Journal articles: 'Remote-sensing maps'

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Asylbekova, A., and A. Kikkarina. "Updating topographic maps using Remote Sensing." Journal of Geography and Environmental Management 42, no. 1 (2016): 168–74. http://dx.doi.org/10.26577/jgem.2016.1.296.

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Villmann, Thomas, Erzsébet Merényi, and Barbara Hammer. "Neural maps in remote sensing image analysis." Neural Networks 16, no. 3-4 (April 2003): 389–403. http://dx.doi.org/10.1016/s0893-6080(03)00021-2.

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Zeug, Gunter, and Olaf Kranz. "Remote Sensing Based Population Maps for Crisis Response." Photogrammetrie - Fernerkundung - Geoinformation 2010, no. 1 (February 1, 2010): 33–46. http://dx.doi.org/10.1127/1432-8364/2010/0038.

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King, Trude V. V., Raymond F. Kokaly, Todd M. Hoefen, and Michaela R. Johnson. "Hyperspectral remote sensing data maps minerals in Afghanistan." Eos, Transactions American Geophysical Union 93, no. 34 (August 21, 2012): 325–26. http://dx.doi.org/10.1029/2012eo340002.

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Babayev, A. G., and N. G. Kharin. "REMOTE SENSING METHODS FOR COMPILING MAPS OF DESERTIFICATION." Mapping Sciences and Remote Sensing 26, no. 4 (October 1989): 325–30. http://dx.doi.org/10.1080/07493878.1989.10641780.

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Bedell, Richard. "Remote Sensing in Mineral Exploration." SEG Discovery, no. 58 (July 1, 2004): 1–14. http://dx.doi.org/10.5382/segnews.2004-58.fea.

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ABSTRACT The proliferation of remote sensing platforms has resulted in unprecedented opportunities for ore deposit vectoring. Importantly, remote sensing technology is now beyond the vague identifıcation of alteration, and can accurately map specifıc minerals and directly contribute to the understanding of ore systems. Remote sensing is making discoveries of new alteration zones within classic and previously well mapped ore systems, as well as outlining their geometry and mineralogy. Confıning this review to the geologically important reflected-light remote sensing systems, there are four main categories of sensors readily available to economic geologists, including the following: (1) submeter resolution panchromatic satellites that offer little spectral information but provide base maps; (2) multispectral Landsat satellites that can map iron and clay alteration; (3) the new ASTER satellite that can map important alteration groups and some specifıc minerals; and (4) hyperspectral airborne scanners that can provide maps of specifıc mineral species important to detailed alteration mapping. At the core of comprehending this plethora of technology is the difference between spectral and spatial resolution. This review will provide an understanding of the more fundamental aspects of remote sensing systems that will help fıeld geologists to interact better with and leverage this rapidly evolving technology.

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Yakubov, Gayrat, Khamid Mubarakov, Ilkhomjon Abdullaev, and Azizjon Ruziyev. "Creating large-scale maps for agriculture using remote sensing." E3S Web of Conferences 227 (2021): 03002. http://dx.doi.org/10.1051/e3sconf/202122703002.

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Reliable information on the real state of agricultural lands will be required to the development of appropriate measures for the rational use of agricultural lands. To obtain such information, it is necessary to keep permanent and systematic records and inventories of land resources. Large-scale special plans and maps will be required for accounting, inventory and classification of agricultural land. Currently in Uzbekistan such cartographic materials are being created on the scale 1: 10 000 and 1: 25 000 by administrative and territorial units, farms or individual land plots. The article considers the issues of creation of special maps of agricultural land in scale 1:10000 on the example of Sharof Rashidov district of Jizzakh region using remote sensing data with very high spatial resolution KOMPSAT-3.

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Zhu, Zhiqin, Yaqin Luo, Guanqiu Qi, Jun Meng, Yong Li, and Neal Mazur. "Remote Sensing Image Defogging Networks Based on Dual Self-Attention Boost Residual Octave Convolution." Remote Sensing 13, no. 16 (August 6, 2021): 3104. http://dx.doi.org/10.3390/rs13163104.

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Remote sensing images have been widely used in military, national defense, disaster emergency response, ecological environment monitoring, among other applications. However, fog always causes definition of remote sensing images to decrease. The performance of traditional image defogging methods relies on the fog-related prior knowledge, but they cannot always accurately obtain the scene depth information used in the defogging process. Existing deep learning-based image defogging methods often perform well, but they mainly focus on defogging ordinary outdoor foggy images rather than remote sensing images. Due to the different imaging mechanisms used in ordinary outdoor images and remote sensing images, fog residue may exist in the defogged remote sensing images obtained by existing deep learning-based image defogging methods. Therefore, this paper proposes remote sensing image defogging networks based on dual self-attention boost residual octave convolution (DOC). Residual octave convolution (residual OctConv) is used to decompose a source image into high- and low-frequency components. During the extraction of feature maps, high- and low-frequency components are processed by convolution operations, respectively. The entire network structure is mainly composed of encoding and decoding stages. The feature maps of each network layer in the encoding stage are passed to the corresponding network layer in the decoding stage. The dual self-attention module is applied to the feature enhancement of the output feature maps of the encoding stage, thereby obtaining the refined feature maps. The strengthen-operate-subtract (SOS) boosted module is used to fuse the refined feature maps of each network layer with the upsampling feature maps from the corresponding decoding stage. Compared with existing image defogging methods, comparative experimental results confirm the proposed method improves both visual effects and objective indicators to varying degrees and effectively enhances the definition of foggy remote sensing images.

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Huang, Liang, Qiuzhi Peng, and Xueqin Yu. "Change Detection in Multitemporal High Spatial Resolution Remote-Sensing Images Based on Saliency Detection and Spatial Intuitionistic Fuzzy C-Means Clustering." Journal of Spectroscopy 2020 (March 23, 2020): 1–9. http://dx.doi.org/10.1155/2020/2725186.

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In order to improve the change detection accuracy of multitemporal high spatial resolution remote-sensing (HSRRS) images, a change detection method of multitemporal remote-sensing images based on saliency detection and spatial intuitionistic fuzzy C-means (SIFCM) clustering is proposed. Firstly, the cluster-based saliency cue method is used to obtain the saliency maps of two temporal remote-sensing images; then, the saliency difference is obtained by subtracting the saliency maps of two temporal remote-sensing images; finally, the SIFCM clustering algorithm is used to classify the saliency difference image to obtain the change regions and unchange regions. Two data sets of multitemporal high spatial resolution remote-sensing images are selected as the experimental data. The detection accuracy of the proposed method is 96.17% and 97.89%. The results show that the proposed method is a feasible and better performance multitemporal remote-sensing image change detection method.

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Anugraha, A. S., and H. J. Chu. "LAND USE CLASSIFICATION FROM COMBINED USE OF REMOTE SENSING AND SOCIAL SENSING DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 (September 19, 2018): 33–39. http://dx.doi.org/10.5194/isprs-archives-xlii-4-33-2018.

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<p><strong>Abstract.</strong> Large amounts of data can be sensed and analyzed to discover patterns of human behavior in cities for the benefit of urban authorities and citizens, especially in the areas of traffic forecasting, urban planning, and social science. In New York, USA, social sensing, remote sensing, and urban land use information support the discovery of patterns of human behavior. This research uses two types of openly accessible data, namely, social sensing data and remote sensing data. Bike and taxi data are examples of social sensing data, whereas sentinel remote sensed imagery is an example of remote sensing data. This research aims to sense and analyze the patterns of human behavior and to classify land use from the combination of remote sensing data and social sensing data. A decision tree is used for land use classification. Bike and taxi density maps are generated to show the locations of people around the city during the two peak times. On the basis of a geographic information system, the maps also reflect the residential and office areas in the city. The overall accuracy of land use classification after the consideration of social sensing data is 85.3%. The accuracy assessment shows that the combination of remote sensing data and social sensing data facilitates accurate urban land use classification.</p>

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Franklin, S. E., M. J. Hansen, and G. B. Stenhouse. "Quantifying landscape structure with vegetation inventory maps and remote sensing." Forestry Chronicle 78, no. 6 (December 1, 2002): 866–75. http://dx.doi.org/10.5558/tfc78866-6.

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Two input maps based on Alberta Vegetation Inventory (AVI) data and Landsat satellite imagery were generated for use in a fragmentation analysis of a large area in the Alberta Yellowhead Ecosystem to support long-term grizzly bear habitat analysis. Accuracy was assessed using visual interpretation of classes on digital orthophotography. Approximately 45% map accuracy was obtained after applying a generalization procedure to the available AVI GIS database. Approximately 80% map accuracy was achieved used a supervised classification approach applied to the Landsat image. Differences in accuracy were most apparent in non-treed vegetation classes (e.g., shrub), closed conifer, mixedwood and deciduous forest classes. Very large differences were observed in many of the landscape metrics computed from these two maps to quantify landscape structure. Simulating forest changes on these maps illustrated the difficulty of comparing maps generated with different geospatial technologies. Key words: fragmentation, satellite remote sensing, GIS vegetation inventory maps, landscape metrics

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Räsänen, Aleksi, Terhikki Manninen, Mika Korkiakoski, Annalea Lohila, and Tarmo Virtanen. "Predicting catchment-scale methane fluxes with multi-source remote sensing." Landscape Ecology 36, no. 4 (February 10, 2021): 1177–95. http://dx.doi.org/10.1007/s10980-021-01194-x.

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Abstract Context Spatial patterns of CH4 fluxes can be modeled with remotely sensed data representing land cover, soil moisture and topography. Spatially extensive CH4 flux measurements conducted with portable analyzers have not been previously upscaled with remote sensing. Objectives How well can the CH4 fluxes be predicted with plot-based vegetation measures and remote sensing? How does the predictive skill of the model change when using different combinations of predictor variables? Methods We measured CH4 fluxes in 279 plots in a 12.4 km2 peatland-forest-mosaic landscape in Pallas area, northern Finland in July 2019. We compared 20 different CH4 flux maps produced with vegetation field data and remote sensing data including Sentinel-1, Sentinel-2 and digital terrain model (DTM). Results The landscape acted as a net source of CH4 (253–502 µg m−2 h−1) and the proportion of source areas varied considerably between maps (12–50%). The amount of explained variance was high in CH4 regressions (59–76%, nRMSE 8–10%). Regressions including remote sensing predictors had better performance than regressions with plot-based vegetation predictors. The most important remote sensing predictors included VH-polarized Sentinel-1 features together with topographic wetness index and other DTM features. Spatial patterns were most accurately predicted when the landscape was divided into sinks and sources with remote sensing-based classifications, and the fluxes were modeled for sinks and sources separately. Conclusions CH4 fluxes can be predicted accurately with multi-source remote sensing in northern boreal peatland landscapes. High spatial resolution remote sensing-based maps constrain uncertainties related to CH4 fluxes and their spatial patterns.

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Tofani, V., S. Segoni, A. Agostini, F. Catani, and N. Casagli. "Technical Note: Use of remote sensing for landslide studies in Europe." Natural Hazards and Earth System Sciences 13, no. 2 (February 8, 2013): 299–309. http://dx.doi.org/10.5194/nhess-13-299-2013.

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Abstract. Within the framework of FP7, an EU-funded SafeLand project, a questionnaire was prepared to collect information about the use of remote sensing for landslide study and to evaluate its actual application in landslide detection, mapping and monitoring. The questionnaire was designed using a Google form and was disseminated among end-users and researchers involved in landslide studies in Europe. In total, 49 answers from 17 different European countries were collected. The outcomes showed that landslide detection and mapping is mainly performed with aerial photos, often associated with optical and radar imagery. Concerning landslide monitoring, satellite radars prevail over the other types of data. Remote sensing is mainly used for detection/mapping and monitoring of slides, flows and lateral spreads with a preferably large scale of analysis (1:5000–1:25 000). All the compilers integrate remote sensing data with other thematic data, mainly geological maps, landslide inventory maps and DTMs and derived maps. According to the research and working experience of the compilers, remote sensing is generally considered to have a medium effectiveness/reliability for landslide studies. The results of the questionnaire can contribute to an overall sketch of the use of remote sensing in current landslide studies and show that remote sensing can be considered a powerful and well-established instrument for landslide mapping, monitoring and hazard analysis.

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McDermid, Gregory J., Steven E. Franklin, and Ellsworth F. LeDrew. "Remote sensing for large-area habitat mapping." Progress in Physical Geography: Earth and Environment 29, no. 4 (December 2005): 449–74. http://dx.doi.org/10.1191/0309133305pp455ra.

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Remote sensing has long been identified as a technology capable of supporting the development of wildlife habitat maps over large areas. However, progress has been constrained by underdeveloped linkages between resource managers with extensive knowledge of ecology and remote sensing scientists with backgrounds in geography. This article attempts to traverse that gap by (i) clarifying the imprecise and commonly misunderstood concept of ‘habitat’, (ii) exploring the recent use of remote sensing in previous habitat-mapping exercises, (iii) reviewing the remote sensing toolset developed for extracting information from optical satellite imagery, and (iv) outlining a framework for linking ecological information needs with remote sensing techniques.

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Varlamova, A. A., A. Y. Denisova, and V. V. Sergeev. "Earth remote sensing data processing for obtaining vegetation types maps." Computer Optics 42, no. 5 (2018): 864–76. http://dx.doi.org/10.18287/2412-6179-2018-42-5-864-876.

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Gottwald, Manfred, Thomas Fritz, Helko Breit, Birgit Schättler, and Alan Harris. "Remote sensing of terrestrial impact craters: The TanDEM-X digital elevation model." Meteoritics & Planetary Science 52, no. 7 (November 27, 2016): 1412–27. http://dx.doi.org/10.1111/maps.12794.

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Anugraha, Adindha, Hone-Jay Chu, and Muhammad Ali. "Social Sensing for Urban Land Use Identification." ISPRS International Journal of Geo-Information 9, no. 9 (September 15, 2020): 550. http://dx.doi.org/10.3390/ijgi9090550.

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The utilization of urban land use maps can reveal the patterns of human behavior through the extraction of the socioeconomic and demographic characteristics of urban land use. Remote sensing that holds detailed and abundant information on spectral, textual, contextual, and spatial configurations is crucial to obtaining land use maps that reveal changes in the urban environment. However, social sensing is essential to revealing the socioeconomic and demographic characteristics of urban land use. This data mining approach is related to data cleaning/outlier removal and machine learning, and is used to achieve land use classification from remote and social sensing data. In bicycle and taxi density maps, the daytime destination and nighttime origin density reflects work-related land uses, including commercial and industrial areas. By contrast, the nighttime destination and daytime origin density pattern captures the pattern of residential areas. The accuracy assessment of land use classified maps shows that the integration of remote and social sensing, using the decision tree and random forest methods, yields accuracies of 83% and 86%, respectively. Thus, this approach facilitates an accurate urban land use classification. Urban land use identification can aid policy makers in linking human activities to the socioeconomic consequences of different urban land uses.

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Yang, Shu Rong, and Yi Lung Yeh. "Geologic Hazard Assessment of Slopeland Villages Using Remote Sensing Techniques." Applied Mechanics and Materials 764-765 (May 2015): 1095–99. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.1095.

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This study focuses on 53 villages located in the slopelands of Pingtung County. Remote sensing image interpretation techniques are used to identify geologic hazard areas. GIS map overlay analysis of environmental geologic maps, landslide susceptibility maps and potential debris flow torrent maps provided by local and regional governments are used to further interpret and correctly identify the extent of the geologic hazard zone. This study successfully combines both GIS and GPS techniques, and according to data analysis results, constructs a slopeland village geologic hazard assessment method.

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Cheng, Gong, Huikun Huang, Huan Li, Xiaoqing Deng, Rehan Khan, Landry SohTamehe, Asad Atta, Xuechong Lang, and Xiaodong Guo. "Quantitative Remote Sensing of Metallic Elements for the Qishitan Gold Polymetallic Mining Area, NW China." Remote Sensing 13, no. 13 (June 28, 2021): 2519. http://dx.doi.org/10.3390/rs13132519.

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The recent development in remote sensing imagery and the use of remote sensing detection feature spectrum information together with the geochemical data is very useful for the surface element quantitative remote sensing inversion study. This aim of this article is to select appropriate methods that would make it possible to have rapid economic prospecting. The Qishitan gold polymetallic deposit in the Xinjiang Uygur Autonomous Region, Northwest China has been selected for this study. This paper establishes inversion maps based on the contents of metallic elements by integrating geochemical exploration data with ASTER and WorldView-2 remote sensing data. Inversion modelling maps for As, Cu, Hg, Mo, Pb, and Zn are consistent with the corresponding geochemical anomaly maps, which provide a reference for metallic ore prospecting in the study area. ASTER spectrum covers short-wave infrared and has better accuracy than WorldView-2 data for the inversion of some elements (e.g., Au, Hg, Pb, and As). However, the high spatial resolution of WorldView-2 drives the final content inversion map to be more precise and to better localize the anomaly centers of the inversion results. After scale conversion by re-sampling and kriging interpolation, the modeled and predicted accuracy of the models with square interpolation is much closer compare with the ground resolution of the used remote sensing data. This means our results are much satisfactory as compared to other interpolation methods. This study proves that quantitative remote sensing has great potential in ore prospecting and can be applied to replace traditional geochemical exploration to some extent.

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Hou, Jie-Bo, Xiaobin Zhu, and Xu-Cheng Yin. "Self-Adaptive Aspect Ratio Anchor for Oriented Object Detection in Remote Sensing Images." Remote Sensing 13, no. 7 (March 30, 2021): 1318. http://dx.doi.org/10.3390/rs13071318.

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Object detection is a significant and challenging problem in the study of remote sensing. Since remote sensing images are typically captured with a bird’s-eye view, the aspect ratios of objects in the same category may obey a Gaussian distribution. Generally, existing object detection methods ignore exploring the distribution character of aspect ratios for improving performance in remote sensing tasks. In this paper, we propose a novel Self-Adaptive Aspect Ratio Anchor (SARA) to explicitly explore aspect ratio variations of objects in remote sensing images. To be concrete, our SARA can self-adaptively learn an appropriate aspect ratio for each category. In this way, we can only utilize a simple squared anchor (related to the strides of feature maps in Feature Pyramid Networks) to regress objects in various aspect ratios. Finally, we adopt an Oriented Box Decoder (OBD) to align the feature maps and encode the orientation information of oriented objects. Our method achieves a promising mAP value of 79.91% on the DOTA dataset.

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Chen, Peng Xiao, Shao Hong Shen, and Xiong Fei Wen. "Remote Sensing Dynamic Monitoring on Illegal Capacity Occupation of Reservoir." Advanced Materials Research 718-720 (July 2013): 1124–28. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1124.

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Monitoring the illegally occupied channels is very important for the management and regulations of reservoirs. This paper proposes an automatic and efficient approach to identify the changes in the river course with geographic information system and global position system using multi-temporal remote sensing images. Unlike the traditional river course monitoring system, this approach is mainly based on the change detection information extracting from multi-temporal high spatial resolution remote sensing images. Firstly, change detection from different information of multi-temporal remote sensing images are applied to obtain the change information thematic maps which can be used as working maps for on-site investigation are extracted. Secondly, GPS-RTK measurement technology is used to obtain 3-D position information of the terrain features points in those channel occupied areas. Then, an approach for calculating the volume of the channel occupied area is designed and developed by ArcGIS software using multi-temporal remote sensing images, 3-D position information and historical digital terrain date of channel occupied area. Finally, channel occupied area volume data and thematic maps are acquired by ArcGIS software. The data of reservoir is selected as experimental area, and the experiments have confirmed the high efficiency and accuracy of this approach proposed in this paper.

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Su, Hao, Shunjun Wei, Shan Liu, Jiadian Liang, Chen Wang, Jun Shi, and Xiaoling Zhang. "HQ-ISNet: High-Quality Instance Segmentation for Remote Sensing Imagery." Remote Sensing 12, no. 6 (March 19, 2020): 989. http://dx.doi.org/10.3390/rs12060989.

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Instance segmentation in high-resolution (HR) remote sensing imagery is one of the most challenging tasks and is more difficult than object detection and semantic segmentation tasks. It aims to predict class labels and pixel-wise instance masks to locate instances in an image. However, there are rare methods currently suitable for instance segmentation in the HR remote sensing images. Meanwhile, it is more difficult to implement instance segmentation due to the complex background of remote sensing images. In this article, a novel instance segmentation approach of HR remote sensing imagery based on Cascade Mask R-CNN is proposed, which is called a high-quality instance segmentation network (HQ-ISNet). In this scheme, the HQ-ISNet exploits a HR feature pyramid network (HRFPN) to fully utilize multi-level feature maps and maintain HR feature maps for remote sensing images’ instance segmentation. Next, to refine mask information flow between mask branches, the instance segmentation network version 2 (ISNetV2) is proposed to promote further improvements in mask prediction accuracy. Then, we construct a new, more challenging dataset based on the synthetic aperture radar (SAR) ship detection dataset (SSDD) and the Northwestern Polytechnical University very-high-resolution 10-class geospatial object detection dataset (NWPU VHR-10) for remote sensing images instance segmentation which can be used as a benchmark for evaluating instance segmentation algorithms in the high-resolution remote sensing images. Finally, extensive experimental analyses and comparisons on the SSDD and the NWPU VHR-10 dataset show that (1) the HRFPN makes the predicted instance masks more accurate, which can effectively enhance the instance segmentation performance of the high-resolution remote sensing imagery; (2) the ISNetV2 is effective and promotes further improvements in mask prediction accuracy; (3) our proposed framework HQ-ISNet is effective and more accurate for instance segmentation in the remote sensing imagery than the existing algorithms.

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Mutowo, Godfrey, and David Chikodzi. "Remote sensing based drought monitoring in Zimbabwe." Disaster Prevention and Management 23, no. 5 (October 28, 2014): 649–59. http://dx.doi.org/10.1108/dpm-10-2013-0181.

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Purpose – Drought monitoring is an important process for national agricultural and environmental planning. Droughts are normal recurring climatic phenomena that affect people and landscapes. They occur at different scales (locally, regionally, and nationally), and for periods of time ranging from weeks to decades. In Zimbabwe drought is increasingly becoming an annual phenomenon, with varying parts of the country being affected. The purpose of this paper is to analyse the spatial variations in the seasonal occurrences of drought in Zimbabwe over a period of five years. Design/methodology/approach – The Vegetation Condition Index (VCI), which shows how close the Normalized Difference Vegetation Index of the current time is to the minimum Normalized Difference Vegetation Index calculated from the long-term record for that given time, was used to monitor drought occurrence in Zimbabwe. A time series of dekadal Normalized Difference Vegetation Index, calculated from SPOT images, was used to compute seasonal VCI maps from 2005 to 2010. The VCI maps were then classified into three drought severity classes (severe, moderate, and mild) based on the relative changes in the vegetation condition from extremely bad to optimal. Findings – The results showed that droughts occur annually in Zimbabwe though, on average, the droughts are mostly mild. The occurrence and the spatial distribution of drought in Zimbabwe was also found to be random affecting different places from season to season thus the authors conclude that most parts of the country are drought prone. Originality/value – Remote sensing technologies utilising such indices as the VCI can be used for drought monitoring in Zimbabwe.

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Franklin, S. E., E. E. Dickson, M. J. Hansen, D. R. Farr, and L. M. Moskal. "Quantification of landscape change from satellite remote sensing." Forestry Chronicle 76, no. 6 (December 1, 2000): 877–86. http://dx.doi.org/10.5558/tfc76877-6.

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Satellite remote sensing data and methods can be used to develop maps of large areas at different times in order to assess changes in forest ecosystem patterns and processes. Such maps are useful in understanding wildlife populations and habitat, forest biodiversity, and forest productivity. They may be important in ecological monitoring programs at multiple spatial and temporal scales, and could include assessment of structural aspects of the landscape, such as forest or habitat fragmentation. Quantification and measurement of landscape structure depend on the definition of landscape classes or patches, defined on the basis of more or less homogeneous elements, which differ in some measurable way from neighbouring patches. In this paper, we review some of the issues, and provide examples using satellite remote sensing data, in the quantification of landscape structure in two Canadian forests. The link between landscape structure and biodiversity is provided through the emergence of ecological understanding of species richness, species-habitat or niches, and metapopulation dynamics. Key words: forest disturbance, landscape metrics, satellite remote sensing, forest fragmentation, monitoring, biodiversity, change detection

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Soloviov, I. V., and V. A. Zhelezniakov. "Algorithms for updating electronic maps on the remote sensing data use." Geodesy and Cartography 886, no. 4 (May 20, 2014): 13–18. http://dx.doi.org/10.22389/0016-7126-2014-886-4-13-18.

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Bennett, Rohan, Peter van Oosterom, Christiaan Lemmen, and Mila Koeva. "Remote Sensing for Land Administration." Remote Sensing 12, no. 15 (August 4, 2020): 2497. http://dx.doi.org/10.3390/rs12152497.

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Land administration constitutes the socio-technical systems that govern land tenure, use, value and development within a jurisdiction. The land parcel is the fundamental unit of analysis. Each parcel has identifiable boundaries, associated rights, and linked parties. Spatial information is fundamental. It represents the boundaries between land parcels and is embedded in cadastral sketches, plans, maps and databases. The boundaries are expressed in these records using mathematical or graphical descriptions. They are also expressed physically with monuments or natural features. Ideally, the recorded and physical expressions should align, however, in practice, this may not occur. This means some boundaries may be physically invisible, lacking accurate documentation, or potentially both. Emerging remote sensing tools and techniques offers great potential. Historically, the measurements used to produce recorded boundary representations were generated from ground-based surveying techniques. The approach was, and remains, entirely appropriate in many circumstances, although it can be timely, costly, and may only capture very limited contextual boundary information. Meanwhile, advances in remote sensing and photogrammetry offer improved measurement speeds, reduced costs, higher image resolutions, and enhanced sampling granularity. Applications of unmanned aerial vehicles (UAV), laser scanning, both airborne and terrestrial (LiDAR), radar interferometry, machine learning, and artificial intelligence techniques, all provide examples. Coupled with emergent societal challenges relating to poverty reduction, rapid urbanisation, vertical development, and complex infrastructure management, the contemporary motivation to use these new techniques is high. Fundamentally, they enable more rapid, cost-effective, and tailored approaches to 2D and 3D land data creation, analysis, and maintenance. This Special Issue hosts papers focusing on this intersection of emergent remote sensing tools and techniques, applied to domain of land administration.

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Wu, Hai Yan. "Research and Implementation of Remote Sensing Image Vectorization." Key Engineering Materials 439-440 (June 2010): 220–24. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.220.

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Remote sensing image vectorization; edge enhancement; boundary tracking; Abstract. This paper researched and implemented remote sensing image vectorization process using semi-automated way. Remote sensing image can be vectored as the following five steps:vertical and horizontal edge enhancement, open operation, close operation, binarization, and finally the boundary tracking. The paper presented an improved tracking algorithm to avoid the shortcomings of traditional edge tracking algorithm. In our tracking algorithm, for image maps, we first use open operation and then use close operation to remove noise.This vectorization method changed the traditional data collection mode and improved data collection efficiency.

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Ghasemi Nejad, R., P. Pahlavani, and B. Bigdeli. "AUTOMATIC BUILDING EXTRACTION USING A DECISION TREE OBJECT-BASED CLASSIFICATION ON JOINT USE OF AERIAL AND LIDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 429–34. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-429-2019.

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Abstract. Updating digital maps is a challenging task that has been considered for many years and the requirement of up-to-date urban maps is universal. One of the main procedures used in updating digital maps and spatial databases is building extraction which is an active research topic in remote sensing and object-based image analysis (OBIA). Since in building extraction field a full automatic system is not yet operational and cannot be implemented in a single step, experts are used to define classification rules based on a complex and subjective “trial-and-error” process. In this paper, a decision tree classification method called, C4.5, was adopted to construct an automatic model for building extraction based on the remote sensing data. In this method, a set of rules was derived automatically then a rule-based classification is applied to the remote sensing data include aerial and lidar images. The results of experiments showed that the obtained rules have exceptional predictive performance.

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Schreiner, Simon, Dubravko Culibrk, Michele Bandecchi, Wolfgang Gross, and Wolfgang Middelmann. "Soil monitoring for precision farming using hyperspectral remote sensing and soil sensors." at - Automatisierungstechnik 69, no. 4 (April 1, 2021): 325–35. http://dx.doi.org/10.1515/auto-2020-0042.

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Abstract This work describes an approach to calculate pedological parameter maps using hyperspectral remote sensing and soil sensors. These maps serve as information basis for automated and precise agricultural treatments by tractors and field robots. Soil samples are recorded by a handheld hyperspectral sensor and analyzed in the laboratory for pedological parameters. The transfer of the correlation between these two data sets to aerial hyperspectral images leads to 2D-parameter maps of the soil surface. Additionally, rod-like soil sensors provide local 3D-information of pedological parameters under the soil surface. The goal is to combine the area-covering 2D-parameter maps with the local 3D-information to extrapolate large-scale 3D-parameter maps using AI approaches.

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Møller-Jensen, L., and A. N. Allotey. "CONSISTENCY IN REMOTE SENSING-BASED URBAN MAPPING FOR GROWTH AND MOBILITY ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W16 (October 1, 2019): 461–62. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w16-461-2019.

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Abstract. This extended abstract presents initial results from a study that focus on the accuracy of satellite-based maps of Accra’s urban expansion as well as their comparability and ability to provide a basis for assessing urban spatial growth dynamics. Within the study we have analysed five different satellite-derived maps of urban development in Accra and compared the results and underlying methods. It is discussed how these maps can be operationalized and, combined with census data and digital road maps, used for calculating how flood events that disable parts of the transport infrastructure impact the overall mobility of the Accra population.

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Hua, Zhen, Zhenzhu Bian, and Jinjiang Li. "Airport Detection-Based Cosaliency on Remote Sensing Images." Mathematical Problems in Engineering 2021 (May 7, 2021): 1–17. http://dx.doi.org/10.1155/2021/8956396.

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This paper proposes a contour extraction model based on cosaliency detection for remote sensing image airport detection and improves the traditional line segmentation detection (LSD) algorithm to make it more suitable for the goal of this paper. Our model consists of two parts, a cosaliency detection module and a contour extraction module. In the first part, the cosaliency detection module mainly uses the network framework of Visual Geometry Group-19 (VGG-19) to obtain the result maps of the interimage comparison and the intraimage consistency, and then the two result maps are multiplied pixel by pixel to obtain the cosaliency mask. In the second part, the contour extraction module uses superpixel segmentation and parallel line segment detection (PLSD) to refine the airport contour and runway information to obtain the preprocessed result map, and then we merge the result of cosaliency detection with the preprocessed result to obtain the final airport contour. We compared the model proposed in this article with four commonly used methods. The experimental results show that the accuracy of the model is 15% higher than that of the target detection result based on the saliency model, and the accuracy of the active contour model based on the saliency analysis is improved by 1%. This shows that the model proposed in this paper can extract a contour that closely matches the actual target.

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Matsui, Kai, Yoichi Kageyama, and Hiroshi Yokoyama. "Analysis of Water Quality Conditions of Lake Hachiroko Using Fuzzy C-Means." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 3 (May 20, 2019): 456–64. http://dx.doi.org/10.20965/jaciii.2019.p0456.

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Lake Hachiroko, Japan, has many water quality issues, evident from phenomena such as green algae blooms. Understanding the details of the surface water quality of the lake, and the effect of seasons on the quality, is important. In our previous studies, we conducted fuzzy regression analysis of remote sensing data and direct measurements of water quality. The results showed that estimation maps of water quality were well created, using only five data points of the water quality parameters. To obtain maps that are in good agreement with the experimental data, remote sensing data and water quality values should be acquired simultaneously. However, performing such simultaneous observations can affect the preparation of the water quality estimation maps. We overcame this obstacle by using fuzzy c-means clustering (FCM), and considered the effect of specific disturbances and uncertainties on the remote sensing data. Furthermore, FCM using only remote sensing data creates estimation maps in which relative water surface conditions are classified. Therefore, determining the relationship between FCM results and water quality facilitates the creation of low-cost, high-frequency water quality estimation maps. Our results indicated that FCM was particularly effective in determining the presence of suspended solids (SS) during water quality analysis. However, the relationship between FCM results and water quality has not been determined in detail. In this study, we analyzed the water quality conditions of Lake Hachiroko with FCM using the data collected by the Advanced Space-borne Thermal Emission and Reflection Radiometer on Terra and, the Operational Land Imager on Landsat-8. In addition, FCM results were compared with the maps created by fuzzy regression analysis and the actual conditions of water pollution. The results indicated that (i) the maps created using FCM are effective in determining the water surface conditions, (ii) the FCM maps using data obtained during August and September have a strong relationship with biochemical oxygen demand (BOD) and SS, and (iii) the FCM maps using data obtained during May and June have a strong relationship with chemical oxygen demand (COD), SS, and total nitrogen (T-N).

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Van de Voorde, Tim, Johannes van der Kwast, Frank Canters, Guy Engelen, Marc Binard, Yves Cornet, and Inge Uljee. "A Remote Sensing Based Calibration Framework for the MOLAND Urban Growth Model of Dublin." International Journal of Agricultural and Environmental Information Systems 3, no. 2 (July 2012): 1–21. http://dx.doi.org/10.4018/jaeis.2012070101.

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Land-use change models are useful tools for assessing and comparing the environmental impact of alternative policy scenarios. Their increasing popularity as spatial planning instruments also poses new scientific challenges, such as correctly calibrating the model. The challenge in model calibration is twofold: obtaining a reliable and consistent time series of land-use information and finding suitable measures to compare model output to reality. Both of these issues are addressed in this paper. The authors propose a model calibration framework that is supported by information on urban form and function derived from medium-resolution remote sensing data through newly developed spatial metrics. The remote sensing derived maps are compared to model output of the same date for two model scenarios using well-known spatial metrics. Results demonstrate a good resemblance between the simulation output and the remote sensing derived maps.

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MANDAL, DEBA PRASAD, C. A. MURTHY, and SANKAR K. PAL. "A REMOTE SENSING APPLICATION OF A FUZZY CLASSIFIER." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 02, no. 03 (September 1994): 287–95. http://dx.doi.org/10.1142/s0218488594000237.

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A multivalued (fuzzy) recognition system was formulated by Mandal et. al. which is capable of handling various imprecise inputs and provides multiple class choices to any input. In the present article, an application of the recognition system for detecting road-like structures from Indian Remote Sensing satellite (IRS) imagery has been described. The concept of multiple choices of the recognition system is found to be quite useful here. The results are seen to agree well with the geographical maps when various image frames were considered as input.

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Meshchaninova, E. G., and Yu A. Stepkin. "Application of remote sensing data in agriculture." Economy and ecology of territorial educations 4, no. 4 (2020): 72–77. http://dx.doi.org/10.23947/2413-1474-2020-4-4-72-77.

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The lack of accurate maps, an undeveloped network of points for operational and meteorological monitor-ing of ground stations, lack of air support, and much more makes it difficult to control large areas of agricultural land. Due to these factors and the lack of objective data in determining the state of land and forecasting the state of the situation, it is extremely difficult to increase agricultural production, optimize the costeffective use of land, and reduce costs to a minimum. Remote sensing of the Earth is actively used to solve various problems of integrated and specialized management of agricultural territories. It is difficult to overestimate the importance of the obtained remote sensing data, they allow us to solve a number of tasks in agriculture and, in particular, facilitate monitoring of the state of crops over large areas.

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Karimzadeh, Sadra, and Masashi Matsuoka. "Development of Nationwide Road Quality Map: Remote Sensing Meets Field Sensing." Sensors 21, no. 6 (March 23, 2021): 2251. http://dx.doi.org/10.3390/s21062251.

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In this study, we measured the in situ international roughness index (IRI) for first-degree roads spanning more than 1300 km in East Azerbaijan Province, Iran, using a quarter car (QC). Since road quality mapping with in situ measurements is a costly and time-consuming task, we also developed new equations for constructing a road quality proxy map (RQPM) using discriminant analysis and multispectral information from high-resolution Sentinel-2 images, which we calibrated using the in situ data on the basis of geographic information system (GIS) data. The developed equations using optimum index factor (OIF) and norm R provide a valuable tool for creating proxy maps and mitigating hazards at the network scale, not only for primary roads but also for secondary roads, and for reducing the costs of road quality monitoring. The overall accuracy and kappa coefficient of the norm R equation for road classification in East Azerbaijan province are 65.0% and 0.59, respectively.

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Fiorucci, Federica, Daniele Giordan, Michele Santangelo, Furio Dutto, Mauro Rossi, and Fausto Guzzetti. "Criteria for the optimal selection of remote sensing optical images to map event landslides." Natural Hazards and Earth System Sciences 18, no. 1 (January 30, 2018): 405–17. http://dx.doi.org/10.5194/nhess-18-405-2018.

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Abstract. Landslides leave discernible signs on the land surface, most of which can be captured in remote sensing images. Trained geomorphologists analyse remote sensing images and map landslides through heuristic interpretation of photographic and morphological characteristics. Despite a wide use of remote sensing images for landslide mapping, no attempt to evaluate how the image characteristics influence landslide identification and mapping exists. This paper presents an experiment to determine the effects of optical image characteristics, such as spatial resolution, spectral content and image type (monoscopic or stereoscopic), on landslide mapping. We considered eight maps of the same landslide in central Italy: (i) six maps obtained through expert heuristic visual interpretation of remote sensing images, (ii) one map through a reconnaissance field survey, and (iii) one map obtained through a real-time kinematic (RTK) differential global positioning system (dGPS) survey, which served as a benchmark. The eight maps were compared pairwise and to a benchmark. The mismatch between each map pair was quantified by the error index, E. Results show that the map closest to the benchmark delineation of the landslide was obtained using the higher resolution image, where the landslide signature was primarily photographical (in the landslide source and transport area). Conversely, where the landslide signature was mainly morphological (in the landslide deposit) the best mapping result was obtained using the stereoscopic images. Albeit conducted on a single landslide, the experiment results are general, and provide useful information to decide on the optimal imagery for the production of event, seasonal and multi-temporal landslide inventory maps.

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Englert, Peter A. J. "Planetary gamma ray spectrometry: remote sensing of elemental abundances." Proceedings in Radiochemistry 1, no. 1 (September 1, 2011): 349–55. http://dx.doi.org/10.1524/rcpr.2011.0062.

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Abstract Planetary gamma ray spectrometry is a form of nuclear spectroscopy applied remotely to provide geochemical maps of planetary bodies. From early developments it has by now become a standard modality of planetary exploration. Basic and applied nuclear science has made significant contributions to the advancement of planetary gamma ray spectrometry, as outlined in this methodological and historical assessment.

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Ma, Yunchuan, Pengyuan Lv, Hao Liu, Xuehong Sun, and Yanfei Zhong. "Remote Sensing Image Super-Resolution Based on Dense Channel Attention Network." Remote Sensing 13, no. 15 (July 28, 2021): 2966. http://dx.doi.org/10.3390/rs13152966.

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In the recent years, convolutional neural networks (CNN)-based super resolution (SR) methods are widely used in the field of remote sensing. However, complicated remote sensing images contain abundant high-frequency details, which are difficult to capture and reconstruct effectively. To address this problem, we propose a dense channel attention network (DCAN) to reconstruct high-resolution (HR) remote sensing images. The proposed method learns multi-level feature information and pays more attention to the important and useful regions in order to better reconstruct the final image. Specifically, we construct a dense channel attention mechanism (DCAM), which densely uses the feature maps from the channel attention block via skip connection. This mechanism makes better use of multi-level feature maps which contain abundant high-frequency information. Further, we add a spatial attention block, which makes the network have more flexible discriminative ability. Experimental results demonstrate that the proposed DCAN method outperforms several state-of-the-art methods in both quantitative evaluation and visual quality.

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Dai, Y., J. S. Xiao, B. S. Yi, J. F. Lei, and Z. Y. Du. "DETECTION OF ARTIFICIAL OBJECTS IN REMOTE SENSING IMAGE BASED ON DEEP LEARNING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 321–26. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-321-2020.

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Abstract. Aiming at multi-class artificial object detection in remote sensing images, the detection framework based on deep learning is used to extract and localize the numerous targets existing in very high resolution remote sensing images. In order to realize rapid and efficient detection of the typical artificial targets on the remote sensing image, this paper proposes an end-to-end multi-category object detection method in remote sensing image based on the convolutional neural network to solve several challenges, including dense objects and objects with arbitrary direction and large aspect ratios. Specifically, in this paper, the feature extraction process is improved by utilizing a more advanced backbone network with deeper layers and combining multiple feature maps including the high-resolution features maps with more location details and low-resolution feature maps with highly-abstracted information. And a Rotating Regional Proposal Network is adopted into the Faster R-CNN network to generate candidate object-like regions with different orientations and to improve the sensitivity to dense and cluttered objects. The rotation factor is added into the regional proposal network to control the generation of anchor box’s angle and to cover enough directions of typical man-made objects. Meanwhile, the misalignment caused by the two quantifications operations in the pooling process is eliminated and a convolution layer is appended before the fully connected layer of the final classification network to reduce the feature parameters and avoid overfitting. Compared with current generic object detection method, the proposed algorithm focus on the arbitrary oriented and dense artificial targets in remote sensing images. After comprehensive evaluation with several state-of-the-art object detection algorithms, our method is proved to be effective to detect multi-class artificial object in remote sensing image. Experiments demonstrate that the proposed method combines the powerful features extracted by the improved convolutional neural networks with multi-scale features and rotating region network is more accurate in the public DOTA dataset.

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Hsu, Astrid J., Joy Kumagai, Fabio Favoretto, John Dorian, Benigno Guerrero Martinez, and Octavio Aburto-Oropeza. "Driven by Drones: Improving Mangrove Extent Maps Using High-Resolution Remote Sensing." Remote Sensing 12, no. 23 (December 5, 2020): 3986. http://dx.doi.org/10.3390/rs12233986.

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This study investigated how different remote sensing techniques can be combined to accurately monitor mangroves. In this paper, we present a framework to use drone imagery to calculate correction factors which can improve the accuracy of satellite-based mangrove extent. We focus on semi-arid dwarf mangroves of Baja California Sur, Mexico, where the mangroves tend to be stunted in height and found in small patches, as well as larger forests. Using a DJI Phantom 4 Pro, we imaged mangroves and labeled the extent by manual classification in QGIS. Using ArcGIS, we compared satellite-based mangrove extent maps from Global Mangrove Watch (GMW) in 2016 and Mexico’s national government agency (National Commission for the Knowledge and Use of Biodiversity, CONABIO) in 2015, with extent maps generated from in situ drone studies in 2018 and 2019. We found that satellite-based extent maps generally overestimated mangrove coverage compared to that of drone-based maps. To correct this overestimation, we developed a method to derive correction factors for GMW mangrove extent. These correction factors correspond to specific pixel patterns generated from a convolution analysis and mangrove coverage defined from drone imagery. We validated our model by using repeated k-fold cross-validation, producing an accuracy of 98.3% ± 2.1%. Overall, drones and satellites are complementary tools, and the rise of machine learning can help stakeholders further leverage the strengths of the two tools, to better monitor mangroves for local, national, and international management.

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Colditz, R. R., R. M. Llamas, and R. A. Ressl. "Land cover change analysis in Mexico using 30m Landsat and 250m MODIS data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 29, 2015): 367–74. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-367-2015.

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Change detection is one of the most important and widely requested applications of terrestrial remote sensing. Despite a wealth of techniques and successful studies, there is still a need for research in remote sensing science. This paper addresses two important issues: the temporal and spatial scales of change maps. Temporal scales relate to the time interval between observations for successful change detection. We compare annual change detection maps accumulated over five years against direct change detection over that period. Spatial scales relate to the spatial resolution of remote sensing products. We compare fractions from 30m Landsat change maps to 250m grid cells that match MODIS change products. Results suggest that change detection at annual scales better detect abrupt changes, in particular those that do not persist over a longer period. The analysis across spatial scales strongly recommends the use of an appropriate analysis technique, such as change fractions from fine spatial resolution data for comparison with coarse spatial resolution maps. Plotting those results in bi-dimensional error space and analyzing various criteria, the “lowest cost”, according to a user defined (here hyperbolic) cost function, was found most useful. In general, we found a poor match between Landsat and MODIS-based change maps which, besides obvious differences in the capabilities to detect change, is likely related to change detection errors in both data sets.

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Konopska, Beata, and Mirosław Krukowski. "Image data paradox – on the impact of the development of image-based remote sensing on the maps’ content in the Eastern Bloc. The case of Poland." Polish Cartographical Review 50, no. 4 (December 1, 2018): 211–22. http://dx.doi.org/10.2478/pcr-2018-0016.

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Abstract The authors examine the impact of the development of image-based remote sensing systems on the activities of state administrations in the cartographic production and making of geographical information publicly available in the Eastern Bloc countries. A convergence of cartography, secrecy, and power occurred during the Cold War. Through investigation of facts relevant to the acquisition image data of the Earth surface performed by the USA and the USSR, it aims to examine the key questions of why the logic behind the development of cartography in the Eastern Bloc countries after World War II was distorted. The lack of logic was reflected in the fact that the amount of information actually presented on maps decreased with an increase in the information about the surface of the Earth acquired by the means of remote sensing systems. It was suggested that image data in the member states of the Eastern Bloc, in spite of their restricted use and a drop in the informational value of maps, was the main factor behind the creation, detail, and geometric accuracy of civilian maps. Proving this thesis involved analyzing the correlations between the achievements in the field of remote sensing and the quality of maps developed during the Cold War in the Eastern Bloc states.

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Zhang, Wei, Ping Tang, and Lijun Zhao. "Remote Sensing Image Scene Classification Using CNN-CapsNet." Remote Sensing 11, no. 5 (February 28, 2019): 494. http://dx.doi.org/10.3390/rs11050494.

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Remote sensing image scene classification is one of the most challenging problems in understanding high-resolution remote sensing images. Deep learning techniques, especially the convolutional neural network (CNN), have improved the performance of remote sensing image scene classification due to the powerful perspective of feature learning and reasoning. However, several fully connected layers are always added to the end of CNN models, which is not efficient in capturing the hierarchical structure of the entities in the images and does not fully consider the spatial information that is important to classification. Fortunately, capsule network (CapsNet), which is a novel network architecture that uses a group of neurons as a capsule or vector to replace the neuron in the traditional neural network and can encode the properties and spatial information of features in an image to achieve equivariance, has become an active area in the classification field in the past two years. Motivated by this idea, this paper proposes an effective remote sensing image scene classification architecture named CNN-CapsNet to make full use of the merits of these two models: CNN and CapsNet. First, a CNN without fully connected layers is used as an initial feature maps extractor. In detail, a pretrained deep CNN model that was fully trained on the ImageNet dataset is selected as a feature extractor in this paper. Then, the initial feature maps are fed into a newly designed CapsNet to obtain the final classification result. The proposed architecture is extensively evaluated on three public challenging benchmark remote sensing image datasets: the UC Merced Land-Use dataset with 21 scene categories, AID dataset with 30 scene categories, and the NWPU-RESISC45 dataset with 45 challenging scene categories. The experimental results demonstrate that the proposed method can lead to a competitive classification performance compared with the state-of-the-art methods.

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Lu, Di, Liejun Wang, Shuli Cheng, Yongming Li, and Anyu Du. "CANet: A Combined Attention Network for Remote Sensing Image Change Detection." Information 12, no. 9 (September 7, 2021): 364. http://dx.doi.org/10.3390/info12090364.

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Change detection (CD) is one of the essential tasks in remote sensing image processing and analysis. Remote sensing CD is a process of determining and evaluating changes in various surface objects over time. The impressive achievements of deep learning in image processing and computer vision provide an innovative concept for the task of CD. However, existing methods based on deep learning still have problems detecting small changed regions correctly and distinguishing the boundaries of the changed regions. To solve the above shortcomings and improve the efficiency of CD networks, inspired by the fact that an attention mechanism can refine features effectively, we propose an attention-based network for remote sensing CD, which has two important components: an asymmetric convolution block (ACB) and a combined attention mechanism. First, the proposed method extracts the features of bi-temporal images, which contain two parallel encoders with shared weights and structures. Then, the feature maps are fed into the combined attention module to reconstruct the change maps and obtain refined feature maps. The proposed CANet is evaluated on the two publicly available datasets for challenging remote sensing image CD. Extensive empirical results with four popular metrics show that the designed framework yields a robust CD detector with good generalization performance. In the CDD and LEVIR-CD datasets, the F1 values of the CANet are 3.3% and 1.3% higher than those of advanced CD methods, respectively. A quantitative analysis and qualitative comparison indicate that our method outperforms competitive baselines in terms of both effectiveness and robustness.

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Wahyuni, Nurlita Indah. "The Utilization of ALOS PALSAR Image to Estimate Natural Forest Biomass: Case Study at Bogani Nani Wartabone National Park." Jurnal Wasian 1, no. 1 (December 26, 2014): 15. http://dx.doi.org/10.20886/jwas.v1i1.844.

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The development of remote sensing technology makes it possible to utilize its data in many sectors including forestry. Remote sensing image has been used to map land cover and monitor deforestation. This paper presents utilization of ALOS PALSAR image to estimate and map aboveground biomass at natural forest of Bogani Nani Wartabone National Park especially SPTN II Doloduo and SPTN III Maelang. We used modeling method between biomass value from direct measurement and digital number of satellite image. There are two maps which present the distribution of biomass and carbon from ALOS PALSAR image with 50 m spatial resolution. These maps were built based on backscatter polarization of HH and HV bands. The maps indicate most research area dominated with biomass stock 0-5.000 ton/ha.

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Dobermann, A., and J. L. Ping. "Geostatistical Integration of Yield Monitor Data and Remote Sensing Improves Yield Maps." Agronomy Journal 96, no. 1 (2004): 285. http://dx.doi.org/10.2134/agronj2004.0285.

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Stepanova, V. I., and A. A. Ishkhanova. "THE USE OF LOCATION MAPS BY THE REMOTE SENSING OF THE EARTH." Bulletin of Agrarian Science 1, no. 76 (March 2019): 52–57. http://dx.doi.org/10.15217/issn2587-666x.2019.1.52.

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Tenzer, Robert, Wenjin Chen, Alexey Baranov, and Mohammad Bagherbandi. "Gravity Maps of Antarctic Lithospheric Structure from Remote-Sensing and Seismic Data." Pure and Applied Geophysics 175, no. 6 (February 17, 2018): 2181–203. http://dx.doi.org/10.1007/s00024-018-1795-z.

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Dobermann, A., and J. L. Ping. "Geostatistical Integration of Yield Monitor Data and Remote Sensing Improves Yield Maps." Agronomy Journal 96, no. 1 (January 2004): 285–97. http://dx.doi.org/10.2134/agronj2004.2850.

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Journal articles on the topic 'Remote-sensing maps'

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