Изучение и картографирование почв южной части охотоморского сектора острова Кунашир, Курильские острова

Urban large-scale digital mapping demanded a greatly efficient and speedy surveying method. The traditional surveying process lagged behind because it had many shortcomings in mapping cost, mapping period, data storage and further usage. It is an effective way to use high spatial resolution remote sensing image to make large-scale urban maps. Firstly this paper discussed the relationship between mapping scale and image spatial resolution, analyzed the way of processing image about large-scale mapping, explained the data-abstracted method based on customized rules, intelligent symbols and automated topology. Secondly this paper expatiated on the core technology of geo-database based on large-scale digital maps and high spatial resolution image, argued how to establish and maintain a spatial database and how to serve the other different applications by the public interface. The remote sensing image, large-scale digital maps, intelligent symbols, map elements, map-framing indexes and social statistics data were stored into the same spatial database which further provided comprehensive data and the thematic maps for urban administration, urban plan, land management, digital city and Urban GIS. And then the spatio-temporal geo-database was established by the "versioning" which could manage different spatial and temporal data. So it was feasible to produce large-scale digital maps using high spatial resolution remote sensing image and construct spatio-temporal database using "versioning." Furthermore it is a necessary way to urban informational and intelligent administration.

Multiplexed Superresolution CRISPR Imaging of Chromatin in Living Cells

Lake surface water temperature (LSWT) is an important factor of water ecological environment. As global warming, LSWT is also on the rise. Research on the main reasons of LSWT rising is the basis for controlling and improving the regional ecological environment. However, it is difficult for the existing remote sensing images to take into account the temporal and spatial resolution. Low-resolution images have a serious impact on data accuracy and even produce incorrect results. Therefore, obtaining high temporal and spatial resolution images by downscaling is of great significance to more accurately analyze the temporal and spatial characteristics of LSWT. In this article, Dianchi Lake is selected as research area, and the high spatial resolution image (Landsat) and high temporal resolution image (MODIS) are taken as data. Based on the downscaling algorithm of statistics and learning, DisTrad– super-resolution convolutional neural network (SRCNN) downscaling model is proposed, and the monthly average dataset of LSWT with 50 m resolution is constructed. The results showed 1) DisTrad–SRCNN can reflect the most distribution characteristics of LSWT (SSIMday = 0.96, PSNRday = 23.97; SSIMnight = 0.95, PSNRnight = 24.99). 2) LSWT had an overall upward trend (CRday = 0.22 °C/10a, CRnight = 0.21 °C/10a), showing a cyclical change of cold–warm–cold about 4 years. 3) The northern and lakeshore area were basically in the high temperature, and the whole lake presents a 4–5-year warm–cold–warm periodic change; the LSWT closer to the urban and residential areas and its change rate were relatively high, which indirectly reflected the serious impact of human activities on LSWT.

By labelling high spatial resolution (HSR) images with specific semantic classes according to geographical properties, scene classification has been proven to be an effective method for HSR remote sensing image semantic interpretation. Deep learning is widely applied in HSR remote sensing scene classification. Most of the scene classification methods based on deep learning assume that the training datasets and the test datasets come from the same datasets or obey similar feature distributions. However, in practical application scenarios, it is difficult to guarantee this assumption. For new datasets, it is time-consuming and labor-intensive to repeat data annotation and network design. The neural architecture search (NAS) can automate the process of redesigning the baseline network. However, traditional NAS lacks the generalization ability to different settings and tasks. In this paper, a novel neural network search architecture framework—the spatial generalization neural architecture search (SGNAS) framework—is proposed. This model applies the NAS of spatial generalization to cross-domain scene classification of HSR images to bridge the domain gap. The proposed SGNAS can automatically search the architecture suitable for HSR image scene classification and possesses network design principles similar to the manually designed networks. To obtain a simple and low-dimensional search space, the traditional NAS search space was optimized and the human-the-loop method was used. To extend the optimized search space to different tasks, the search space was generalized. The experimental results demonstrate that the network searched by the SGNAS framework with good generalization ability displays its effectiveness for cross-domain scene classification of HSR images, both in accuracy and time efficiency.

This study aimed at obtaining factual information and overview to the development of land use patterns for buildings in urban areas by interval time period, both spatially and aspatially, by utilizing high-resolution satellite photo image (high resolution spatial image) combined with field observations. This research used survey method approach. The data of this study consisted of primary and secondary data classified into spatial and aspatial data in the form of time series obtained through documents recording techniques, field observations, previous mapping sources, as well as depth interviews. The analysis technique used Image Processing Analysis through programs and software Arc View. The result of research showed that there was a quite rapid development of land use patterns for building in Palu within the last 50 years (≤ 1970 till 2010) It had building addition in 65,173 units (82.28%), from 14,032 units in ≤1970 to 79,205 units in 2010, and the addition of extensive use of land for building was 4723.52 ha (89.06%), from 516.98 ha in ≤ 1970 to 4723.52 ha in 2010. The development level of land use patterns for building was getting along with the size of distribution and population growth in Palu.

To improve the efficiency of agricultural lands condition and use monitoring, method for detecting fallow lands overgrowth with tree and shrub vegetation using multi-temporal multispectral satellite images of high spatial resolution is proposed. The task of localization of overgrowth is solved on the basis of multi-temporal satellite images wavelet analysis. This technique allows to operationally and automatically detect areas of fallow lands overgrowth with tree and shrub vegetation. The proposed methodology was tested using satellite images obtained from Quickbird, GeoEye and WorldView satellites. For each image, a fourth-order wavelet decomposition was performed in terms of the Daubeshi wavelet functions. Comparing the different levels of the images wavelet transform, it is possible to divide the areas without tree and shrub vegetation, and where overgrowth was depicted. The method proposed by the authors for detecting changes based on wavelet analysis has shown that this method practically does not depend on the influence of shooting conditions and, accordingly, is more effective at detecting changes.

Facing-slope measurement from topographic maps was the traditional method used to divide sub-compartment for forest management division. However, the sub-compartment, which usually covers a large area and contains multiple forest types, can not be divided accurately and intuitively. Therefore it is not easy to operate in forestry management practice. An improved forest management planning method based on 3S technology with considering the reality of forest management was proposed in this paper. Firstly, forest function regions were distinguished based on digital topographic map. Secondly, the stands were divided in every sub-compartment based on high spatial resolution images by visual interpretation method in ArcGIS software. Thirdly, many neighboring sub-compartments with same geographical characteristics were combined into one operating area which was convenient for operating management forest. Then stands with the same management mode in one operating area were merged. Finally, compiling computer program to implement the aim of inputting some attribute information to generate other attribute information automatically. The approach put forward in this paper not only allows the forest management sophisticated, targeted but also easy to practice and implement.

With the application of Geographic entities for the expression of remote sensing information model, Analysis of remote sensing image was improved from pixels to the object layers. Combined with rules and reasoning mechanism, identification of geographical entities had been improved with intelligence from the image objects using more multiple factors. The traditional pixel-based methods that could not been effectively carried out information extraction on the high spatial resolution image was broken. A result of comparison was Provided about information extraction of remote sensing image in a mining area of Guangxi. The classification accuracy of two methods based on pixels and objects was evaluated. The difficulty of integration had been reduced between remote sensing information and GIS.

It has been reported and discussed that electrical current can be produced when an insulating material interacts with ionizing radiation. We have found that high-resolution images can be obtained from insulating materials if this current is guided by an electric field to the pixels of a TFT array. The charge production efficiency of insulators is much smaller than that of photoconductor materials such as selenium, silicon, or other conventional semiconductors. Nevertheless, when the intensity of the ionizing radiation is sufficiently high, a charge sensitive TFT imaging array with only dielectric material can produce high MTF images with contrast resolution proportional to the intensity of the radiation. The function of the dielectric in this new detector may be similar to that of an ionization chamber. Without the semiconductor charge generating material, the dielectric imaging detector does not exhibit charge generation fatigue or charge generation saturation. Prototype detectors have been tested using diagnostic x-ray beams with energy ranging from 25 kVp to 150 kVp, and therapeutic 2.5MV, 6MV, 10MV, and 15MV photon beams (with and without an electron built-up layer), electron beams, broad area proton beams, and proton pencil beams in the energy range of 150 MeV. High spatial resolution images up to the Nyquist frequency have been demonstrated. The physics, structure, and the imaging properties as well as the potential application of this detector will be presented and discussed.

Mixed pixels constitute AVHRR image. Evapotranspiration (ET) calculated from AVHRR image certainly includes some mistake information of ground objects which have no meaning as far as ET is concerned. Registration of AVHRR image and high spatial resolution image is carried out. A meteorology-RS combination model is used to calculate the ET of the AVHRR image and of the cropping area of the corresponding high resolution image respectively and the difference of these two ETs is given. The error is analyzed and a corresponding statistical calibration method adaptable to test region is given.

Downscaling is a particularly needed process in many environmental, social and governance applications at the fine scale. The need for an automated and reliable very high spatial resolution downscaling approach is then required. In this paper, a fully-automated open-access downscaling approach was proposed, named HSR-LST. It is based on the High Spatial Resolution (HSR) Red, Green and Blue (RGB) bands collected from commercial and free-to-access satellite images, generating LST values lower than 2-m spatial resolutions. This is based on the Landsat-8 thermal datasets and while implementing a fully-automated Ordinary Least Squares (OLS) approach. HSR-LST was implemented over Beirut, Boston and Dubai between 2016 and 2018. In comparison to an airborne LST image captured over ElKhorn River in Nebraska, USA, HSR-LST showed an AME of 0.88 °C and a R-squared value of 86.33%. Main results showed the variability of LST based on the sensed land features’ type. Different LST distribution footprints (i.e., irregular in Beirut, intermitted in Boston, systematic in Dubai) were highlighted depicting a characteristic urban configuration in each city. This latter along buildings’ material, density and height appear also to show a different effect on the local and surrounding LST values. By implementing the automated HSR-LST model in cities around the Globe, urban planners, policy makers and inhabitants can acquire improved information to assess urban heat islands, to propose more adequate planning policies, but more importantly to tackle urban heat and thermal comfort at the finest scales. HST-LST will effectively address the low spatial resolution of thermal bands. As HSR-LST is both automated and dynamic, it can be portable to other urban areas with diverse climatic regions.

Remote sensing image analysis systems and geographic information systems (GIS) show great promise for the integration of a wide variety of spatial information as a support to tasks such as urban and regional planning, natural resources management, agricultural studies, topographic and thematic mapping, civil engineering, hydrology studies, and geological exploration. Current and future remote sensing satellite programs are based on a variety of imaging sensors that will provide timely and repetitive multisensor earth observation data on a global scale. Visible, infrared, and microwave images of high spatial and spectral resolution will be available eventually for all parts of the earth. However, it is essential that efficient synergistic processing techniques be developed to cope with the large multisensor data volumes and to allow efficient GIS integration. Many preliminary efforts to unite remote sensing and GIS into one technology for information management have been made over the past few years, both in the commercial and the academic world. In the first part of this paper, some of the achievements so far in the areas of topographic mapping, cartographic feature extraction, and urban and regional planning are discussed. A characteristic of most successful integrated systems is that they are confined to very specific problems. In the second part of the paper, these successes are placed in the context of a vision of longterm integration. Obstacles and concerns related to such integration are discussed.

New ongoing rural construction has resulted in an extensive mixture of new settlements with old ones in the rural areas of China. Understanding the spatial characteristic of these rural settlements is of crucial importance as it provides essential information for land management and decision-making. Despite a great advance in High Spatial Resolution (HSR) satellite images and deep learning techniques, it remains a challenging task for mapping rural settlements accurately because of their irregular morphology and distribution pattern. In this study, we proposed a novel framework to map rural settlements by leveraging the merits of Gaofen-2 HSR images and representation learning of deep learning. We combined a dilated residual convolutional network (Dilated-ResNet) and a multi-scale context subnetwork into an end-to-end architecture in order to learn high resolution feature representations from HSR images and to aggregate and refine the multi-scale features extracted by the aforementioned network. Our experiment in Tongxiang city showed that the proposed framework effectively mapped and discriminated rural settlements with an overall accuracy of 98% and Kappa coefficient of 85%, achieving comparable and improved performance compared to other existing methods. Our results bring tangible benefits to support other convolutional neural network (CNN)-based methods in accurate and timely rural settlement mapping, particularly when up-to-date ground truth is absent. The proposed method does not only offer an effective way to extract rural settlement from HSR images but open a new opportunity to obtain spatial-explicit understanding of rural settlements.

This work deals with the retrieval of distribution of seabed vegetation cover (essentially Posidonia Oceanica) in the coastal waters (1-10m) of the Gulf of Oristano. A physical approach was used to derive bottom cover map from airborne and satellite imagery (MIVIS, KOMPSAT-2 and RapidEye) acquired in summer 2001, 2011 and 2013. A bathymetric map was also derived from the RapidEye image of 2003. A field campaign was held on 1-8 August 2013 to parameterize the physical model and to validate the 2013 results. Accuracy of bottom cover map and regression between measured and modelled water depths was good providing an overall accuracy of 0.88% and a correlation coefficient of 0.84, respectively. The retrospective analysis revealed that P. oceanica meadow seems to be stable over the 12 years showing only very small variations (-0.2km 2 ). Sea bottom morphology and vegetation cover were also related.

Remote Sensing is identified as an effective and efficient tool for monitoring fire events, and for quantifying fire effects on environment. Satellite images are used both to identify active fires and to analyse their effects, as well as to define burnt areas and map the severity of fires. Fires modify the structure and the reflectance of vegetation as well as the soil properties within the burned area; the produced changes are detectable in the visible, infrared and microwave parts of the electromagnetic spectrum. One of the most useful approach is based on classification of images using the spectral properties of burnt residues. This paper aims to use data obtained from optical sensor mounted on Sentinel-2 platform for mapping areas damaged by fire in a precise and rapid way. Sentinel-2 offers multispectral medium and high spatial resolution images with 13 spectral bands and about 5-day temporal resolution. Two images concerning the same scene in Campania Region but acquired on different dates are considered: pre-fire and post-fire. For each image, the Normalized Burned Ratio Index (NBR) is calculated, which allows to identify the areas affected by the fire and the relative degree of severity. Using change detection techniques, burned map can be identified. The evaluation of the accuracy is carried out using some indexes widespread in remote sensing literature, such as User’s Accuracy, Producer’s Accuracy, Overall Accuracy and Kappa coefficient. The values obtained in the confusion matrix showed the high quality of the developed method based on the use of the NBR index.KeywordsNBR (Normalized Burned Ratio Index)Sentinel-2Burned areaChange detection