fundamental data for forest planningand designing.Traditional delineation was mostly based on manual force which was time and laborconsuming, and also there were some interpretation mistakes lying in the result becausesubjective opinions would affect the result. Therefore, it is necessary to develop a semi-automethod depending on remote sensing technology to get a more accurate and consistencyresult.Considering the principles in delineation work, taking ALOS multi-spectral andpanchromatic image of Dialing, Heilongjiang province as data source, we had some test onthe following sides.(1) We researched on differences of multi information integrated insegmentation process and developed two indexes to evaluate the segment results according towhich we chose the best segment parameters consisted of segment scheme and scale.(2) Weidentified four basic forest types including coniferous forest, broad-leave forest, coniferousmixed forest and mixed broad-conifer forest by SVM and CART classifier based on spectral,texture and DEM information extracted from segment results.(3) After some manual editwork like small object elimination and border smoothing on result produced by step (1) and (2), we got the final computer delineation. Compared with manual result in some quantitativeindexes, we verified the delineation accuracy of the method we took in this experiment.The result showed that:(1) object-based classification applied in forest identificationbased on high spatial resolution image has more advantages than traditional pixel-based onebecause the more detailed texture features lies in the objects.(2) It is limited whensegmentation only with spectral information. Through evaluating by true sample data, wefound it was such different and the segmentation result was not that good. But whenconsidering terrain information into segmentation, we got a closer result. According to thedefinition of optimized scale, we compared five different scales in segmentation and got fiveSEIs in stand for homogeneity and heterogeneity of each object. We found50as the best scalein forest delineation.(3) In the study on multi-feature forest identification, we had test onthree individual situations which were spectral only, spectral and texture features andadditional terrain information. The result shows the classification accuracy was higher whencoming into the second situation and terrain information was really help in specific types.What’s more, we compared classification accuracy in different scales and verified50was thebest scale and SEI index was effective to express the classification possibility.(4) Taking area,perimeter and other indexed as objective evaluation indexes, computer-assistant delineationwas80%close to manual one and the average area coherency of forest types was73.4%which was such close to reference in spatial distribution. To make a conclusion, we thought itwas so possible that this cost-saving and efficient method would replace the costive andsubjective traditional one. |
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