Τετάρτη, 1 Μαΐου 2019

Remote Sensing

Quantitative Remote Sensing Analysis of the Geomorphological Development of the Lijiang River Basin, Southern China


The Lijiang River Basin is associated with a classic karst terrain characterized by conical hills that are referred to as karst towers. To date, few quantitative studies have been conducted on the geomorphological development of the river basin, in part, because it covers a large area of about 5800 km2, making it difficult to carry out a comprehensive quantitative analysis using traditional methods. This study using Landsat-8 OLI images and a digital elevation model firstly assesses the longitudinal profile of the Lijiang River, calculates the average bifurcation (bifurcation) ratio of the basin's drainage network, and conducts a hypsometric (elevation) analysis of the Lijiang Basin. The stage of geomorphological development is quantitatively constrained. It is concluded that the Lijiang River is currently in the old stage of geomorphological development, the stream systems in the Lijiang River Basin are in a relatively balanced developmental stage and the geomorphological development is dominated by the old-stage (monadnock) landforms.

Daily and Seasonal Pistachio Evapotranspiration in Saline Condition: Comparison of Satellite-Based and Ground-Based Results


This study was conducted in 2015 to estimate pistachio (Pistacia vera L.) water use in Marvast region, central Iran. Daily and seasonal pistachio actual evapotranspiration (ETa) was first estimated by running the surface energy balance algorithm for land (SEBAL) model, using 12 Landsat 8 satellite images and other ancillary data. Finally, SEBAL estimates of daily and seasonal water use over three experimental sites of pistachio orchards were compared against ones based on performing water balance analysis for the top 150 cm of the soil layer. For this purpose, a simple funnel-shaped device was made and used to collect the beneath rootzone drainage water and determine the leaching fraction (LF) as well as deep percolation at three scattered points across the sub-sections of each experimental site. The results reveal that the LF varies from 0.16 to 0.34, leading to deep percolation from 95.0 to 334.0 mm of the irrigation water. Eventually, the differences between ground-based and SEBAL-based pistachio ETa were 11% (ranged from 5.7 to 14.0%) and 10% (ranged from 7.1 to 16.4%) for daily and seasonal time scales, respectively. Based on the obtained results, more than 60% of Marvast pistachio orchards have seasonal water use of 410.0 to 680.0 mm (with the average of 594.3 mm), while the cumulative ETo and cumulative pistachio ETc of the same period were 1558.0 mm and 920.0 mm, respectively. In other words, Marvast pistachio trees extract less water from the soil compared with their potential water demands. This is due to the effect of salinity on reducing evapotranspiration rate as well as deficit irrigation of pistachio trees in the water scarce area of Marvast region. In any case, the difference between pistachio ETcand ETa is reflected in declination of the pistachio yield.

Relating ALOS-2 PALSAR-2 Parameters to Biomass and Structure of Temperate Broadleaf Hyrcanian Forests


Evaluation of forest biomass is required for sustainable forest management, efficiency valuation and exploring variations in carbon resources. In this research, we studied the possibility of polarimetric synthetic aperture radar (PolSAR) features in order to approximation of forest biomass in Hyrcanian forests. Our study sought to resolve the following inquiries: (1) Does the relevance between aboveground biomass (AGB) and SAR features depend on forest type and structure? (2) Does the use of polarimetric decomposition components elevate the saturation point of SAR response to biomass? (3) What are the most impressive texture parameters for mapping Hyrcanian vegetation biomass? For this purpose, we recorded 115 circular sample plots with 0.1 ha area in four sites, with various forest structures and biomass. Quad-pol PALSAR-2 data were used to apply decomposition methods and investigate the relationship between AGB and PolSAR attributes. To measure the efficiency of PolSAR data for biomass estimation, we used regression analysis, in which second-order and linear models were fit to forecast biomass per hectare, as defined from the field computations. Our results indicated that decomposition features have a high ability to enhance the saturation point and can produce more favorable outcomes than the backscatter coefficients for biomass estimation. Experimental results showed that the response of backscattering coefficients to biomass is affected by the forest type and canopy structure. These findings confirmed that the HH polarization backscatter is better suited for sparse areas, while HV polarization backscatter is qualified for dense areas.

Glacial Geomorphology and Landscape Evolution of the Thangu Valley, North Sikkim Himalaya, India


The present study describes glacial-geomorphological landforms in and around Thangu area, North Sikkim, India, and also provides significant insights about the evolution of pro- and paraglacial landscapes. Paraglacial processes are the governing mechanism of landscape evolution in deglaciating valleys and are now studied to understand the deglacial and postglacial landscape dynamics. We here present and describe detailed glacial geomorphology of Lashar and Chopta valleys as the area is strongly modified by the erosional and depositional imprints of late Pleistocene glaciations (MIS 2). Using geomorphological and stratigraphical methods, field surveys, SRTM DEM, Landsat ETM + and Google Earth Pro data, we have mapped glacial and glaciofluvial landforms and established moraine stratigraphy in the study area. Based on the morphostratigraphical mapping of the moraines supported by limited optical chronology, four events of glaciations have been identified in the in the Lashar, Chopta and Kalip valleys and date back to last glacial maximum and advocate for a widespread ice cover with large outlet tributary glaciers. The disposition of the lateral and terminal moraines has been used to estimate the area of paleoglacial extent and their corresponding ice volume during different stages. The presence of proglacial lakes, sensitive indicators of climate change, suggests that the glaciers in the region are melting and radically responding to global warming and are potentially vulnerable for generating glacial lake outburst floods.

Image Processing Techniques Applied to Satellite Data for Extracting Lineaments Using PCI Geomatica and Their Morphotectonic Interpretation in the Parts of Northwestern Himalayan Frontal Thrust


Lineament extraction technique is a very considerate method for the study of regional structural geology and tectonics. Lineaments are geomorphological features associated with fractures at a scale ranging from meters to tens of kilometer. In this work, an attempt of automatic extraction of lineaments and their morphotectonic interpretation has been made by satellite image processing in the NW part of HFT. Lineaments are auto-extracted from Landsat-8 OLI using PCI Geomatica software to achieve this objective edge enhancement, and filtering techniques were used. The methodology of this work enumerates the following steps: (1) pansharpening, (2) principal component analysis, (3) directional filtering, (4) lineament extraction, (5) density map generation, and (6) trend analysis. Extracted lineaments were draped over Cartosat DEM-derived ancillary products to understand the morphotectonic condition of the area. The orientation analysis of the extracted lineaments indicates that the major trend of linear features in the study area is NW–SE and parallel to the major structural trend (NNW–SSE). The trend of lineaments is in concordance with the direction of major faults, fractures, and associated geomorphological features. The lineament density value is relatively higher in the high relief area owing to the presence of fractured rocks to the structurally active terrain along the Siwalik range. The overlapping of lineament over geological map, slope aspect map, and drainage map confirms that some of the lineaments identified in the area are morphological features associated with fractures and faults. The study reveals that analysis of extracted lineament with the geological map and DEM gives a good interpretation of active morphotectonics in the parts of northwestern HFT.

Morphological and Chronological Mapping of Manilius Crater Region Using Chandrayaan-1 Data Sets


Fine-resolution morphological mapping aided by ortho-images and digital elevation model from Chandrayaan-1 Terrain Mapping Camera and 3D GIS visualization has revealed scientifically diverse characteristics of lunar surface features, due to unique topographical significance of morphological features, i.e., highlands, basaltic plains and craters, which are very well manifested in 3D GIS environment. The distribution of various morphological features provides insights into the sequential evolution and surface process of the study area. The highland region represented by the Fra Mauro formation in the study area exhibits high albedo with distinct topography. The northern part of the study area falls in the southern part of major basin Serenitatis, and exhibits the dark mantling material with low albedo. The morphological features, i.e., wrinkle ridges and rilles, indicate volcanic flow events consequence to the loading of basaltic materials in the interior of the Serenitatis and Imbrium Basins and related extensional failure. The Manilius crater, which occupies the central part of the study area, is a complex crater with a central peak and asymmetric ejecta deposit. The ages of the major surficial features were determined based on size, frequency and distribution pattern of craters using crater size-frequency distribution model. Age of the Fra Mauro highland, Manilius crater, Mare Serenitatis and Mare Vaporum is, respectively, 3.9, 3.5, 2.8 and 1.7 Ga years, indicating that the lunar surface of this region evolved in Imbrian to Eratosthenian age of lunar selenological timescale.

Remote Sensing Image Matching Based Improved ORB in NSCT Domain


Aiming at the problem that the ORB algorithm has no scale invariance and low matching accuracy in image matching, an improved ORB algorithm is proposed on the basis of SURF algorithm. Based on the flexibility of NSCT in image decomposition and the effectiveness of the improved ORB algorithm in remote sensing image matching, an improved ORB algorithm based on NSCT domain is proposed for remote sensing image matching. The image to be matched and the reference image are decomposed by NSCT. Two corresponding low-frequency images are obtained. Then, to reduce the influence of high-frequency noise on matching results, two low-frequency images are inputted to the improved ORB algorithm to obtain initial match results. The RANSAC algorithm is adopted to eliminate the mismatching points and complete the image matching. The experimental results show that the algorithm can make up the problem that the ORB algorithm has no scale invariance, and effectively improve the matching speed and accuracy of scale and rotation changes between two images. Meanwhile, the algorithm is more robust than classical methods in many complex situations such as image blur, field of view change, and noise interference.

A Novel Method for Segmentation and Change Detection of Satellite Images Using Proximal Splitting Algorithm and Multiclass SVM


This paper presents a novel method for segmentation and change detection of multispectral images using proximal splitting-based clustering and multiclass support vector machine (MSVM). Initially, the multitemporal satellite images are preprocessed and then textures are extracted using Difference of Offset Gaussian filter. In general, the traditional clustering method uses Euclidean distance as a prime factor for segmentation process. For multitextured images such as remotely sensed images, this metric provides inconsistent output. To achieve better segmented results, proximal splitting algorithm has been proposed. This method has been considered as a solution for iterative minimization problem, which is required to find exact changes between the multitemporal images. The MSVM is chosen to group the segmented clusters into a fixed number of classes, since the clusters obtained from the proximal splitting algorithm are not independent with each other. Then, the classified images are subjected to image differencing method to detect the changes. Experimentation is performed with two real data sets of Landsat7 images, which illustrates that the mean of difference in area obtained by the proposed method is reduced by an average of 35.24% compared to the conventional system. The validity index obtained for data set 1 using proposed algorithm is lower than the existing methods.

Sensitivity of Above-Ground Biomass to Terrestrial LIDAR-Derived Tree Height in Berkelah Tropical Rainforest, Malaysia


Tree height can be derived from airborne and terrestrial LIDAR in a nondestructive way. This study aims to analyze and investigate whether above-ground biomass (AGB) is sensitive or not to tree height derived from terrestrial LIDAR point cloud data in Berkelah tropical rainforest, Malaysia. To select the unit of analysis, a non-probability sampling of which purposive sampling approach was adopted. Accordingly, 32 sample plots were measured and scanned during the field data collection. Upper and lower canopy trees height was derived from airborne and terrestrial LIDAR, respectively. Moreover, terrestrial LIDAR was used to derive DBH of all upper and lower canopy trees. DBH measured in the field was used to validate the DBH manually derived from terrestrial laser scanner (TLS) point cloud data. To calculate AGB of both lower and upper canopy trees, the DBH derived from TLS point cloud data was used. The coefficient of determination R2 and RMSE of the DBH manually derived from TLS point cloud data validated by field measured DBH were 0.99 and 1.37 cm, respectively. This result revealed the existence of almost one to one relationship and based on the statistical test undertaken; there is no statistically significant difference between the two DBH measurements. Moreover, for sensitivity of AGB, when TLS tree height was validated by corresponding trees height from airborne LIDAR, 0.72 and 2.42 m were found for R2 and RMSE, respectively. Based on the findings, AGB is not sensitive to tree height derived from terrestrial LIDAR point cloud data.

Geospatial Target Detection from High-Resolution Remote-Sensing Images Based on PIIFD Descriptor and Salient Regions


Geospatial target detection from visible remote-sensing images is considered as one of the most important issues in the analysis of aerial and satellite imagery. Development of remote-sensing techniques and enhancing resolution of images provide an opportunity to advance automatic analysis. The proposed methods of geospatial target detection have faced to a variety of challenges. Recently, local features are extensively used which play a very effective role in dealing with these issues. High intensity variations between targets and backgrounds in different images are the most critical challenges. Most proposed local feature descriptors are not able to deal with this amount of intensity variations and are accompanied with errors when facing them. In this paper, PIIFD descriptor has been applied to cope with intense intensity variations, as this descriptor is symmetrical against contrast. The proposed framework to automatically detect geospatial targets includes a supervised approach based on local features extraction and description and consists of three main steps including training, image searching, and geospatial target detection. In the training step, local features are extracted by UR-SIFT algorithm that properly matches with remote-sensing images and are described by the PIIFD descriptor. Due to the large dimensions of the extracted features, the SABOVW model has been used for quantization purpose. This model uses soft assignment of features to codebook, and the presentation provided by this model is used to train SVM classifier. In the second step, for the sake of computational efficiency, the salient regions of the image are detected by combining the saliency models, which reduces the image space to search the geospatial targets. In the third step, the salient regions are scanned by the sliding window approach and a descriptor of each position is generated. Eventually, the process of detection of geospatial targets will be carried out by applying the trained SVM model to each window. In order to evaluate the efficiency of the PIIFD descriptor, its performance is compared with descriptors such as SIFT, DAISY, LSS, and LBP. The results showed better performance of the PIIFD descriptor in detection of geospatial targets.

Alexandros Sfakianakis
Anapafseos 5 . Agios Nikolaos

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