Comparative analysis of GF-1,HJ-1,and Landsat-8 data for estimating the leaf area index of winter wheat

Using simultaneously collected remote sensing data and field measurements,this study firstly assessed the consistency and applicability of China high-resolution earth observation system satellite 1(GF-1) wide field of view(WFV) camera,environment and disaster monitoring and forecasting satellite(HJ-1) charge coupled device(CCD),and Landsat-8 operational land imager(OLI) data for estimating the leaf area index(LAI) of winter wheat via reflectance and vegetation indices(VIs). The accuracies of these LAI estimates were then assessed through comparison with an empirical model and the PROSAIL radiative transfer model. The effects of radiation calibration,spectral response functions,and spatial resolution on discrepancies in the LAI estimates between the different sensors were also analyzed. The results yielded the following observations:(1) The correlation between reflectance from different sensors is relative good,with the adjusted coefficients of determination(R2) between 0.375 to 0.818. The differences in reflectance are ranging from 0.002 to 0.054. The correlation between VIs from different sensors is high with the R2 between 0.729 and 0.933. The differences in the VIs are ranging from 0.07 to 0.156. These results show the three sensors' images can all be used for cross calibration of the reflectance and VIs.(2) The four VIs from the three sensors are all demonstrated to be highly correlated with LAI(R2 between 0.703 and 0.849). The linear models associated with the 2-band enhanced vegetation index(EVI2),which feature the highest R2(higher than 0.746) and the lowest root mean square errors(RMSE)(less than 0.21),were selected to estimate the winter wheat LAI. The accuracy of the estimated LAI from Landsat-8 was the highest,with the relative errors(RE) of 2.18% and an RMSE of 0.13,while the HJ-1 was the lowest,with the RE of 2.43% and the RMSE of 0.15.(3) The inversion errors in the different sensors' LAI estimates using the PROSAIL model are small. The accuracy of the GF-1 is the highest with the RE of 3.44%,and the RMSE of 0.22,whereas that of the HJ-1 is the lowest with the RE of 4.95%,and the RMSE of 0.26.(4) The effects of the spectral response function and radiation calibration for the different sensors are small and can be ignored,but the effects of spatial resolution are significant and must be taken into consideration in practical applications.     

新疆喀纳斯国家自然保护区植被叶面积指数观测与遥感估算

叶面积指数(Leaf Area Index,LAI)是重要的植被结构参数,调控着植被与大气之间的物质与能量交换,在生态环境脆弱的我国西北部开展植被LAI的研究对阐明该地区植被对气候变化和人类活动的响应特征具有重要的科学意义。利用LAI-2200和TRAC仪器观测了新疆喀纳斯国家级自然保护区森林和草地的有效叶面积指数(LAI_e)和真实LAI,构建了其遥感估算模型,生成了研究区LAI_e和LAI的空间分布图。在此基础上,分析了LAI随地形因子(海拔、坡度、坡向)的变化特征,探讨了将其应用于估算研究区森林生物量密度的可行性,并评估了研究区MODIS LAI产品的精度。结果表明:研究区阔叶林、针阔混交林、针叶林、草地LAI_e的平均值分别为4.40、3.18、2.57、1.76,LAI的平均值分别为4.76、3.93、3.27、2.30。LAI_e和LAI的高值主要集中分布在湖泊和河流附近;植被LAI随海拔、坡度和坡向的变化表现出明显的垂直地带性的特点。LAI随海拔和坡度的增加呈现先增加后减小的变化趋势,坡向对针叶林和草地LAI的影响明显,但对阔叶林和针阔混交林LAI的影响较弱;森林生物量密度(BD)随LAI增加而线性增加(BD=44.396LAI-25.946,R²=0.83),研究区森林生物量密度平均值为120.3 t/hm²,估算的总生物量为5.0×10⁶t;MODIS LAI产品与利用TM数据生成的LAI之间具有一定的相似性(森林R²=0.42,草地R²=0.53),但森林和草地的MODIS LAI产品分别比利用TM数据生成的LAI偏低16.5%和24.4%。     

Assimilation of temporal-spatial leaf area index into the CERES-Wheat model with ensemble Kalman filter and uncertainty assessment for improving winter wheat yield estimation

To accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations,winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index(LAI) values. The performances of the calibrated crop environment resource synthesis for wheat(CERES-Wheat) model for two different assimilation scenarios were compared by employing ensemble Kalman filter(EnKF)-based strategies. The uncertainty factors of the crop model data assimilation was analyzed by considering the observation errors,assimilation stages and temporal-spatial scales. Overall,the results indicated a better yield estimate performance when the EnKF-based strategy was used to comprehensively consider several factors in the initial conditions and observations. When using this strategy,an adjusted coefficients of determination(R~2) of 0.84,a root mean square error(RMSE) of 323 kg ha~(–1),and a relative errors(RE) of 4.15% were obtained at the field plot scale and an R~2 of 0.81,an RMSE of 362 kg ha~(–1),and an RE of 4.52% were obtained at the pixel scale of 30 m×30 m. With increasing observation errors,the accuracy of the yield estimates obviously decreased,but an acceptable estimate was observed when the observation errors were within 20%. Winter wheat yield estimates could be improved significantly by assimilating observations from the middle to the end of the crop growing seasons. With decreasing assimilation frequency and pixel resolution,the accuracy of the crop yield estimates decreased; however,the computation time decreased. It is important to consider reasonable temporal-spatial scales and assimilation stages to obtain tradeoffs between accuracy and computation time,especially in operational systems used for regional crop yield estimates.     

Phenotyping a diversity panel of quinoa using UAV-retrieved leaf area index,SPAD-based chlorophyll and a random forest approach

Given its high nutritional value and capacity to grow in harsh environments, quinoa has significant potential to address a range of food security concerns. Monitoring the development of phenotypic traits during field trials can provide insights into the varieties best suited to specific environmental conditions and management strategies. Unmanned aerial vehicles (UAVs) provide a promising means for phenotyping and offer the potential for new insights into relative plant performance. During a field trial exploring 141 quinoa accessions, a UAV-based multispectral camera was deployed to retrieve leaf area index (LAI) and SPAD-based chlorophyll across 378 control and 378 saline-irrigated plots using a random forest regression approach based on both individual spectral bands and 25 different vegetation indices (VIs) derived from the multispectral imagery. Results show that most VIs had stronger correlation with the LAI and SPAD-based chlorophyll measurements than individual bands. VIs including the red-edge band had high importance in SPAD-based chlorophyll predictions, while VIs including the near infrared band (but not the red-edge band) improved LAI prediction models. When applied to individual treatments (i.e. control or saline), the models trained using all data (i.e. both control and saline data) achieved high mapping accuracies for LAI (R²?=?0.977–0.980, RMSE?=?0.119–0.167) and SPAD-based chlorophyll (R²?=?0.983–0.986, RMSE?=?2.535–2.861). Overall, the study demonstrated that UAV-based remote sensing is not only useful for retrieving important phenotypic traits of quinoa, but that machine learning models trained on all available measurements can provide robust predictions for abiotic stress experiments.

     

种植密度和施肥量对南麦618农艺性状、叶绿素含量及产量的影响

[目的]探讨小麦品种南麦618在四川东北部丘陵地区的最佳种植密度和施肥量,为科学制定其高产高效栽培技术提供参考依据.[方法]采用2因素3水平完全随机区组设计,种植密度3个水平分别为2.18×106株/ha(A1)、2.48×106株/ha(A2)和2.78×106株/ha(A3),施肥量3个水平分别为N 33.0 kg/ha+P 25.7 kg/ha(B1)、N 66.0 kg/ha+P 51.3 kg/ha(B2)和N 99.0 kg/ha+P 77.0 kg/ha(B3).生育期内分别测定南麦618的相关农艺性状、旗叶叶绿素含量和单茎绿叶面积,成熟时各小区单独收获计算产量.[结果]随着种植密度加大,南麦618的单位面积有效穗数增加,成穗率下降,穗粒数和千粒重呈不同程度的下降趋势,穗长变短,产量、收获指数呈上升趋势,旗叶叶绿素含量降低,单茎绿叶面积减小.随着施肥量的增加,南麦618的单位面积有效穗数增加,成穗率下降,穗粒数和千粒重呈不同程度的上升趋势,穗长变长,产量、收获指数呈上升趋势,旗叶叶绿素含量升高,单茎绿叶面积增大.种植密度和施肥量的交互作用对穗粒数和产量有极显著影响(P<0.01),有效穗数与产量显著相关(P<0.05).综合分析,A2B3处理的农艺性状最优,光合性能最佳,产量最高(5.49×103 kg/ha).[结论]在川东北丘陵区种植南麦618时,种植密度以2.48×106株/ha、施肥量以N 99.0 kg/ha+P 77.0 kg/ha为宜.     

基于CBERS-02B卫星影像光谱信息的水稻种植面积提取方法——以南京市溧水县为例

利用水稻成熟期的影像,在进行光谱信息的统计和分析的基础上,对比水稻与其他植被在各波段DN(digital number)值以及NDVI(normalized difference vegetable index)值的数值差异,总结出水稻与其他植被反射光谱特征,认为成熟期的水稻在红光、绿光、蓝光波段DN值以及NDVI值上具有异于其他植被的特征,并通过波段运算扩大其数值差异,建立水稻像元提取模型,确定阈值,从而进行水稻种植面积的提取,精度可达91.95%。该方法能满足小区域的水稻种植面积提取和制图,并能够满足一定的精度要求。     

Extended-altitude, aerial mapping of crop NDVI using an active optical sensor: A case study using a Raptor™ sensor over wheat

Recently reported testing of active, optical crop sensors in low-level aircraft have demonstrated a new class of airborne sensing system that can be deployed under any ambient illumination conditions, including at night. A second-generation, high-powered, light-emitting diode system has been assembled and tested over a 80 ha field of wheat (Triticum aestevum) by mapping the normalised difference vegetation index (NDVI) at altitudes ranging from 15 to 45 m above the canopy; significantly higher altitudes than existing systems. Comparisons with a detailed on-ground NDVI survey indicated the aerial sensor values were highly correlated to the on-ground sensor (0.79 < R² < 0.85), with close to unity slope and zero offset. The maximum average deviation between aerial and on-ground NDVI values was 0.04. Sample calculations involving two exemplar algorithms, one for estimating monoculture pasture biomass and the other for estimating wheat yield, indicate such deviations to have no significant effect on prediction accuracy. The subsequent NDVI maps proved to be invariant to sensor height over the 15-45 m altitude range meaning this new sensor configuration can be deployed over undulating crops and pastures and in areas with nearby obstacles such as trees and buildings.     

Low altitude remote sensing technologies for crop stress monitoring: a case study on spatial and temporal monitoring of irrigated pinto bean

Site-specific crop management is a promising approach to maximize crop yield with optimal use of rapidly depleting natural resources. Availability of high resolution crop data at critical growth stages is a key for real-time data-driven decisions during the production season. The goal of this study was to evaluate the possibility of using small unmanned aerial system (UAS)-based remote sensing technologies to monitor the crop stress of irrigated pinto beans (Phaseolus vulgaris L.) with varied irrigation and tillage treatments. A small UAS with onboard multispectral and infrared thermal imaging sensors was used to collect data from bean field plots on three growth stages in 2015 and 2016, respectively. Indicators including green normalized vegetation index (GNDVI), canopy cover (CC, ratio of ground covered by crop canopy to the total plot area) and canopy temperature (CT, °C) of crops were extracted from imaging data and correlated with ground-reference crop yield and leaf area index (LAI) estimated with a handheld ceptometer. Results show that GNDVI, CC and CT were able to differentiate crops with full and deficit irrigation treatments at each of the three growth stages in both years. Developed indicators were strongly correlated with to the crop yield with Pearson correlation coefficients (r) of approximate 0.71 and 0.72 for GNDVI and CC, respectively, in the early growth stage (54 days after planting) in both years. Canopy temperature showed even stronger correlation (r > 0.8) with yield at early growth stage. Performance of small UAS-based imagery-based indicators in crop stress monitoring and crop yield estimation was better than or comparable to that of the ground-based LAI estimates, indicating the potential of such remote sensing tool in rapid crop stress monitoring and management.     

麦红吸浆虫在小麦穗上的分布和危害特点

通过调查麦红吸浆虫幼虫在小麦穗上的分布和危害特点 ,进一步明确 :麦穗上麦红吸浆虫幼虫聚集分布的田间分布格局与虫口密度无关 ;麦红吸浆虫在麦穗上的分布和危害与麦穗大小和受害部位有一定的关系。就麦穗大小而言 ,以中型穗上虫量最多 ,受害最重 ;大型穗次之 ;小型穗上虫量最少 ,受害最轻。就麦穗受害部位而言 ,以中部虫量最多 ,受害最重 ;下部次之 ;上部虫量最少 ,受害最轻。     

玉米纹枯病危害损失及防治指标的初步研究

研究结果表明,随着玉米纹枯病病情指数的提高,玉米叶片自下而上干枯严重,产量损失率提高。玉米产量损失率与玉米纹枯病病情指数及病株率呈正相关,相关系数分别为r1=0.9973^※※、r2=0.9732^※※。产量损失率与病情指数及病株率的回归式分别为y=0.39x1-2.41,y=0.43x2-11.94。拟定玉米纹枯病防治指标为病情指数16.4,或病株率为37.1%。     

半干旱区旱地春小麦全膜覆土穴播对土壤水热效应及产量的影响

以春小麦"陇春27"为试材,研究半干旱区旱地全膜覆土穴播对土壤水热效应及小麦产量的影响。结果表明:与裸地平作(CK)相比,全膜覆土穴播(PM)和全沙覆盖平作(SM)增温效果呈"挂钩型",增温效果在小麦拔节期前最为明显,PM 0-25 cm土壤平均温度较CK提高1.8 ℃,全沙覆盖平作提高1.4 ℃,拔节后PM和SM的增温效果逐渐减弱直至消失,而在成熟期又呈现较弱的增温效果。PM和SM能提高小麦出苗后耗水速度并加大耗水量,其中,小麦拔节到扬花期耗水量增加最多,分别较CK平均增加54.93%和31.54%,且此阶段越是干旱,促进耗水作用越明显,PM促进作用大于SM。PM和SM能显著提高小麦阶段性水分利用效率(WUE_b),其中PM以苗期提高最多,2a平均较CK提高365.17%,SM拔节期提高最多,2年平均较CK提高119.00%。PM和SM在增温、促进耗水作用下使小麦各生育期提前并增加单株干重,产量较CK分别平均增加432.28%和375.82%, 水分利用效率(WUE)分别平均增加351.51%和338.29%,而且越干旱年份增产效应愈加明显。可见,PM和SM在越为干旱的年份促进耗水、增产和提高WUE的作用越显著,PM效果强于SM。     

Ultra low-level airborne (ULLA) sensing of crop canopy reflectance: A case study using a CropCircle™ sensor

Operational airborne and satellite remote sensing in agriculture remains constrained by matching platform availability to suitable daytime weather and illumination conditions, crop development, and availability of ground staff. An ultra low-level aircraft carrying an active NIR/Red CropCircle™ sensor was successfully deployed to record and subsequently map crop vigour via the simple ratio (SR) index over a field of sorghum. Given the logging frequency of ≈20 Hz and the presence of alternate rows of bare soil, the Moiré effect reduced the contrast between crop and bare soil skip-rows. Such effects would not be expected to occur in non-skip-row crops. The ultra low-level airborne (ULLA)-SR map derived from the 20 m transect records compared favorably with the SR map derived from a meter-resolution airborne digital multispectral image that was re-sampled to a similar spatial resolution. This case study, involving a CropCircle™ sensor mounted in a low-level aircraft demonstrates another deployment option for users of this class of sensor. Moreover, an ULLA configuration offers the potential for greater flexibility in scheduling compared to airborne imaging, given it can be flown at any sun-angle, under cloud, at night, and may easily be incorporated into aircraft already conducting low-level operations, for example crop dusting and reconnaissance, over agricultural fields.     

Monitoring of winter wheat distribution and phenological phases based on MODIS time-series: A case study in the Yellow River Delta,China

Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in the Yellow River Delta(YRD) region using moderate resolution imaging spectroradiometer(MODIS) time-series data. The normalized difference vegetation index(NDVI) was obtained by calculating the surface reflectance in red and infrared. We used the Savitzky-Golay filter to smooth time series NDVI curves. We adopted a two-step classification to identify winter wheat. The first step was designed to mask out non-vegetation classes, and the second step aimed to identify winter wheat from other vegetation based on its phenological features. We used the double Gaussian model and the maximum curvature method to extract phenology. Due to the characteristics of the time-series profiles for winter wheat, a double Gaussian function method was selected to fit the temporal profile. A maximum curvature method was performed to extract phenological phases. Phenological phases such as the green-up, heading and harvesting phases were detected when the NDVI curvature exhibited local maximum values. The extracted phenological dates then were validated with records of the ground observations. The spatial patterns of phenological phases were investigated. This study concluded that, for winter wheat, the accuracy of classification is 87.07%, and the accuracy of planting acreage is 90.09%. The phenological result was comparable to the ground observation at the municipal level. The average green-up date for the whole region occurred on March 5, the average heading date occurred on May 9, and the average harvesting date occurred on June 5. The spatial distribution of the phenology for winter wheat showed a significant gradual delay from the southwest to the northeast. This study demonstrates the effectiveness of our proposed method for winter wheat classification and phenology detection.     

高寒地区居住小区海绵化改造建设研究——以西宁市安泰华庭小区为例

【目的】受既有建筑、管网、高差等基础条件的限制,城市建成区的海绵化改造是海绵城市建设的难点,尤其是高寒地区居住区海绵建设面临诸多现实问题和技术困难。本文以西宁市安泰华庭小区海绵化改造为例,详细介绍其海绵化改造具体方案,并验证实施方案的有效性,为此类地区开展海绵城市建设提供案例依据。【方法】通过对现状场地条件分析、LID设施的选择与布置,运用SWMM建立不同重现期(2年一遇、5年一遇、10年一遇、20年一遇、50年一遇)2 h降雨下海绵化改造前后的两种模型,并进行对比,同时在改造后模型中使用实测降雨数据模拟,并与排放口实测数据对比验证。【结果】(1)依据场地现状可使用下凹绿地、植草沟渠、高强度透水铺装、屋顶花园、雨水调蓄池5种LID设施组合的海绵设施系统。(2)在10、20及50年重现期降雨条件下,C1排水口的流量峰值分别降低了83.45%、79.31%和57.39%,峰现时间延迟了82、40和30 min,C3排水口的流量峰值分别降低了65.96%、34.04%和22.34%,峰现时间延迟了65、50和62 min,达到了很好的削峰延时效果;改造后50年重现期下排水口的流量均低于改造前2年重现期排放口的流量,小区的防洪排涝能力由2年一遇提高到50年一遇。(3)通过与3次实际降雨值和产流数据对比,发现模拟数据与实际监控数据能够很好地匹配,说明设计模型的参数率定合理,模拟结论具有较高的可信度。【结论】使用多种LID设施组合的海绵系统改造后的区域相对于未改造区域的产流峰值、出流量均大幅度降低,充分说明了在高寒地区海绵化改造的实施有效地削减了外排量,实现了削峰、延时、去污等综合目标。     

Root crops diversity and agricultural resilience: a case study of traditional agrosystems in Vanuatu (Oceania)

In Vanuatu (Oceania), small-scale farmers’ subsistence still largely relies on the sustainable use and maintenance of a wide-ranging biodiversity out of which root and tuber crops provide the bulk of daily subsistence. In neighboring countries, foreign influence since the first European contacts, further associated changes and the introduction of new crop species have induced a loss of cultivated diversity. This paper presents a baseline study of the diversity of root and tuber crops in ten communities throughout Vanuatu. In a context where the smallholders’ agrosystems are increasingly considered as key components for the global conservation of crop genetic resources, this study provides clues to better understand the effective roles of biodiversity in traditional agrosystems. It also provides insights on the rationale behind the constitution of agricultural portfolios and discusses how the cultivated diversity allows communities to cope with changes and pressures. The paper also shows that recently introduced crops neither seem to have compromised agricultural diversity nor drastically changed the agrosystems in Vanuatu. On the contrary, such crops are used by farmers to strengthen the resilience of their agrosystems. A discussion then presents the idea of continuity through change and novelty as a critical factor for resilience. The paper concludes by discussing the role of indigenous agriculture and culture in ensuring food security and in development strategies in a larger context.     

大棚非洲菊烟粉虱成虫空间分布模型及抽样技术研究

采用5种聚集度指标测定以及Taylor的幂法则与Iwao的M*-M直线回归方程分析,烟粉虱成虫在大棚非洲菊上呈聚集分布。其聚集原因经Blackith种群聚集均数(λ)分析,当M<2.0995时,其聚集是由于某些环境如气候、土壤湿度、植株生长状况等所致;当M≥2.0995时,其聚集是由于害虫本身的群集行为与环境条件综合影响所致。在此基础上,提出了最适理论抽样数模型[N=t2/D2(1.1716/M 0.1377)]与最佳序贯抽样模型[T0(N)=3N±4.2728N]。     

贵州东北部五县陆生贝类资源调查及区系分析*(腹足纲:前鳃亚纲,肺螺亚纲)

笔者对贵州省东北部地区进行了陆生贝类资源调查,获得陆生贝类标本6000多号.经鉴定得69种,分隶于12科31属.区系分析表明:黔东北部地区的陆生贝类以东洋界西南区种类和华中区种类为优势.在69种中,有30种分布于华中区和西南区,约占总数的43.48%;有13种分布于华南区、华中区和西南区(即东洋界),约占总数的18.84%;有9种仅分布于华中区,约占总数的13.04%.将本调查结果与贵州南部地区陆生贝类物种进行比较,发现两地的种类差异明显.     

深松(耕)时期与方式对砂姜黑土耕层改良及其 冬小麦物质积累和养分吸收的影响

为探明深松(耕)时期与方式对砂姜黑土耕层改良、冬小麦物质积累和养分吸收的影响,设置5个周年定位处理,即秋季旋耕+夏季免耕(ART+SNT,对照)、秋季深松+夏季免耕(ASS+SNT)、秋季深耕+夏季免耕(ADMP+SNT)、秋季深耕+夏季下位深松(ADMP+SSSS)和秋季深耕+夏季侧位深松(ADMP+SSSL),研究深松(耕)时期与方式对砂姜黑土土壤孔隙、土壤含水量、土壤呼吸速率、干物质积累、植株氮磷钾积累和产量的影响。结果表明,深松(耕)可以改善土壤孔隙特性,增加土壤孔隙度、孔隙体积和大孔隙数量。而秋季深耕+夏季深松对土壤耕层改良的效果优于单一秋季深松(耕),其冬小麦物质积累、养分吸收和产量均高于秋季深松(耕)+夏季免耕。5个处理中,以秋季深耕+夏季侧位深松对土壤耕层的改良效果最好,植株干物质积累量、养分积累量和产量最高。与秋季旋耕+夏季免耕相比,秋季深耕+夏季侧位深松土壤含水量和土壤呼吸速率分别增加11.3%和35.4%,冬小麦干物质积累量、植株氮磷钾积累分别增加28.2%,62.3%,67.8%和63.8%,产量提高27.8%。因此,秋季深耕+夏季侧位深松是砂姜黑土区适宜的土壤耕作制度。     

温度对黄斑长翅卷叶蛾生长发育及繁殖的影响

为明确温度对黄斑长翅卷叶蛾生长发育及繁殖的影响,测定了5个恒温(19、22、25、28和31℃)条件下黄斑长翅卷叶蛾各虫态的发育历期、卵孵化率、存活率、产卵量、成虫寿命等指标,计算出各个虫态的发育起点温度和有效积温。结果表明:在19～31℃范围内,各虫态发育历期随着温度的升高而逐渐缩短。卵、幼虫、蛹、产卵前期和全世代的发育起点温度分别为(9.58±0.55)、(10.24±0.85)、(15.82±1.22)、(10.25±1.24)和(10.65±0.85)℃,有效积温分别为163.35、455.68、186.54、15.55和821.12 d·℃。在22、25和28℃下,该虫的产卵量、卵孵化率以及幼虫和蛹存活率明显高于其它温度,成虫寿命和产卵期随温度升高逐渐缩短。总体上,22～28℃是该虫较为理想的生长发育和繁殖温度范围,研究结果为开展黄斑长翅卷叶蛾预测预报和综合防控提供了参考依据。     

林分空间结构参数N元分布及其诠释——以小陇山锐齿栎天然混交林为例

【目的】以甘肃省小陇山锐齿栎天然混交林为例,采用林分空间结构参数N元分布全面、系统地揭示林分空间结构特征,以实现森林结构信息的精确描述和直观表达,为森林结构精准调控和重建提供基础参考信息。【方法】对70 m×70 m标准地内的林木进行每木定位和调查,采用Winkelmass计算每株林木的混交度(M)、角尺度(W)、大小比数(U)和密集度(C),借助Excel透视表统计N元分布相对频率,并采用R 3.4.3、Origin 2015绘制N元分布图。【结果】锐齿栎天然混交林整体及林分内大多数林木呈随机分布、混交良好、较为密集;林分整体中庸且各大小比数等级林木均接近20%;二元分布、三元分布和四元分布中该林分结构最突出特点表现为:不管结构参数如何组合,林分均表现出不同结构组合下大多数林木呈随机分布或混交良好。【结论】结构参数N元分布借助分布频率表达优势从不同层次和角度全面系统地描述了林分结构特征信息,且不同分布之间优势互补,实现了林分结构从"点→线→面→体→超体"的精准详尽解译;双X横坐标或双Y纵坐标的3D图满足了多元分布结构信息直观展现需求;N元分布为森林结构精准调控和林分结构重建提供了先决信息。