| Forest resource monitoring is an important basic work of forestry, also an important part of our national situation. Due to the long monitoring period for5to10years and lagging information output of traditional forest resource, it is hard to meet the needs of the forestry development and ecological construction. With the deeper research and application of RS, GIS and GPS technology in forest resources monitoring, dynamic monitoring based on remote sensing technology is brought to the attention of the countries all over the world more and more. This study aims to improve the dynamic monitoring ability of forest resource, which helps strengthen the pertinence and efficiency of forest resources supervision and management. We selected Anshan (in Liaoning province) as demonstration research area, used multi-source, multi-resolution, multi-temporal remote sensing data, combined with field inventory data, to obtain some indexes (such as forest area, canopy density, volume, etc.) through remote sensing inversion technique. Then, the field survey data were used for model testing. Meanwhile, the dynamic of forest resource indexes were analysised by multi-temporal remote sensing data. Finally, we completed the monitoring work of dynamic changes for forest resources at the demonstration zone of Anshan. The results showed that:(1) Based on the current investigation method for forest resources and-PPS sampling, with comprehensive utilization of multiple remote sensing data, multi-level remote sensing method based on sampling technology was put forward. Taking Anshan as an example, TM, MODIS and aerial photographs of2006were used for the multi-level remote sensing experiment. Results showed that, compared with the traditional remote sensing method, the new method can significantly improve the classification accuracy of large area forest distribution by low resolution MODIS images for, which reached to86.77%.(2) Taking TM image as main source of data, and using linear spectral mixing model, the crown density indexes in the images at3stages for the studied area were evaluated. Comparing with field measured values, the prediction accuracy was up to80%. Crown density layers of3stages were generated then, for the analysis on the dynamic changes of forest canopy density. The results indicated that forest crown density in Aanshan showed a trend of "U-shaped" during1995to2006, with a best level in1995.(3) Based on the data of aerial photography, with field inventory and visual plot materials, forest structural indexes were extracted by watershed algorithm. The extraction of tree number, canopy coverage, crown width, and location for single tree were analyzed and discussed then. Results showed the forest structural indexes can be extracted effectively, and it could be applied in forest survey for open forestland or urban area. (4) Taking TM images at3stages in1995,2000and2006,as main data source, combined with the national forest inventory data, forest stock volume was estimated by stepwise regression method. The space-time dynamic progress of stock estimations for3stages were analysed respectively. Results showed a growth trend for forest stock volume from1995to2006, with estimations followed by688.11×105m3、755.86×105m3and1042.67×105m3. Comparing estimated volume in2006with sub-comparment statistic result of that in2007from forest resource inventory planning and design, we found no significant difference between them (13.79%), which suggested the model can ideally estimate volume, with high accuracy (above85%).(5) Based on the national forest inventory data, growth models for height, DBH (diameter at breast height) and volume were finished by site classification method. All of that growth models were used for the updates of tree measured factors in forest resources survey. Meanwhile, prediction models for volume, growth and consumption at stand level were bulited with some related factors as age, height, DBH crown density, etc. Results showed that archival data of forest resources can be updated based on growth model, which would helped for the annual monitoring of forest resources.(6). Our research improved the technology of forest resources monitoring based on Multi-data Source, and built a city level system for the annual monitoring of forest resources, which had been applicated successfully in Anshan. With the demonstration application, we learned the dynamic of forest resource from2007to2009, and also provided reliable basis and construction suggestions for forestry development and eco-environment planning.(7) Our study promoted to comprehensive utilization of multi-source, multi-level remote sensing data. We studied quantitative extraction of forest area by multilevel remote sensing method based on PPS sampling, and combined with field survey data. Focused on dynamic monitoring of forest resource, we took remote sensing technology and spatial information technology as main methods for gathering data. And then, a system for the annual monitoring of forest resources in Anshan was built by series methods as remote sensing macroscopic monitoring&field investigation, quantitative analysis&qualitative analysis, model estimation&field measurements. This study improved the urgently need technology to monitor forest resource dynamic, and successfully applied to Anshan annual monitoring of forest resources dynamic. In conclusion, our research filled the blank of the municipal annual monitoring technology of forest resources. The techonology could greatly improve the ability of forest resources monitoring in Anshan, reduce field investigation cost, and met the need of municipal survey, supervision and enforcement of forest resources. |
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