一种高郁闭度林分树高的估测方法

针对单一无人机影像无法有效地提取高郁闭度林分树高的问题,该文提出一种结合无人机影像数据和全站仪测量的地形数据获取高郁闭度林分树高的方法。首先,利用搭载数码相机的小型无人机平台,以50 m航高获取实验区局部高精度林分影像,利用全站仪获取实验区的地形数据。然后,利用无人机影像处理软件对影像进行处理,通过初步的几何校正以及空三加密过程得到整个实验区的高分辨率DEM和DOM模型;接下来,采用局部最大值算法探测单株林木的树冠中心点坐标,利用自然生长算法和高程差值公式得到树冠中心对应的树根高程;最后,以树冠中心点高程以及树根高程的差值作为单木树高的估计值。通过实验得出:结合无人机影像与全站仪数据能够准确快速地获取高郁闭度林分树高,本文提出的方法可以为森林可持续经营提供数据基础。     

一种高郁闭度林分树高的估测方法

针对单一无人机影像无法有效地提取高郁闭度林分树高的问题,该文提出一种结合无人机影像数据和全站仪测量的地形数据获取高郁闭度林分树高的方法。首先,利用搭载数码相机的小型无人机平台,以50 m航高获取实验区局部高精度林分影像,利用全站仪获取实验区的地形数据。然后,利用无人机影像处理软件对影像进行处理,通过初步的几何校正以及空三加密过程得到整个实验区的高分辨率DEM和DOM模型;接下来,采用局部最大值算法探测单株林木的树冠中心点坐标,利用自然生长算法和高程差值公式得到树冠中心对应的树根高程;最后,以树冠中心点高程以及树根高程的差值作为单木树高的估计值。通过实验得出:结合无人机影像与全站仪数据能够准确快速地获取高郁闭度林分树高,本文提出的方法可以为森林可持续经营提供数据基础。     

无人机航测技术在森林蓄积量估测中的应用

无人机（UAV）航测技术是近年来发展起来的快速获取高分辨率影像的测绘新技术。森林蓄积量估算需要快速高效地获取森林遥感影像。虽然利用卫星和机载雷达同样可获取高分辨率遥感影像,但无人机航测技术与其相比具有飞行成本低、外业周期短、机动灵活等优点。本文利用无人机航测系统获取了案例地区DSM和DEM,采用最大邻域法提取了树高,采用分水岭算法分割了树冠信息,并以树高和冠幅作为解释变量的立木材积二元模型估算了森林蓄积量。结果表明,树高提取精度为83.73%,冠幅提取精度为86.98%,林分蓄积量估算精度为81.80%。     

超分辨率学习算法在海岸地形航空摄影测量中的应用

针对海岸地形航空摄影遥感影像分辨率低导致测量测量低问题,研究超分辨率学习算法在海岸地形航空摄影测量中的应用。基于无人机海岸地形航测遥感影像数据,利用超分辨率学习算法对影像数据进行重建获得高分辨率海岸地形遥感影像;通过最大类间方差法对遥感影像进行分类,并采用反距离加权值法进行匹配,获得海岸地形地理信息。实验结果表明：相比传统方法,本文方法可以获取较高的分辨率遥感影像以及海岸地形航空摄影测量精度,遥感影像峰值信噪比(PSNR)指标平均值在19 dB以上,平面误差与高程误差均小于等于±1.50。     

基于无人机影像和高程数据的地形构造线确定方法

目前传统的地形数据定量分析在图层提取应用中,存在误差较大的问题,为此提出一种基于无人机影像和高程数据的地形构造线确定方法.通过无人机遥感系统对高程数据进行获取,利用图像灰度化、平滑处理、山轮廓提取以及转化为坡度数据,实现对地形高程数据的预处理.基于坡度数据提取山脊线,借助反地形思想提取山谷线,由此确定地形构造线.实验结果证明,设计方法在坡度为35.47°～51.47°时,图层提取误差低于现有方法,具有较优越的图层提取性能.     

无人机影像在地质灾害调查中的应用

汶川地震后,西南地区进入地质灾害高发期。面对高发的地质灾害,如何快速准确地获取地质灾害信息已经成为亟待解决的问题。文中选取映秀镇老虎嘴滑坡所在位置为实验区,采集实验区10km2分辨率0.3m的无人机影像。对获取的无人机影像进行数字摄影测量处理,生成实验区精度0.476m的正射影像,0.731m精度的点云数据以及实景三维模型。在这些数字成果的基础上,对老虎嘴滑坡进行定量分析。此次实验验证无人机在高海拔、高落差的地形环境下作业的可行性,探索定量研究地质灾害的方法。     

复杂地形条件下多密度级无人机LiDAR点云DEM精度研究 ——以2022年北京冬奥会延庆赛区为例

为进一步提升复杂地形条件下无人机激光雷达(Light Detection and Ranging,LiDAR)点云数据构建数字高程模型的效率与精度,以2022年北京冬奥会延庆赛区场馆建设用地为实验区,按照不同抽稀比例,对实验区原始无人机激光雷达点云中分类出的地面点数据进行抽稀处理,利用克里金插值算法对不同密度地面点数据进行插值处理,结合高程中误差、平均绝对误差对生成的数字高程模型进行双重精度评定,得出以下结论:对于复杂地形而言,随着点云数据密度的下降,数字高程模型建模效率明显提升,但地形特征逐渐模糊,数据精度级别逐级降低,其中高程中误差由0.381 m增大至1.914 m,平均绝对误差值由0.335 m增大至1.357 m.在满足精度要求的前提下,对LiDAR点云数据进行适度抽稀处理,可保障生产成本与时效.     

基于实景三维模型的城市1:500地形图测绘

利用六旋翼无人机航拍获取了实验区五镜头像片数据,应用倾斜摄影技术建设了实景三维模型,并基于模型开展了城市地形样本和村庄地形样本下的1:500地形图测绘,最后进行了精度评定和误差分析.结果表明,就图形精度而言,两类地形条件下的图形平面精度和高程精度均满足规范要求,但比较城市地形图和村庄地形图,前者平面精度和高程精度均略低于后者.基于实景三维模型的1:500地形图测绘可在城市地区开展,较之传统的全站仪或GPS RTK数字测图具有更快的生产速度和更高的生产效率.     

无人机技术在露天土石挖填方计算中的应用

传统的全站仪或RTK技术进行土石方的填挖计算时,外业数据采集需耗费较多的时间和人力成本,作业效率较低;而三维激光扫描仪、InSAR等技术通过获取点云数据构建DEM的方式计算土石方量,效率较高,但成本也相对较高,因此拟研究利用消费级无人机进行外业地形数据的快速采集,并进行露天土石挖填方计算的方法。首先通过低空无人机获取影像,并利用少量像控点,根据计算机视觉原理生成地形稠密三维点云;然后利用密集点云数据生成DEM,利用影像生成DOM;最后结合DOM与DEM得到露天土石挖填方量。为了验证无人机制作生成的DEM计算土石挖填方的精度,选择有起伏的地形进行实验,并与传统RTK方法计算的土石方量进行精度对比。结果表明,利用无人机构建的露天地形DEM计算的挖填石土方量与传统RTK方法的误差在3%以内,能满足工程中土石方量计算要求,且作业成本较低、效率较高,具有一定的工程实用价值。     

利用无人机影像制作地震灾区三维景观图

为实时、准确地获取地震灾区的灾情和灾后重建状况,研究如何利用高分辨率无人机影像制作灾区三维景观图。对无人机影像进行几何纠正、影像拼接,利用摄影测量方法生成实验区的DEM,进而将影像制作成正射影像图;将无人机影像纹理映射到DEM上构建灾区的三维地形景观,并以正射影像图为底图对安置区的地物进行三维建模;最后根据规划和管理需要,编制三维景观系统,实现地震灾区三维景观的浏览、查询与分析。实践表明,采用无人机影像制作的三维景观图具有分辨率高、形象逼真等特点;影像的获取和处理以及系统的编制可为灾区重建提供丰富详实的信息。     

Estimate of heavy metals in soil and streams using combined geochemistry and field spectroscopy in Wan-sheng mining area,Chongqing, China

Heavy metals contaminated soils and water will become a major environmental issue in the mining areas. This paper intends to use field hyper-spectra to estimate the heavy metals in the soil and water in Wan-sheng mining area in Chongqing. With analyzing the spectra of soil and water, the spectral features deriving from the spectral of the soils and water can be found to build the models between these features and the contents of Al, Cu and Cr in the soil and water by using the Stepwise Multiple Linear Regression (SMLR). The spectral features of Al are: 480 nm, 500 nm, 565 nm, 610 nm, 680 nm, 750 nm, 1000 nm, 1430 nm, 1755 nm, 1887 nm, 1920 nm, 1950 nm, 2210 nm, 2260 nm; The spectral features of Cu are: 480 nm, 500 nm, 610 nm, 750 nm, 860 nm, 1300 nm, 1430 nm, 1920 nm, 2150 nm, 2260 nm; And the spectral features of Cr are: 480 nm, 500 nm, 610 nm, 715 nm, 750 nm, 860 nm, 1300 nm, 1430 nm, 1755 nm, 1920 nm, 1950 nm. With these features, the best models to estimate the heavy metals in the study area were built according to the maximal R². The R² of the models of estimating Al, Cu and Cr in the soil and water are 0.813, 0.638, 0.604 and 0.742, 0.584, 0.513 respectively. And the gradient maps of these three types of heavy metals’ concentrations can be created by using the Inverse distance weighted (IDW).The gradient maps indicate that the heavy metals in the soil have similar patterns, but in the North-west of the streams in the study area, the contents are of great differences. These results show that it is feasible to predict contaminated heavy metals in the soils and streams due to mining activities by using the rapid and cost-effective field spectroscopy.     

Determination of the factors governing soil erodibility using hyperspectral visible and near-infrared reflectance spectroscopy

Soil erodibility, which is difficult to estimate and upscaling, was determined in this study using multiple spectral models of soil properties (soil organic matter (SOM), water-stable aggregates (WSA) > 0.25 mm, the geometric mean radius (D_g)). Herein, the soil erodibility indicators were calculated, and soil properties were quantitatively analyzed based on laboratory simulation experiments involving two selected contrasting soils. In addition, continuous wavelet transformation was applied to the reflectance spectra (350–2500 nm) of 65 soil samples from the study area. To build the relationship, the soil properties that control erodibility were identified prior to the spectral analysis. In this study, the SOM, D_g and WSA >0.25 mm were selected to represent the most significant soil properties controlling erodibility and describe the erodibility indicator based on a logarithmic regression model as a function of SOM or WSA > 0.25 mm. Five, six and three wavelet features were observed to calibrate the estimated soil properties model, and the best performance was obtained with a combination feature regression model for SOM (R² = 0.86, p < 0.01), D_g (R² = 0.79, p < 0.01) and WSA >0.25 mm (R² = 0.61, p < 0.01), respectively. One part of the wavelet features captured amplitude variations in the broad shape of the reflectance spectra, and another part captured variations in the shape and depth of the soil dry substances. The wavelet features for the validated dataset used to predict the SOM, WSA >0.25 mm and D_g were not significantly different compared with the calibrated dataset. The synthesized spectral models of soil properties, and the formation of a new equation for soil erodibility transformed from the spectral models of soil properties are presented in this study. These results show that a spectral analytical approach can be applied to complex datasets and provide new insights into emerging dynamic variation with erodibility estimation.     

Determination of the crop row orientations from Formosat-2 multi-temporal and panchromatic images

This paper presents a technique developed for the retrieval of the orientation of crop rows, over anthropic lands dedicated to agriculture in order to further improve estimate of crop production and soil erosion management. Five crop types are considered: wheat, barley, rapeseed, sunflower, corn and hemp. The study is part of the multi-sensor crop-monitoring experiment, conducted in 2010 throughout the agricultural season (MCM’10) over an area located in southwestern France, near Toulouse. The proposed methodology is based on the use of satellite images acquired by Formosat-2, at high spatial resolution in panchromatic and multispectral modes (with spatial resolution of 2 and 8 m, respectively). Orientations are derived and evaluated for each image and for each plot, using directional spatial filters (45° and 135°) and mathematical morphology algorithms. “Single-date” and “multi-temporal” approaches are considered. The single-date analyses confirm the good performances of the proposed method, but emphasize the limitation of the approach for estimating the crop row orientation over the whole landscape with only one date. The multi-date analyses allow (1) determining the most suitable agricultural period for the detection of the row orientations, and (2) extending the estimation to the entire footprint of the study area. For the winter crops (wheat, barley and rapeseed), best results are obtained with images acquired just after harvest, when surfaces are covered by stubbles or during the period of deep tillage (0.27 > R² > 0.99 and 7.15° > RMSE > 43.02°). For the summer crops (sunflower, corn and hemp), results are strongly crop and date dependents (0 > R² > 0.96, 10.22° > RMSE > 80°), with a well-marked impact of flowering, irrigation equipment and/or maximum crop development. Last, the extent of the method to the whole studied zone allows mapping 90% of the crop row orientations (more than 45,000 ha) with an error inferior to 40°, associated to a confidence index ranging from 1 to 5 for each agricultural plot.     

Computer algebra solution of the GPS N-points problem

A computer algebra solution is applied here to develop and evaluate algorithms for solving the basic GPS navigation problem: finding a point position using four or more pseudoranges at one epoch (the GPS N-points problem). Using Mathematica 5.2 software, the GPS N-points problem is solved numerically, symbolically, semi-symbolically, and with Gauss–Jacobi, on a work station. For the case of N > 4, two minimization approaches based on residuals and distance norms are evaluated for the direct numerical solution and their computational duration is compared. For N = 4, it is demonstrated that the symbolic computation is twice as fast as the iterative direct numerical method. For N = 6, the direct numerical solution is twice as fast as the semi-symbolic, with the residual minimization requiring less computation time compared to the minimization of the distance norm. Gauss–Jacobi requires eight times more computation time than the direct numerical solution. It does, however, have the advantage of diagnosing poor satellite geometry and outliers. Besides offering a complete evaluation of these algorithms, we have developed Mathematica 5.2 code (a notebook file) for these algorithms (i.e., Sturmfel’s resultant, Dixon’s resultants, Groebner basis, reduced Groebner basis and Gauss–Jacobi). These are accessible to any geodesist, geophysicist, or geoinformation scientist via the GPS Toolbox () website or the Wolfram Information Center ().

On Definition of Geograghic Variable Scaling

IntroductionThe map is a basic form of geographic informationvisualization[1]. To provide space attributes or geo-graphic orders is the basic function of a map. Incartography, according to the different measure ofphenomenal quantitative attribute, four fo…     

Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat

Hyperspectral sensing can provide an effective means for fast and non-destructive estimation of leaf nitrogen (N) status in crop plants. The objectives of this study were to design a new method to extract hyperspectral spectrum information, to explore sensitive spectral bands, suitable bandwidth and best vegetation indices based on precise analysis of ground-based hyperspectral information, and to develop regression models for estimating leaf N accumulation per unit soil area (LNA, g N m⁻²) in winter wheat (Triticum aestivum L.). Three field experiments were conducted with different N rates and cultivar types in three consecutive growing seasons, and time-course measurements were taken on canopy hyperspectral reflectance and LNA under the various treatments. Then, normalized difference spectral indices (NDSI) and ratio spectral indices (RSI) based on the original spectrum and the first derivative spectrum were constructed within the range of 350–2500 nm, and their relationships with LNA were quantified. The results showed that both LNA and canopy hyperspectral reflectance in wheat changed with varied N rates, with consistent patterns across different cultivars and seasons. The sensitive spectral bands for LNA existed mainly within visible and near infrared regions. The best spectral indices for estimating LNA in wheat were found to be NDSI (R₈₆₀, R₇₂₀), RSI (R₉₉₀, R₇₂₀), NDSI (FD₇₃₆, FD₅₂₆) and RSI (FD₇₂₅, FD₅₁₆), and the regression models based on the above four spectral indices were formulated as Y = 26.34x^1.887, Y = 5.095x − 6.040, Y = 0.609 e^3.008x and Y = 0.388x^1.260, respectively, with R² greater than 0.81. Furthermore, expanding the bandwidth of NDSI (R₈₆₀, R₇₂₀) and RSI (R₉₉₀, R₇₂₀) from 1 nm to 100 nm at 1 nm interval produced the LNA monitoring models with similar performance within about 33 nm and 23 nm bandwidth, respectively, over which the statistical parameters of the models became less stable. From testing of the derived equations, the model for LNA estimation on NDSI (R₈₆₀, R₇₂₀), RSI (R₉₉₀, R₇₂₀), NDSI (FD₇₃₆, FD₅₂₆) and RSI (FD₇₂₅, FD₅₁₆) gave R² over 0.79 with more satisfactory performance than previously reported models and physical models in wheat. It can be concluded that the present hyperspectral parameters of NDSI (R₈₆₀, R₇₂₀), RSI (R₉₉₀, R₇₂₀), NDSI (FD₇₃₆, FD₅₂₆) and RSI (FD₇₂₅, FD₅₁₆) can be reliably used for estimating LNA in winter wheat.     

Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data

Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) has been used for the blending of Landsat and MODIS data. Specifically, the 30 m Landsat-7 ETM+ (Enhanced Thematic Mapper plus) surface reflectance was predicted for a period of 10 years (2000–2009) as the product of observed ETM+ and MODIS surface reflectance (MOD09A1) on the predicted and observed ETM+ dates. A pixel based analysis for six observed ETM+ dates covering winter and summer crops showed that the prediction method was more accurate for NIR band (mean r² = 0.71, p ≤ 0.01) compared to green band (mean r² = 0.53; p ≤ 0.01). A recently proposed chlorophyll index (CI), which involves NIR and green spectral bands, was used to retrieve gross primary productivity (GPP) as the product of CI and photosynthetic active radiation (PAR). The regression analysis of GPP derived from closet observed and synthetic ETM+ showed a good agreement (r² = 0.85, p ≤ 0.01 and r² = 0.86, p ≤ 0.01) for wheat and sugarcane crops, respectively. The difference between the GPP derived from synthetic and observed ETM+ (prediction residual) was compared with the difference in GPP values from observed ETM+ on the two dates (temporal residual). The prediction residuals (mean value of 1.97 g C/m² in 8 days) was found to be significantly lower than the temporal residuals (mean value of 4.46 g C/m² in 8 days) that correspondence to 12% and 27%, respectively, of GPP values (mean value of 16.53 g C/m² in 8 days) from observed ETM+ data, implying that the prediction method was better than temporal pixel substitution. Investigating the trend in synthetic ETM+ GPP values over a growing season revealed that phenological patterns were well captured for wheat and sugarcane crops. A direct comparison between the GPP values derived from MODIS and synthetic ETM+ data showed a good consistency of the temporal dynamics but a systematic error that can be read as bias (MODIS GPP over estimation). Further, the regression analysis between observed evapotranspiration and synthetic ETM+ GPP showed good agreement (r² = 0.66, p ≤ 0.01).     

A new calibration of the effective scattering albedo and soil roughness parameters in the SMOS SM retrieval algorithm

This study focuses on the calibration of the effective vegetation scattering albedo (ω) and surface soil roughness parameters (H_R, and N_Rp, p = H,V) in the Soil Moisture (SM) retrieval from L-band passive microwave observations using the L-band Microwave Emission of the Biosphere (L-MEB) model. In the current Soil Moisture and Ocean Salinity (SMOS) Level 2 (L2), v620, and Level 3 (L3), v300, SM retrieval algorithms, low vegetated areas are parameterized by ω = 0 and H_R = 0.1, whereas values of ω = 0.06 − 0.08 and H_R = 0.3 are used for forests. Several parameterizations of the vegetation and soil roughness parameters (ω, H_R and N_Rp, p = H,V) were tested in this study, treating SMOS SM retrievals as homogeneous over each pixel instead of retrieving SM over a representative fraction of the pixel, as implemented in the operational SMOS L2 and L3 algorithms. Globally-constant values of ω = 0.10, H_R = 0.4 and N_Rp = −1 (p = H,V) were found to yield SM retrievals that compared best with in situ SM data measured at many sites worldwide from the International Soil Moisture Network (ISMN). The calibration was repeated for collections of in situ sites classified in different land cover categories based on the International Geosphere-Biosphere Programme (IGBP) scheme. Depending on the IGBP land cover class, values of ω and H_R varied, respectively, in the range 0.08–0.12 and 0.1–0.5. A validation exercise based on in situ measurements confirmed that using either a global or an IGBP-based calibration, there was an improvement in the accuracy of the SM retrievals compared to the SMOS L3 SM product considering all statistical metrics (R = 0.61, bias = −0.019 m³ m⁻³, ubRMSE = 0.062 m³ m⁻³ for the IGBP-based calibration; against R = 0.54, bias = −0.034 m³ m⁻³ and ubRMSE = 0.070 m³ m⁻³ for the SMOS L3 SM product). This result is a key step in the calibration of the roughness and vegetation parameters in the operational SMOS retrieval algorithm. The approach presented here is the core of a new forthcoming SMOS optimized SM product.     

Nonlinear regional economic dynamics: continuous-time specification,estimation and stability analysis

This paper presents an innovative approach to the study of regional economic dynamics within a nonlinear continuous-time econometric framework—a generalized specification of the Lotka–Volterra system of equations. This specification, which accounts for interdependent behavior of three industrial sectors and spillover effects of activities in neighboring regions, is employed in an analysis of five Italian regions between 1980 and 2003. For these regions, we report estimation results, characterize the varying systems dynamics, analyze the models’ local and global stability properties, and determine via sensitivity analyses which structural features appear to exert the greatest influence on these properties.