Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery

This paper presents an application of Airborne Laser Scanning (ALS) data in conjunction with an IRS LISS-III image for mapping forest fuel types. For two study areas of 165 km² and 487 km² in Sicily (Italy), 16,761 plots of size 30-m × 30-m were distributed using a tessellation-based stratified sampling scheme. ALS metrics and spectral signatures from IRS extracted for each plot were used as predictors to classify forest fuel types observed and identified by photointerpretation and fieldwork. Following use of traditional parametric methods that produced unsatisfactory results, three non-parametric classification approaches were tested: (i) classification and regression tree (CART), (ii) the CART bagging method called Random Forests, and (iii) the CART bagging/boosting stochastic gradient boosting (SGB) approach. This contribution summarizes previous experiences using ALS data for estimating forest variables useful for fire management in general and for fuel type mapping, in particular. It summarizes characteristics of classification and regression trees, presents the pre-processing operation, the classification algorithms, and the achieved results. The results demonstrated superiority of the SGB method with overall accuracy of 84%. The most relevant ALS metric was canopy cover, defined as the percent of non-ground returns. Other relevant metrics included the spectral information from IRS and several other ALS metrics such as percentiles of the height distribution, the mean height of all returns, and the number of returns.     

Evaluation of environmental parameters in logistic regression models for landslide susceptibility mapping

Abstract

The aim of this study was to determine how well the landslide susceptibility parameters, obtained by data-dependent statistical models, matched with the parameters used in the literature. In order to achieve this goal, 20 different environmental parameters were mapped in a well-studied landslide-prone area, the Asarsuyu catchment in northwest Turkey. A total of 4400 seed cells were generated from 47 different landslides and merged with different attributes of 20 different environmental causative variables into a database. In order to run a series of logistic regression models, different random landslide-free sample sets were produced and combined with seed cells. Different susceptibility maps were created with an average success rate of nearly 80%. The coherence among the models showed spatial correlations greater than 90%. Models converged in the parameter selection peculiarly, in that the same nine of 20 were chosen by different logistic regression models. Among these nine parameters, lithology, geological structure (distance/density), landcover-landuse, and slope angle were common parameters selected by both the regression models and literature. Accuracy assessment of the logistic models was assessed by absolute methods. All models were field checked with the landslides resulting from the 12 November 1999, Kayna?li Earthquake (Ms = 7.2).     

基于整体最小二乘法的线性回归建模和解法

对基于自变量和因变量误差的回归问题进行了进一步研究,证明了两种方法的实质并未解决同时考虑自变量和因变量的误差问题,其解算结果和不考虑自变量误差的解算结果完全相同。给出了能同时顾及自变量和因变量误差的新的回归模型,并推导了具体的解算方法。算例结果和基于矩阵分解的整体最小二乘法解算方法的结果相同,说明了本文方法的正确性。     

POS数据的上下视差误差源检测及误差补偿回归模型

分析POS数据存在系统误差的主要原因,推导基于POS的立体像对相对定向模型,建立POS系统误差回归补偿模型。三个不同比例尺测区试验数据的结果表明：POS系统提供的外方位元素中角元素误差是引起模型上下视差的主要误差源;利用回归补偿模型对POS数据进行改正后模型上下视差明显减小;在无需区域网平差的情况下,直接通过相对定向消除POS系统误差,定位精度有较大提高,从而证明该补偿模型的正确性与可行性。     

方向数据插值方法研究

方向数据是矢量数据的标量表示,通常标量数据的插值方法不适用于方向数据。本文根据方向数据的特征,把其分解成沿坐标轴的分量,通过对分量数据的插值形成表面,最后合成反算出方向数据,给出了一种对于方向数据的行之有效的插值方法;并用实验进行了验证,对其插值结果和精度进行了统计和分析,取得了较为理想的内插结果。     

Ordered weighted averaging with fuzzy quantifiers: GIS-based multicriteria evaluation for land-use suitability analysis

The objective of this paper is to incorporate the concept of fuzzy (linguistic) quantifiers into the GIS-based land suitability analysis via ordered weighted averaging (OWA). OWA is a multicriteria evaluation procedure (or combination operator). The nature of the OWA procedure depends on some parameters, which can be specified by means of fuzzy (linguistic) quantifiers. By changing the parameters, OWA can generate a wide range of decision strategies or scenarios. The quantifier-guided OWA procedure is illustrated using land-use suitability analysis in a region of Mexico.     

半参数模型稳健估计及其应用

在半参数模型估计中,均假设观测误差服从正态分布。当观测量含有粗差时,粗差对参数和非参数估计的影响是不可忽略。基于此,首先在总结线性参数模型稳健估计基本理论的基础上,论述了M估计权因子的确定方法。然后提出了半参数模型稳健估计方法,并导出半参数模型(广义)补偿最小二乘稳健估计的基本公式。最后通过两个模拟算例验证了其估计方法的有效性。     

顾及空间非平稳特征的遥感干旱监测

遥感技术具备实时快速、时空连续、广覆盖尺度等独特优势,在全球气候恶化大背景下,利用遥感干旱监测方法相比于传统地面监测手段,能够提供实时、准确、稳定的旱情信息,辅助科学决策。目前常用遥感旱情监测方法大多依赖全域性数学模型建模,假定了旱情模式的空间平稳特性,因而难以准确反映旱情模式的局部差异特征。本文提出利用地理加权回归模型GWR (Geographically Weighted Regression),考虑旱情模式的空间非平稳特性,综合多种遥感地面旱情监测指数,以实现传统全域旱情监测模型的局部优化。以美国大陆为研究区,监测2002年—2011年共10年的旱情状态。研究表明,GWR模型能够提供空间变化的局部最佳估计模型参数,监测结果更加吻合标准美国旱情监测USDM (U.S Drought Monitor)验证数据,且与地面实测值的最高相关系数R达到0.8552,均方根误差RMSE达到0.972,显著优于其他遥感旱情监测模型。GWR模型具备空间非平稳探测优势,实现了旱情模式的局部精细探测,能够显著提升遥感旱情监测精度,具备较好的应用前景。     

Geospatial data mining for digital raster mapping

We performed an in-depth literature survey to identify the most popular data mining approaches that have been applied for raster mapping of ecological parameters through the use of Geographic Information Systems (GIS) and remotely sensed data. Popular data mining approaches included decision trees or “data mining” trees which consist of regression and classification trees, random forests, neural networks, and support vector machines. The advantages of each data mining approach as well as approaches to avoid overfitting are subsequently discussed. We also provide suggestions and examples for the mapping of problematic variables or classes, future or historical projections, and avoidance of model bias. Finally, we address the separate issues of parallel processing, error mapping, and incorporation of “no data” values into modeling processes. Given the improved availability of digital spatial products and remote sensing products, data mining approaches combined with parallel processing potentials should greatly improve the quality and extent of ecological datasets.     

GPS高程拟合方法的研究

本文在76个拥有GPS大地高和正常高的已知的公共点作为原始数据的基础上,采用二次曲面拟合法、二次曲面加权法、二次曲面多面函数法、二次曲面最小二乘法来建立研究区的似大地水准面,通过实例发现,二次曲面加权法、二次曲面多面函数法、二次曲面最小二乘配置法与二次曲面拟合法进行比较,外检验点中误差的精度得到提高,并且二次曲面拟合所得到的残差值比较大的点精度有明显的提高,同时改善了模型,证明这种组合方法在GPS水准拟合方面有效、可行。