CAJ | 학술논문

【目的】采用 KNN方法进行碳储量估测，并对估测后的数据进行各种校正处理，绘制森林地上碳储量的空间分布图，为我国森林碳储量和固碳潜力的研究提供基础数据和科学依据。【方法】以黑龙江省大兴安岭为研究区(50°05'—53°33'N，121°11'—127°01'E)，基于2010年森林资源连续清查固定样地和同年 Landsat5 TM 影像数据，利用 k－邻近法( KNN)在像素级水平上对森林地上碳储量进行估算。采用多准则方法分东、南、北和中4个区域对样地坐标和其对应的影像光谱值进行坐标重配准，并根据实测样地数据对坐标重配置前后不同林分类型地上碳储量估测精度进行评价；针对 KNN方法像素级估测结果存在明显的高值区域低估和低值区域高估现象，应用直方图匹配方法对估测结果进行变动范围调整；并根据样地实测碳储量和 KNN 估测值间的回归关系对调整后的结果分区域进行进一步匹配校正后处理，绘制森林碳储量的空间分布图。【结果】总体来说，本研究区域像元尺度KNN估测的欧式距离优于马氏距离，均方根误差随着最邻近值 k的增大而降低，当 k大于6时变化缓慢，并逐渐趋于稳定；坐标误差校正后，各林分类型森林地上碳储量的估测精度均显著提高，平均均方根误差由17.23降低到14.3 t·hm －2；直方图匹配后，各区域样地点高值区域低估和低值区域高估现象均有很大程度改善，实测值和估测值间的相关关系明显增强，然而高值地区(碳储量大于20 t·hm －2)出现过高估计现象；经匹配校正后处理的均值、标准差、直方图和累积频率分布图更接近样地实测值，均方根误差也明显降低，高值地区过高估计现象得到很好校正。【结论】森林资源清查数据、遥感数据及 KNN方法相结合逐渐成为区域尺度森林参数空间连续估测的重要手段。同利用光谱值和森林参数建立的回归模型相比，KNN方法能够更多地考虑到森林参数同光谱值之间的非线性依赖关系；但 KNN估测方法除了受距离度量标准、最邻近值 k的大小以及影像波段的选取等因素影响外，还存在如样地坐标和对应的影像光谱值匹配误差、像素级估测结果多呈明显集中分布趋势等问题，使得该方法的应用受到一定限制。本文的研究表明，对这些因素进行合理的校正，将更有利于区域尺度森林参数的精确估计和反演。【目的】採用 KNN方法進行碳儲量估測，併對估測後的數據進行各種校正處理，繪製森林地上碳儲量的空間分佈圖，為我國森林碳儲量和固碳潛力的研究提供基礎數據和科學依據。【方法】以黑龍江省大興安嶺為研究區(50°05'—53°33'N，121°11'—127°01'E)，基于2010年森林資源連續清查固定樣地和同年 Landsat5 TM 影像數據，利用 k－鄰近法( KNN)在像素級水平上對森林地上碳儲量進行估算。採用多準則方法分東、南、北和中4箇區域對樣地坐標和其對應的影像光譜值進行坐標重配準，併根據實測樣地數據對坐標重配置前後不同林分類型地上碳儲量估測精度進行評價；針對 KNN方法像素級估測結果存在明顯的高值區域低估和低值區域高估現象，應用直方圖匹配方法對估測結果進行變動範圍調整；併根據樣地實測碳儲量和 KNN 估測值間的迴歸關繫對調整後的結果分區域進行進一步匹配校正後處理，繪製森林碳儲量的空間分佈圖。【結果】總體來說，本研究區域像元呎度KNN估測的歐式距離優于馬氏距離，均方根誤差隨著最鄰近值 k的增大而降低，噹 k大于6時變化緩慢，併逐漸趨于穩定；坐標誤差校正後，各林分類型森林地上碳儲量的估測精度均顯著提高，平均均方根誤差由17.23降低到14.3 t·hm －2；直方圖匹配後，各區域樣地點高值區域低估和低值區域高估現象均有很大程度改善，實測值和估測值間的相關關繫明顯增彊，然而高值地區(碳儲量大于20 t·hm －2)齣現過高估計現象；經匹配校正後處理的均值、標準差、直方圖和纍積頻率分佈圖更接近樣地實測值，均方根誤差也明顯降低，高值地區過高估計現象得到很好校正。【結論】森林資源清查數據、遙感數據及 KNN方法相結閤逐漸成為區域呎度森林參數空間連續估測的重要手段。同利用光譜值和森林參數建立的迴歸模型相比，KNN方法能夠更多地攷慮到森林參數同光譜值之間的非線性依賴關繫；但 KNN估測方法除瞭受距離度量標準、最鄰近值 k的大小以及影像波段的選取等因素影響外，還存在如樣地坐標和對應的影像光譜值匹配誤差、像素級估測結果多呈明顯集中分佈趨勢等問題，使得該方法的應用受到一定限製。本文的研究錶明，對這些因素進行閤理的校正，將更有利于區域呎度森林參數的精確估計和反縯。
【목적】채용 KNN방법진행탄저량고측，병대고측후적수거진행각충교정처리，회제삼임지상탄저량적공간분포도，위아국삼림탄저량화고탄잠력적연구제공기출수거화과학의거。【방법】이흑룡강성대흥안령위연구구(50°05'—53°33'N，121°11'—127°01'E)，기우2010년삼림자원련속청사고정양지화동년 Landsat5 TM 영상수거，이용 k－린근법( KNN)재상소급수평상대삼임지상탄저량진행고산。채용다준칙방법분동、남、북화중4개구역대양지좌표화기대응적영상광보치진행좌표중배준，병근거실측양지수거대좌표중배치전후불동림분류형지상탄저량고측정도진행평개；침대 KNN방법상소급고측결과존재명현적고치구역저고화저치구역고고현상，응용직방도필배방법대고측결과진행변동범위조정；병근거양지실측탄저량화 KNN 고측치간적회귀관계대조정후적결과분구역진행진일보필배교정후처리，회제삼림탄저량적공간분포도。【결과】총체래설，본연구구역상원척도KNN고측적구식거리우우마씨거리，균방근오차수착최린근치 k적증대이강저，당 k대우6시변화완만，병축점추우은정；좌표오차교정후，각림분류형삼임지상탄저량적고측정도균현저제고，평균균방근오차유17.23강저도14.3 t·hm －2；직방도필배후，각구역양지점고치구역저고화저치구역고고현상균유흔대정도개선，실측치화고측치간적상관관계명현증강，연이고치지구(탄저량대우20 t·hm －2)출현과고고계현상；경필배교정후처리적균치、표준차、직방도화루적빈솔분포도경접근양지실측치，균방근오차야명현강저，고치지구과고고계현상득도흔호교정。【결론】삼림자원청사수거、요감수거급 KNN방법상결합축점성위구역척도삼림삼수공간련속고측적중요수단。동이용광보치화삼림삼수건립적회귀모형상비，KNN방법능구경다지고필도삼림삼수동광보치지간적비선성의뢰관계；단 KNN고측방법제료수거리도량표준、최린근치 k적대소이급영상파단적선취등인소영향외，환존재여양지좌표화대응적영상광보치필배오차、상소급고측결과다정명현집중분포추세등문제，사득해방법적응용수도일정한제。본문적연구표명，대저사인소진행합리적교정，장경유리우구역척도삼림삼수적정학고계화반연。
Objective]Forest is the major terrestrial carbon pool. Accurate assessment of forest carbon storage and its spatial distribution is the key to investigating the terrestrial carbon cycle. [Method]Based on the PSPs data from continuous forest resource inventory and Landsat5 TM in 2010,the k-nearest neighbor ( KNN ) method was used to estimate,at the pixel level,the aboveground carbon storage in Daxing’an Mountains of Heilongjiang Province. The field PSP data and its corresponding satellite image information were reassigned using a multi-criteria approach in east,south, northand middle regions. The accuracy estimation of different forests before and after the reassignment was also evaluated according to the data of PSPs. In view of the phenomenon that the pixel level KNN estimation having the large values underestimated and small values overestimated,the histogram matching method was used to adjust the variation range of the estimation results. Then,further correction treatment was applied to each region according to the regression equations of field data and the estimation data from the histogram matching until the spatial distribution map of forest carbon storage was drawn.[Result]Overall,Euclidean distance was better than Mahalanobis in our study area at the pixel level of KNN estimation. The root mean square error decreased with the increase of the nearest neighbor k,whereas,the tendency was slow down and gradually stabilized when k is greater than 6 . The estimate accuracy was improved significantly at the pixel level in each forest type when the coordinate errors was corrected,and the average root mean square error was reduced from 17. 23 to 14. 3 t · hm - 2. After histogram matching,the phenomenon of underestimation for high value and overestimation for low value was greatly improved in each region,and the correlation between filed data and estimation data was enhanced obviously. However,high value area ( carbon storage value was larger than 20 t·hm - 2 ) was overestimated evidently. The mean value, standard deviation, histogram and cumulative frequency distribution graph of the final corrected values through the further correction treatment were more close to those of the field values, and the overestimation in high value area was also well corrected. [Conclusion]The integration of forest inventory plot data, satellite image data with the KNN method has gradually become a popular approach for spatial continuous estimation of forest vegetation parameters over large regions. Compared with the regression model established by the spectral value and forest parameters,KNN method is more focuses on the nonlinear dependence between forest parameters and spectral values. However,the KNN estimation method is not only influenced by the distance metric standard,the nearest neighbor k and the image band selection,but it also has the problems such as the location errors of field plots with respect to the satellite image,the tendency to having a suppressed variation range at the pixel level,which make this method subjected to a certain application restrictions. This study indicated that if these impact factors were reasonably corrected,it would be more conducive to the accurate estimation and inversion of forest parameters at regional scale.