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    王雪军, 马炜, 黄国胜, 陈新云, 党永峰. 基于遥感大样地抽样调查的森林面积监测[J]. 北京林业大学学报, 2015, 37(11): 1-9. DOI: 10.13332/j.1000-1522.20150083
    引用本文: 王雪军, 马炜, 黄国胜, 陈新云, 党永峰. 基于遥感大样地抽样调查的森林面积监测[J]. 北京林业大学学报, 2015, 37(11): 1-9. DOI: 10.13332/j.1000-1522.20150083
    WANG Xue-jun, MA Wei, HUANG Guo-sheng, CHEN Xin-yun, DANG Yong-feng.. Estimation of forest area by large plot interpretation and division based on remote sensing.[J]. Journal of Beijing Forestry University, 2015, 37(11): 1-9. DOI: 10.13332/j.1000-1522.20150083
    Citation: WANG Xue-jun, MA Wei, HUANG Guo-sheng, CHEN Xin-yun, DANG Yong-feng.. Estimation of forest area by large plot interpretation and division based on remote sensing.[J]. Journal of Beijing Forestry University, 2015, 37(11): 1-9. DOI: 10.13332/j.1000-1522.20150083

    基于遥感大样地抽样调查的森林面积监测

    Estimation of forest area by large plot interpretation and division based on remote sensing.

    • 摘要: 基于黑龙江森工集团2009年林地“一张图”成果数据,利用2010—2013年多源遥感数据,结合2010年一类清查和二类调查成果及其他林业经营资料,实践了遥感样地判读与地面样地抽样调查相结合的大样地调查方法的应用。结果表明: 1)黑龙江森工集团可系统布设间隔20 km的大样地231个,面积以4 km×4 km为宜,对林地面积估计的总体变动系数为14.41%;2)遥感数据对大样地各地类综合正判率为90.68%,以有林地96.82%最高,且遥感数据分辨率降低会导致地类可判读性的降低,分辨率提高则有利于提高判读精度;3)不同面积大样地对林地、有林地面积监测结果的影响差异不明显,4 km×4 km大样地调查得到2013年林地面积899.53×104 hm2,占土地面积的89.47%,其中有林地面积876.81×104 hm2;4)2010—2013年森林面积净减5.94×104 hm2(年均减少1.48×104 hm2),与封育相比造林更新是促使森林增加的主要途径,毁林开垦、森林采伐和林地征占用是导致森林面积减少的主要因素。研究认为,基于大样地区划调查的森林面积监测方法科学、经济,充分发挥了遥感技术的优势,提高了遥感数据的应用效率,提升了森林资源变化的动态监测能力,应予以推广应用。

       

      Abstract: Based on “forestland spatial distribution” database, multisource remote sensing (RS) data from 2010 to 2013, continuous forest inventory data in 2010 and other forestry management materials, the area of forest and other different land types in Longjiang Forest Industry Group (LFIG, in Heilongjiang Province) was estimated by interpretation and division on RS large plots. Results showed that: 1) 231 large plots with 20 km interval were set in LFIG, and the 4 km×4 km quadrat was suitable for monitoring with variation coefficient of forest land area of 14.41%. 2) Interpretation accuracy of land type area increased with resolution ratio of RS image. Interpretation accuracy of all land types in RS large plots reached 90.68%, highest as 96.82% of that in forestry land. 3) There was no significant difference among forestry land area, which was monitored by large plots at different scales. Forestry land of LFIG monitored by 4 km×4 km large plots occurred in a higher proportion of 89.47% in land, as almost 9 million hectare, which included 8.7 million hectare forest land. 4) Beside enclosure, afforestation was the main practice for the rise in forestland area. Conversely, deforestation, forest harvesting and forestland requisition occupation all caused decline of forestland area. Forestland area presented a net decrease of 59.4 thousand hectare from 2010 to 2013, i.e., an average reduction of 14.8 thousand hectare every year in LFIG. In conclusion, the RS large plot method proposed in this research for dynamic monitoring forest resources is reliable and worthy of promotion to relevant fields.

       

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