An unsupervised two-stage clustering approach for forest structure classification based on X-band InSAR data — A case study in complex temperate forest stands     

《International Journal of Applied Earth Observation and Geoinformation》2017

Forest structure at stand level plays a key role for sustainable forest management, since the biodiversity, productivity, growth and stability of the forest can be positively influenced by managing its structural diversity. In contrast to field-based measurements, remote sensing techniques offer a cost-efficient opportunity to collect area-wide information about forest stand structure with high spatial and temporal resolution. Especially Interferometric Synthetic Aperture Radar (InSAR), which facilitates worldwide acquisition of 3d information independent from weather conditions and illumination, is convenient to capture forest stand structure. This study purposes an unsupervised two-stage clustering approach for forest structure classification based on height information derived from interferometric X-band SAR data which was performed in complex temperate forest stands of Traunstein forest (South Germany). In particular, a four dimensional input data set composed of first-order height statistics was non-linearly projected on a two-dimensional Self-Organizing Map, spatially ordered according to similarity (based on the Euclidean distance) in the first stage and classified using the k-means algorithm in the second stage. The study demonstrated that X-band InSAR data exhibits considerable capabilities for forest structure classification. Moreover, the unsupervised classification approach achieved meaningful and reasonable results by means of comparison to aerial imagery and LiDAR data.  相似文献   

Homogeneous region determination using linear and nonlinear techniques   总被引：1，自引：0，他引：1  

Janaki Ballav Swain  Mrunmayee Manjari Sahoo  Kanhu Charan Patra 《自然地理学》2016,37(5):361-384

Similarity between catchments in a region can be determined depending on catchment properties. This helps to understand the response behavior of the similar catchments more appropriately. Catchment classification plays a major role in the process of hydrological prediction in the case of ungauged catchments. The following categorization was carried out for 32 catchments of India. Principal Component Analysis (PCA) along with K-means clustering, was applied as linear classification; and Self-Organizing Map (SOM) and Kernel Principal Component Analysis (KPCA) were implemented as nonlinear classification methods on catchment attributes and daily streamflow time series. The classification established on streamflow signatures was taken as the reference classification. Results obtained from PCA, SOM, and KPCA were compared with results of the reference classification. The absence of discordant catchments from the clusters of SOM, based on catchment attributes, suggests homogeneity among SOM-derived clusters. Similarity index scores are 0.48 and 0.47, 0.46 and 0.42 ?for first, second, third and fourth clusters of SOM respectively with that of the reference classification technique. Nonlinear techniques with high similarity index values outperformed standard techniques. This study demonstrated the ability of classification based on catchment attributes to classify ungauged catchments.  相似文献   

Carbon and nitrogen pools in Padanian soils (Italy): Origin and dynamics of soil organic matter     

C. Natali  G. Bianchini  L. Vittori Antisari  M. Natale  U. Tessari 《Chemie der Erde / Geochemistry》2018,78(4):490-499

Carbon and nitrogen elemental (C-N, wt%) and isotopic (δ¹³C-δ¹⁵N, ‰) investigation has been carried out on alluvial and deltaic soils from the Padanian plain (northern Italy), an area interested by intensive agricultural activities, to refine previous inferences on depositional facies, pedogenetic processes and anthropogenic influences. Soil analysis, carried out by EA-IRMS, have been focused on inorganic and organic fractions properly speciated by a thermally-based method, whereas further insights on the organic matter constituents have been obtained by sequential fractionation. The bulk EA-IRMS analyses reveal a remarkable compositional heterogeneity of the investigated soils (TC 0.89 to 11.93?wt%, TN 0.01 to 0.78?wt%, δ¹³C_TC -1.2 to -28.2‰, δ¹⁵N -1.2 to 10.0‰) that has to be explained as an integration between inorganic and organic pools. The latter have been subdivided in Non-Extractable Organic Matter (NEOM, δ¹³C -16.3 to -28.6‰) and in extractable fractions as Fulvic (FA, δ¹³C -24.7 to -27.5‰, δ¹⁵N 0.6 to 5.7‰) and Humic (HA, δ¹³C -24.6 to -27.0‰, δ¹⁵N 1.0 to 9.7‰) Acids, which have been used to infer soil dynamics and Soil Organic Matter (SOM) stability processes. Results indicate that SOM at depth of 100?cm was generally affected by microbial reworking, with the exception of clayey and peaty deposits in which biological activity seems inhibited. Peaty and clayey soils display an organic fraction loss of ca. 20% toward the surface, suggesting deterioration possibly induced by intensive agricultural activities. These latter may be the cause of the ubiquitous losses of organic fraction throughout the investigated area over the last seventy years, evaluated by the comparison with historical data on corresponding topsoils. The obtained insights are very important because these soils are carbon (and nitrogen) sinks that are vulnerable and can be degraded, loosing agricultural productivity and potentially contributing to greenhouse gases fluxes.  相似文献   

基于SOM神经网络的城市土地覆盖遥感分类研究   总被引：1，自引：0，他引：1  

刘艳杰  曾永年 《测绘与空间地理信息》2012,(6):42-45,48

土地覆盖及其变化的研究作为区域及全球环境变化研究所需的极为重要的地表参数,是遥感应用分析的主要内容之一。以往所采用的遥感分类方法主要针对侧重于土地社会属性的土地利用类型的分类研究且很难获得理想的精度。本文在非监督的自组织映射神经网络的基础上进行了一定的改进,构建了有监督的神经网络模型,以湖南省长沙市主城区的土地自然属性为侧重点,对其土地覆盖进行分类。实验结果表明:利用本文所使用的方法得到的分类结果,其总体精度和Kappa系数均高于传统的分类方法得出来的分类结果。  相似文献