Image Spectral and Spatial Information in the Assessment of Forest Structural and Biophysical Data
نویسنده
چکیده
Remote sensing instruments generally regularize a continuous ground surface into a grid of similarly sized and shaped pixels. This regularization results in similarity of neighboring pixels which represent the same objects on the ground. The above ground organization of vegetation in a forest is considered the forest structure. At a high spatial resolution, at approximately 1m, the spatial information that is created though this discretization represents the forest structure. This image spatial information may be applied to improve the accuracy of estimates of forest biophysical and structural parameters from remotely sensed data. In this paper the relationship between image spatial structure and forest structural information is presented. To demonstrate this relationship, examples of the estimation of forest structural parameters from the spatial and spectral information collected with an airborne imaging spectrometer (CASI) at a spatial resolution of 1m are presented.
منابع مشابه
Spectral-spatial classification of hyperspectral images by combining hierarchical and marker-based Minimum Spanning Forest algorithms
Many researches have demonstrated that the spatial information can play an important role in the classification of hyperspectral imagery. This study proposes a modified spectral–spatial classification approach for improving the spectral–spatial classification of hyperspectral images. In the proposed method ten spatial/texture features, using mean, standard deviation, contrast, homogeneity, corr...
متن کامل3D Classification of Urban Features Based on Integration of Structural and Spectral Information from UAV Imagery
Three-dimensional classification of urban features is one of the important tools for urban management and the basis of many analyzes in photogrammetry and remote sensing. Therefore, it is applied in many applications such as planning, urban management and disaster management. In this study, dense point clouds extracted from dense image matching is applied for classification in urban areas. Appl...
متن کاملVHR Semantic Labeling by Random Forest Classification and Fusion of Spectral and Spatial Features on Google Earth Engine
Semantic labeling is an active field in remote sensing applications. Although handling high detailed objects in Very High Resolution (VHR) optical image and VHR Digital Surface Model (DSM) is a challenging task, it can improve the accuracy of semantic labeling methods. In this paper, a semantic labeling method is proposed by fusion of optical and normalized DSM data. Spectral and spatial featur...
متن کاملSPOT-5 Spectral and Textural Data Fusion for Forest Mean Age and Height Estimation
Precise estimation of the forest structural parameters supports decision makers for sustainable management of the forests. Moreover, timber volume estimation and consequently the economic value of a forest can be derived based on the structural parameter quantization. Mean age and height of the trees are two important parameters for estimating the productivity of the plantations. This research ...
متن کاملاستفاده از تبدیل PCA مکانی جهت ادغام تصاویر چند طیفی و تک رنگ
Obtaining of an image with high spectral and spatial resolution is the goal of image fusion. The PCA is a well-known pan-sharpening approach widely used for its efficiency and high spatial resolution. However, it can distort the spectral characteristics of the multispectral images. To avoid the weak points of the standard PCA technique, Spatial PCA transform has been proposed and the reasons of...
متن کامل