Готові списки джерел за темами / Multi-temporal remote sensing

Добірка наукової літератури з теми "Multi-temporal remote sensing"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Multi-temporal remote sensing"

1

Franke, Jonas, and Gunter Menz. "Multi-temporal wheat disease detection by multi-spectral remote sensing." Precision Agriculture 8, no. 3 (June 24, 2007): 161–72. http://dx.doi.org/10.1007/s11119-007-9036-y.

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Fu, N., L. Sun, H. Z. Yang, J. Ma, and B. Q. Liao. "RESEARCH ON MULTI-SOURCE SATELLITE IMAGE DATABASE MANAGEMENT SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 565–68. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-565-2020.

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Анотація:
Abstract. For the exploration and analysis of electricity, it is necessary to continuously acquire multi-star source, multi-temporal, multi-level remote sensing images for analysis and interpretation. Since the overall data has a variety of features, a data structure for multi-sensor data storage is proposed. On the basis of solving key technologies such as real-time image processing and analysis and remote sensing image normalization processing, the .xml file and remote sensing data geographic information file are used to realize effective organization between remote sensing data and remote sensing data. Based on GDAL design relational database, the formation of a relatively complete management system of data management, shared publishing and application services will maximize the potential value of remote sensing images in electricity remote sensing.
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3

Zhu, Lilu, Xiaolu Su, Yanfeng Hu, Xianqing Tai, and Kun Fu. "A Spatio-Temporal Local Association Query Algorithm for Multi-Source Remote Sensing Big Data." Remote Sensing 13, no. 12 (June 14, 2021): 2333. http://dx.doi.org/10.3390/rs13122333.

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It is extremely important to extract valuable information and achieve efficient integration of remote sensing data. The multi-source and heterogeneous nature of remote sensing data leads to the increasing complexity of these relationships, and means that the processing mode based on data ontology cannot meet requirements any more. On the other hand, the multi-dimensional features of remote sensing data bring more difficulties in data query and analysis, especially for datasets with a lot of noise. Therefore, data quality has become the bottleneck of data value discovery, and a single batch query is not enough to support the optimal combination of global data resources. In this paper, we propose a spatio-temporal local association query algorithm for remote sensing data (STLAQ). Firstly, we design a spatio-temporal data model and a bottom-up spatio-temporal correlation network. Then, we use the method of partition-based clustering and the method of spectral clustering to measure the correlation between spatio-temporal correlation networks. Finally, we construct a spatio-temporal index to provide joint query capabilities. We carry out local association query efficiency experiments to verify the feasibility of STLAQ on multi-scale datasets. The results show that the STLAQ weakens the barriers between remote sensing data, and improves their application value effectively.
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4

Smith, A. M., D. J. Major, C. W. Lindwall, and R. J. Brown. "Multi-Temporal, Multi-Sensor Remote Sensing for Monitoring Soil Conservation Farming." Canadian Journal of Remote Sensing 21, no. 2 (June 1995): 177–84. http://dx.doi.org/10.1080/07038992.1995.10874611.

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5

Li, Yinshuai, Chunyan Chang, Zhuoran Wang, Tao Li, Jianwei Li, and Gengxing Zhao. "Identification of Cultivated Land Quality Grade Using Fused Multi-Source Data and Multi-Temporal Crop Remote Sensing Information." Remote Sensing 14, no. 9 (April 27, 2022): 2109. http://dx.doi.org/10.3390/rs14092109.

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To explore the fast, accurate, and efficient remote sensing identification method of cultivated land quality, this study took Shandong Province as the study area, and used measured data to carry out the soil quality evaluation based on conventional GIS. On this basis, MODIS sequence images were used as remote sensing data sources, and multi-source data such as topography, meteorology, and statistical yearbook were fused. Then, according to the Pressure-State-Response framework, we constructed three kinds of characteristic indicators through distinguishing crop rotation types and fusing remote sensing data. Finally, the soil quality grade was identified by the random forest method, and the accuracy analysis was carried out. The results showed that the NDVI peak values of double-season crops are in mid-April and mid-August, and one-season crops are in mid-August. Through evaluation, soil quality was divided into three categories, with six grades. Through principal component analysis, each soil status indicator contains two to three principal components, and each principal component contains five to eight temporal crop remote sensing information. After distinguishing crop rotation types and fusing remote sensing images, the identification accuracy of soil quality is significantly improved. The overall accuracy is 79.18%, 86.12%, and 93.65%, and the Kappa coefficient is 0.66, 0.77, and 0.90, respectively. This research developed an automatic identification method for cultivated land quality grade, and it proved that distinguishing crop rotation types and fusing multi-temporal crop remote sensing information are effective ways to improve identification accuracy.
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6

Liu, J., L. Liu, X. Xing, X. Zheng, Y. Gao, Q. Xu, and J. Du. "MULTI-TIER STORAGE MANAGEMENT AND APPLICATION OF REMOTE SENSING IMAGE DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 31, 2022): 1229–34. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-1229-2022.

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Анотація:
Abstract. With the rapid development of the remote sensing platform and sensor technology, remote sensing image data presents the typical characteristics of the massive complex, multi-source heterogeneous, spatial-temporal intensive, which puts forward higher requirements for data storage management efficiency and real-time online service capability. Combined with the demand for remote sensing image data, the multi-tier migration strategy and approach based on the thermal evaluation model of remote sensing image data are proposed, considering the file size and data activity of remote sensing image data. The linkage between local storage cluster and cloud storage implements multi-tier migration and dynamic flow of remote sensing image data, improves the utilization of storage devices and the rationality of storage resource allocation, and enhances the capability of fast, dynamic, and real-time online service of remote sensing image data.
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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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8

Huang, Fenghua, Zhengyuan Mao, and Wenzao Shi. "ICA-ASIFT-Based Multi-Temporal Matching of High-Resolution Remote Sensing Urban Images." Cybernetics and Information Technologies 16, no. 5 (October 1, 2016): 34–49. http://dx.doi.org/10.1515/cait-2016-0050.

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Abstract While SIFT (Scale Invariant Feature Transform) features are used to match High-Resolution (HR) remote sensing urban images captured at different phases with large scale and view variations, feature points are few and the matching accuracy is low. Although replacing SIFT with fully affine invariant features ASIFT (Affine-SIFT) can increase the number of feature points, it results in matching inefficiency and a non-uniform distribution of matched feature point pairs. To address these problems, this paper proposes the novel matching method ICA-ASIFT, which matches HR remote sensing urban images captured at different phases by using an Independent Component Analysis algorithm (ICA) and ASIFT features jointly. First, all possible affine deformations are modeled for the image transform, extracting ASIFT features of remote sensing images captured at different times. The ICA algorithm reduces the dimensionality of ASIFT features and improves matching efficiency of subsequent ASIFT feature point pairs. Next, coarse matching is performed on ASIFT feature point pairs through the algorithms of Nearest Vector Angle Ratio (NVAR), Direction Difference Analysis (DDA) and RANdom SAmple Consensus (RANSAC), eliminating apparent mismatches. Then, fine matching is performed on rough matched point pairs using a Neighborhoodbased Feature Graph Matching algorithm (NFGM) to obtain final ASIFT matching point pairs of remote sensing images. Finally, final matching point pairs are used to compute the affine transform matrix. Matching HR remote sensing images captured at different phases is achieved through affine transform. Experiments are used to compare the performance of ICA-ASFIT and three other algorithms (i.e., Harris- SIFT, PCA-SIFT, TD-ASIFT) on HR remote sensing images captured at different times in different regions. Experimental results show that the proposed ICA-ASFIT algorithm effectively matches HR remote sensing urban images and outperforms other algorithms in terms of matching accuracy and efficiency.
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Xia, Liheng, and Xueying Wu. "A review of hyperspectral remote sensing of crops." E3S Web of Conferences 338 (2022): 01029. http://dx.doi.org/10.1051/e3sconf/202233801029.

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Анотація:
With the development of space science and technology, various resource monitoring environmental satellites provide multi-platform, multi-spectral, multi-temporal and wide-range real-time information for the study of surface dynamic changes, and remote sensing technology has become a powerful technical means for human to study the earth’s resources and environment, while high-resolution and hyperspectral remote sensing has become the main source for fruit tree growth monitoring and fruit quality detection acquisition. This paper has the following aspects to introduce the current situation of application of high-resolution and hyperspectral remote sensing data.
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Liu, C., X. Zhou, Y. Zhou, and A. Akbar. "MULTI-TEMPORAL MONITORING OF URBAN RIVER WATER QUALITY USING UAV-BORNE MULTI-SPECTRAL REMOTE SENSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 22, 2020): 1469–75. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1469-2020.

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Анотація:
Abstract. Water quality is an important index of the ecological environment, which changes rapidly and needs to be monitored chronically. In urban ecological environment, water quality problem is not only more serious, but also more complex in time and space. Remote sensing water quality monitoring can cover a large area in a short time. Therefore, remote sensing can be adopted to make up for the shortcomings of traditional water quality monitoring methods in space coverage and temporal resolution. In order to monitor the narrow rivers in urban area, low altitude remote sensing is needed. This paper proposes a multi-spectral water quality monitoring method based on UAV platform, which can quickly monitor an entire urban water area and conduct multi-temporal observation for key indices of water quality within one day. It is helpful to find and locate the polluted areas which affect the water environment quickly. Also, it can show the changes of water quality on the time axis. The result can provide a decision-making basis for water environment treatment.
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Більше джерел

Дисертації з теми "Multi-temporal remote sensing"

1

Saha, Sudipan. "Advanced deep learning based multi-temporal remote sensing image analysis." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/263814.

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Анотація:
Multi-temporal image analysis has been widely used in many applications such as urban monitoring, disaster management, and agriculture. With the development of the remote sensing technology, the new generation remote sensing satellite images with High/ Very High spatial resolution (HR/VHR) are now available. Compared to the traditional low/medium spatial resolution images, the detailed information of ground objects can be clearly analyzed in the HR/VHR images. Classical methods of multi-temporal image analysis deal with the images at pixel level and have worked well on low/medium resolution images. However, they provide sub-optimal results on new generation images due to their limited capability of modeling complex spatial and spectral information in the new generation products. Although significant number of object-based methods have been proposed in the last decade, they depend on suitable segmentation scale for diverse kinds of objects present in each temporal image. Thus their capability to express contextual information is limited. Typical spatial properties of last generation images emphasize the need of having more flexible models for object representation. Another drawback of the traditional methods is the difficulty in transferring knowledge learned from one specific problem to another. In the last few years, an interesting development is observed in the machine learning/computer vision field. Deep learning, especially Convolution Neural Networks (CNNs) have shown excellent capability to capture object level information and in transfer learning. By 2015, deep learning achieved state-of-the-art performance in most computer vision tasks. Inspite of its success in computer vision fields, the application of deep learning in multi-temporal image analysis saw slow progress due to the requirement of large labeled datasets to train deep learning models. However, by the start of this PhD activity, few works in the computer vision literature showed that deep learning possesses capability of transfer learning and training without labeled data. Thus, inspired by the success of deep learning, this thesis focuses on developing deep learning based methods for unsupervised/semi-supervised multi-temporal image analysis. This thesis is aimed towards developing methods that combine the benefits of deep learning with the traditional methods of multi-temporal image analysis. Towards this direction, the thesis first explores the research challenges that incorporates deep learning into the popular unsupervised change detection (CD) method - Change Vector Analysis (CVA) and further investigates the possibility of using deep learning for multi-temporal information extraction. The thesis specifically: i) extends the paradigm of unsupervised CVA to novel Deep CVA (DCVA) by using a pre-trained network as deep feature extractor; ii) extends DCVA by exploiting Generative Adversarial Network (GAN) to remove necessity of having a pre-trained deep network; iii) revisits the problem of semi-supervised CD by exploiting Graph Convolutional Network (GCN) for label propagation from the labeled pixels to the unlabeled ones; and iv) extends the problem statement of semantic segmentation to multi-temporal domain via unsupervised deep clustering. The effectiveness of the proposed novel approaches and related techniques is demonstrated on several experiments involving passive VHR (including Pleiades), passive HR (Sentinel-2), and active VHR (COSMO-SkyMed) datasets. A substantial improvement is observed over the state-of-the-art shallow methods.
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2

Mahlayeye, Mbali. "Single and multi-temporal assessment approach of natural resources using remote sensing." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/65908.

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Анотація:
The study area of this project is located in Makhado Municipality, Limpopo, South Africa. The Limpopo Province is commonly known for being rich in the country?s natural resources. It has a number of villages that are characterized by rich natural resources and a well-known nature reserve, Soutpansberg Mountains. Natural resources such as water, plantations, woodlands and grasslands are commonly found in these villages and are commonly used for alleviating poverty. Rural communities in this municipality are still highly dependent on natural resources. The high dependence on these natural resources subsequently affects negatively the natural environment, e.g. processes such as land degradation. Villages in this region have limited infrastructure development that influence people?s livelihood. Infrastructure developments are commonly known for contributing to growing the economy and it will be no different if such developments are built in these villages. Therefore, it is imperative to find innovative and scientific techniques that provide information which can assist in finding ways of balancing the interaction between the environment and its people. In order to successfully do so, ways of managing and monitoring of natural resources in villages such as Makhado becomes a necessity. Land cover information is required to adequately understand the extent and status of the natural resources of the Makhado region. This information is required for effective monitoring of natural resources. With the aid of remote sensing applications, land cover studies are possible. The applications always aim to provide efficient methods using low cost or freely available data. The main objective of this study was to innovatively and accurately map the land cover classes of Makhado Municipality using Landsat imagery. The study investigated the performance of single and multi-temporal assessment approach. The study found that the results of the multi-temporal approach were more accurate compared to the single-date approach for both periods. The overall accuracy of single-date classifications were 78.1% with Kc of 0.74 and 54.3% with Kc of 0.46 respectively. The classification map results of the multi-temporal approach were 72.9% with Kc of 0.68 and 79.0% and a Kc of 0.76 respectively. The multi-temporal classification maps were used for post-classification change detection. The results of these methods illustrated the major decrease in grasslands from 2006-2009 and 2013-2015 respectively. These results assisted in making further inferences of how the drastic and severe drought that occurred in 2015 till recently had a significant impact on the land cover.
Dissertation (MSc)--University of Pretoria, 2017.
Geography, Geoinformatics and Meteorology
MSc
Unrestricted
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3

Ndegwa, Lucy W. "Monitoring the Status of Mt. Kenya Forest Using Multi-Temporal Landsat Data." Miami University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=miami1125426520.

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4

Zheng, Baojuan. "Broad-scale Assessment of Crop Residue Management Using Multi-temporal Remote Sensing Imagery." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/19201.

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Анотація:
Tillage practices have changed dramatically during the past several decades as agricultural specialists have recognized the unfavorable environmental effects of mechanized tillage. Alternatively, conservation tillage management can mitigate adverse environmental impacts of tillage, such as soil and water degradation. Adoption of conservation tillage has continued to increase since its first introduction, which raises questions of when and where it is practiced. Spatial and temporal specifics of tillage practices form important dimensions for development of effective crop management practices and policies.  Because Landsat has been and will continue to image the Earth globally, it provides opportunities for systematic mapping of crop residue cover (CRC) /tillage practices. Thus, the overall objective of this study is to develop methodologies to improve our ability to monitor crop management across different landscapes in a time-efficient and cost-effective manner using Landsat TM and ETM+ imagery, which is addressed in three separate studies. The first study found that previous efforts to estimate CRC along a continuum using Landsat-based tillage indices were unsuccessful because they neglected the key temporal changes in agricultural surfaces caused by tilling, planting, and crop emergence at the start of the growing season. The first study addressed this difficulty by extracting minimum values of multi-temporal NDTI (Normalized Difference Tillage Index) spectral profiles, designated here as the minNDTI method. The minNDTI improves crop residue estimation along a continuum (R2 = 0.87) as well as tillage classification accuracy (overall accuracy > 90%).   A second study evaluated effectiveness of the minNDTI approach for assessing CRC at multiple locations over several years, and compared minNDTI to hyperspectral tillage index (CAI), and the ASTER tillage index (SINDRI). The minNDTI is effective across four different locations (R2 of 0.56 ~ 0.93). The third study, built upon the second study, addressed the Landsat ETM+ missing data issue, and devised methodologies for producing field-level tillage data at broad scales (multiple counties).  In summary, this research demonstrates that the minNDTI technique is currently the best alternative for monitoring CRC and tillage practices from space, and provides a foundation for monitoring crop residue cover at broad spatial and temporal scales.
Ph. D.
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5

Yang, Bo. "Assimilation of multi-scale thermal remote sensing data using spatio-temporal cokriging method." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1377868463.

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6

Wheeler, Brandon Myles. "Evaluating time-series smoothing algorithms for multi-temporal land cover classification." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/74313.

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Анотація:
In this study we applied the asymmetric Gaussian, double-logistic, and Savitzky-Golay filters to MODIS time-series NDVI data to compare the capability of smoothing algorithms in noise reduction for improving land cover classification in the Great Lakes Basin, and providing groundwork to support cyanobacteria and cyanotoxin monitoring efforts. We used inter-class separability and intra-class variability, at varying levels of pixel homogeneity, to evaluate the effectiveness of three smoothing algorithms. Based on these initial tests, the algorithm which returned the best results was used to analyze how image stratification by ecoregion can affect filter performance. MODIS 16-day 250m NDVI imagery of the Great Lakes Basin from 2001-2013 were used in conjunction with National Land Cover Database (NLCD) 2006 and 2011 data, and Cropland Data Layers (CDL) from 2008 to 2013 to conduct these evaluations. Inter-class separability was measured by Jeffries-Matusita (JM) distances between selected land cover classes (both general classes and specific crops), and intra-class variability was measured by calculating simple Euclidean distance for samples within a land cover class. Within the study area, it was found that the application of a smoothing algorithm can significantly reduce image noise, improving both inter-class separability and intra-class variability when compared to the raw data. Of the three filters examined, the asymmetric Gaussian filter consistently returned the highest values of interclass separability, while all three filters performed very similarly for within-class variability. The ecoregion analysis based on the asymmetric Gaussian dataset indicated that the scale of study area can heavily impact within-class separability. The criteria we established have potential for furthering our understanding of the strengths and weaknesses of different smoothing algorithms, thereby improving pre-processing decisions for land cover classification using time-series data.
Master of Science
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7

Zhang, Xiaohu, and 张啸虎. "Automatic detection of land cover changes using multi-temporal polarimetric SAR imagery." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/193496.

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Анотація:
Dramatic land-cover changes have occurred in a broad range of spatial and temporal scales over the last decades. Satellite remote sensing, which can observe the earth's surface in a consistent manner, has been playing an important role in monitoring and evaluating land-cover changes. Meanwhile, optical remote sensing, a common approach to acquiring land-cover information, is limited by weather conditions and thus is greatly constrained in areas with frequent cloud cover and rainfall. Recent advances in polarimetric SAR (PolSAR) provide a promising means to extract timely information of land-cover changes regardless of weather conditions. SAR satellite can pass through an area from different orbits, namely ascending orbit and descending orbit. The PolSAR images from the same orbit will have similar backscattering even with different incident angles. But if images are acquired from different orbits, the backscattering will vary greatly, which causes many difficulties to land cover change detection. The proposed algorithms in this study can perform land cover change detection in three situations: 1) repeat-pass images (image from the same orbit and with same incident angle, 2) images from the same orbit but with different incident angle, and 3) images from different orbits. Using images from different orbits will largely reduce the monitoring interval which is important in the surveillance of natural disasters. The present study proposes 1) a sub-pixel automatic registration technique, 2) an automatic change detection technique and 3) an iterative framework to process a time series of PolSAR images that can be applied to the PolSAR images from different orbits. Firstly, automatic registration is crucial to the change detection task because a small positional error will largely degrade the accuracy of change detection. The automatic registration technique is based on the multi-scale Harris corner detector. To improve the efficiency and robustness, the orientation angle differencing method is proposed to reject outliers. This differencing method has been proved effective even in the experiment of using PolSAR images from different orbits when less than 5% of the feature point matches are correct. Secondly, the change detection technique can automatically detect land-cover conversions and classify the newly input image. Hierarchical segmentation has been applied in the change detection which generates objects within the constraint of the previous classification map. Multivariate kernel density estimation is applied to classify newly input PolSAR image. The experiments show that the proposed change detection technique can mitigate the effect of polarimetric orientation shift when the PolSAR images are from different orbits, and it can achieve high accuracy even when complex local deformation is appeared. Lastly, the iterative framework, which integrates the automatic registration and automatic change detection techniques, is proposed to process a time series of PolSAR images. In the iterative process, no obvious decrease of classification accuracy is observed. Therefore, the proposed framework provides a potential treatment to derive land-cover dynamics from a time series of PolSAR images from different orbits.
published_or_final_version
Urban Planning and Design
Doctoral
Doctor of Philosophy
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8

Shrestha, Bijay. "Parallel compositing of multi-temporal satellite imagery using temporal map algebra." Master's thesis, Mississippi State : Mississippi State University, 2005. http://sun.library.msstate.edu/ETD-db/ETD-browse/browse.

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9

Ren, Jie. "Multi-temporal Remote Sensing of Changing Agricultural Land Uses within the Midwestern Corn Belt, 2001-2015." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/81559.

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Анотація:
The Midwest US has experienced significant changes in agricultural land use and management practices in recent decades. Cropland expansion, crop rotation change, and crop phenology changes could lead to divergent environmental impacts on linked ecosystems. The overall objective is to examine agricultural land use and management changes and their impacts on water quality in the Midwest US, which is addressed in three separate studies. The first study examined spatial and temporal dimensions of agricultural land use dynamics in east-central Iowa, 2001-2012. Results of this study indicated that increases in corn production in response to US biofuel policies had been achieved mainly by altering crop rotation. This study also examined spatial relationships between cultivated fields and crop rotation practices with respect to underlying soils and terrain. The most intensively cultivated land had shallower slopes and fewer pedologic limitations than others, and the corn was planted on the most suitable soils. The second study characterized key crop phenological parameters (SOS and EOS) for corn and soybean and analyzed their spatial patterns to evaluate their change trends in the Midwest US, 2001-2015. Results showed that MODIS-derived SOS and EOS values are sensitive to input time-series data and threshold values chosen for crop phenology detection. The non-winter MODIS NDVI time-series input data, and a lower threshold value (i.e., 40%) both generated better results for SOS and EOS estimates. Spatial analyses of SOS and EOS values displayed clear south-north gradient for corn and trend analyses of SOS revealed only a small percentage of counties showed statistically significant earlier trends within a user-defined temporal window (2001-2012). The third study integrated remote sensing-derived products from the first two studies with the SWAT model to assess impacts of agricultural management changes on sediment and nutrient yields for three selected watersheds in the Midwest US. With satisfied calibration and validation results for stream flows, sediment and nutrient yields, considered under differing management scenarios, were compared at different spatial scales. Results showed that intensive crop rotation, advancing the planting date with the same length of growing season, and longer growing seasons, dramatically increased, maintained, and slightly reduced sediment, total nitrogen, and total phosphorous yields, respectively. Overall, these studies together illuminate relationships between broad-scale agricultural policies, management decisions, and environmental impacts, and the value of multi-temporal, broad-scale, geospatial analysis of agricultural landscapes.
Ph. D.
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10

Formigoni, Mileide de Holanda. "Análise multi-temporal da vegetação na região nordeste do Brasil através do EVI do sensor MODIS." Universidade Federal do Espírito Santo, 2008. http://repositorio.ufes.br/handle/10/6589.

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Анотація:
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The Brazilian Northeast (NEB) region presented different vegetation types that are essential component of its ecosystem. With remote sensing techniques it is possible, for example, to analyzed variations in vegetation community and alterations in vegetation phenological. Analysis the main objective of this work is to evaluate the temporal behavior of the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), of different vegetation types in the NEB over period between February/2000 and July/2006. The study area was the NEB, where it was used to characterize the vegetations types a vegetation map of Brazil, in the 1:5,000,000 scale from Brazilian Institute of Geography and Statistics (IBGE). A total of 140 cloud-free EVI images with spatial resolution 250 m were acquired from National Aeronautics and Space Administration (NASA). Four CBERS-2/CCD images spatian resolution 20 m were also acquired from National Institute for Espace Research (INPE) to assist EVI data sample collection for each vegetation type. Precipitation data of the cities Petrolina and Pesqueira (Pernambuco), São Luiz and Carolina (Maranhão) located in regions of Caatinga, Atlantic Forest, Amazon and Savannah biome vegetation, respectively, were used to analyze its relationship with EVI from these vegetation. Also, EVI from irrigated area at Petrolina were used in these analysis. Results obtained showed that: i) multi-temporal EVI data from different vegetation types were sensitive to the vegetation phenological cycles, with minor and greater values of EVI in the periods of less and greater precipitation, respectively; ii) amazon biome vegetation presented lesser variation in the multitemporal EVI, however with greater values, justified by vegetation species the are always with green leaf; iii) Caatinga biome vegetation presented greater EVI values variation because the vegetation species on the dry periods occur total defoliation and on wet period the vegetation became green; iv) all EVI data from the vegetations studied presented significant relationship with precipitation (p-value< 0.05).
O Nordeste Brasileiro (NEB) apresenta diferentes tipos de vegetação, sendo importantes para o seu ecossistema. Com a utilização de técnicas de sensoriamento remoto é possível, por exemplo, analisar variações de comunidades de vegetação e suas alterações fenológicas. O objetivo principal deste trabalho é avaliar o comportamento temporal do Índice de Vegetação Melhorado (EVI) do sensor Spectroradiômetro de Resolução Espacial Moderada (MODIS), de diferentes tipos de vegetação do NEB no período entre fevereiro de 2000 a julho de 2006. A área de estudo foi a região do NEB, sendo utilizado para caracterização dos tipos de vegetação um mapa de vegetação na escala de 1:5.000.000 do Instituto Brasileiro de Geografia e Estatística (IBGE). Um total de 140 imagens EVI livres de nuvens com resolução espacial de 250 m foram adquiridas da Agência Nacional Aeroespacial Norteamericana (NASA). Quatro imagens CBERS-2/CCD com resolução espacial de 20 m foram também adquiridas do Instituto Nacional de Pesquisas Espaciais (INPE) para auxiliar na coleta das amostras de dados de EVI dos diferentes tipos de vegetação. Dados de precipitação das cidades de Petrolina e Pesqueira (Pernambuco), Barra do Corda e Carolina (Maranhão) localizadas nas regiões de vegetação do tipo Caatinga, Floresta Atlântica, Amazônia e Cerrado, respectivamente, foram utilizados para avaliar sua relação com os dados de EVI sob estas vegetações. Dados de EVI sobre área irrigada também foram utilizados para esta análise. Os resultados obtidos mostraram que: i) os dados multitemporais EVI de diferentes tipos de vegetação foram sensíveis às respectivas variações fenológicas, com os menores e maiores valores de EVI ocorrendo nos períodos de seca e chuva respectivamente; ii) a vegetação Amazônia apresentou a menor variação multitemporal dos valores de EVI, todavia apresentando os valores mais elevados, podendo-se justificar pela maior quantidade de folhas e por estarem sempre verdes; iii) a vegetação de caatinga analisada apresentou a maior variação dos valores de EVI, pois na época de seca, perde todas as folhas e na época de chuva, se torna verde devido a menor variabilidade da precipitação; iv) todos os dados de EVI das vegetações apresentaram relação significativa (valor-p<0,05) com a precipitação.
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Книги з теми "Multi-temporal remote sensing"

1

International Workshop on the Analysis of Multi-temporal Remote Sensing Images (2007 Leuven, Belgium). 2007 International Workshop on the Analysis of Multi-Temporal Remote Sensing Images: Leuven, Belgium, 18 - 20 July 2007. Piscataway, NJ: IEEE, 2007.

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International Workshop on the Analysis of Multi-temporal Remote Sensing Images (3rd 2005 Biloxi, Miss.). Proceedings of the Third International Workshop on the Analysis of Multi-temporal Remote Sensing Images: Multi Temp 2005, 16-18 May 2005, Beau Rivage Resort and Casino, Biloxi, Mississippi USA. Edited by King Roger L, Younan Nicolas H, and Institute of Electrical and Electronics Engineers. Piscataway, N.J: IEEE, 2005.

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Lorenzo, Bruzzone, and Smits Paul, eds. Proceedings of the First International Workshop on the Analysis of Multi-temporal Remote Sensing Images: University of Trento, Italy, 13-14 September 2001. River Edge, N.J: World Scientific, 2002.

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International, Workshop on the Analysis of Multi-Temporal Remote Sensing Images (2nd 2003 Ispra Italy). Proceedings of the Second International Workshop on the Analysis of Multi-Temporal Remote Sensing Images: Multitemp 2003, Joint Research Centre, Ispra, Italy, 16-18 July 2003. [River Edge] N.J: World Scientific, 2004.

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Proceedings of the Second International Workshop on the Analysis of Multi-Temporal Remote Sensing Images: Multitemp 2003, Joint Research Centre, Ispra, Italy, 16-18 July 2003. Singapore: World Scientific, 2005.

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US GOVERNMENT. Proceedings of the Third International Workshop on the Analysis of Multi-Temporal Remote Sensing Images: Multi Temp 2005, 16-18 May 2005, Beau Rivage. Institute of Electrical & Electronics Enginee, 2005.

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(Editor), Lorenzo Bruzzone, and Paul C. Smits (Editor), eds. Analysis of Multi-Temporal Remote Sensing Images: Proceedings of Multitemp 2001 University of Trento, Italy 13-14 September 2001 (Remote Sensing). World Scientific Publishing Company, 2002.

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(Editor), Paul C. Smits, and Lorenzo Bruzzone (Editor), eds. Analysis Of Multi-Temporal Remote Sensing Images: Proceedings Of The Second International Workshop on the Joint Research Centre Ispra, Italy 16-18 July 2003. World Scientific Publishing Company, 2004.

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Частини книг з теми "Multi-temporal remote sensing"

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Mercier, Grégoire, and Florence Tupin. "Analysis of Multi-Temporal Series and Change Detection." In Remote Sensing Imagery, 203–21. Hoboken, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118899106.ch8.

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Meghanadh, Devara, and Ramji Dwivedi. "Multi-Temporal SAR Interferometry." In Spaceborne Synthetic Aperture Radar Remote Sensing, 287–311. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003204466-13.

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Refice, Alberto, Annarita D’Addabbo, Francesco Paolo Lovergine, Khalid Tijani, Alberto Morea, Raffaele Nutricato, Fabio Bovenga, and Davide Oscar Nitti. "Monitoring Flood Extent and Area Through Multisensor, Multi-temporal Remote Sensing: The Strymonas (Greece) River Flood." In Flood Monitoring through Remote Sensing, 101–13. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63959-8_5.

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Liang, Hongyu, Wenbin Xu, Xiaoli Ding, Lei Zhang, and Songbo Wu. "Urban Sensing with Spaceborne Interferometric Synthetic Aperture Radar." In Urban Informatics, 345–65. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_21.

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AbstractSynthetic aperture radar (SAR) and interferometric SAR (InSAR) are state-of-the-art radar remote sensing technologies and are very useful for urban remote sensing. The technologies have some very special characteristics compared to optical remote sensing and are especially advantageous in cloudy regions due to the ability of the microwave radar signals used by the current SAR sensors to penetrate clouds. This chapter introduces the basic concepts of SAR, differential InSAR, and multi-temporal InSAR, and their typical applications in urban remote sensing. Examples of applying the various InSAR techniques in generating DEMs and monitoring ground and infrastructure deformation are given. The capabilities and limitations of InSAR techniques in urban remote sensing are briefly discussed.
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Mustafa, Yaseen T. "Multi-temporal Satellite Data for Land Use/Cover (LULC) Change Detection in Zakho, Kurdistan Region-Iraq." In Environmental Remote Sensing and GIS in Iraq, 161–80. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21344-2_7.

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Pacifici, Fabio, Georgios K. Ouzounis, Lionel Gueguen, Giovanni Marchisio, and William J. Emery. "Very High Spatial Resolution Optical Imagery: Tree-Based Methods and Multi-temporal Models for Mining and Analysis." In Mathematical Models for Remote Sensing Image Processing, 81–135. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66330-2_3.

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Gamon, John A., Ran Wang, Hamed Gholizadeh, Brian Zutta, Phil A. Townsend, and Jeannine Cavender-Bares. "Consideration of Scale in Remote Sensing of Biodiversity." In Remote Sensing of Plant Biodiversity, 425–47. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33157-3_16.

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AbstractA coherent and effective remote sensing (RS) contribution to biodiversity monitoring requires careful consideration of scale in all its dimensions, including spatial, temporal, spectral, and angular, along with biodiversity at different levels of biological organization. Recent studies of the relationship between optical diversity (spectral diversity) and biodiversity reveal a scale dependence that can be influenced by the RS methods used, vegetation type, and degree and nature of disturbance. To better understand these issues, we call for multi-scale field campaigns that test the effect of sampling scale, vegetation type, and degree of disturbance on the ability to detect different kinds of biodiversity, along with the development of improved models that incorporate both physical and biological principles as well as ecological and evolutionary theory. One goal of these studies would be to more closely match instrumentation and sampling scales to biological definitions of biodiversity and so improve optical diversity (spectral diversity) as a proxy for biodiversity. The ultimate goal would be to design and implement a truly effective, “scale-aware” global biodiversity monitoring system employing RS methods. Such a system could improve our understanding of the distribution and functional importance of biodiversity and enhance our ability to manage ecosystems for resilience and sustainability in a changing world.
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Alkaradaghi, Karwan, Salahalddin S. Ali, Nadhir Al-Ansari, and Jan Laue. "Land Use Classification and Change Detection Using Multi-temporal Landsat Imagery in Sulaimaniyah Governorate, Iraq." In Advances in Remote Sensing and Geo Informatics Applications, 117–20. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01440-7_28.

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Lin, Yi, Yuan Hu, and Jie Yu. "Analysis of Shanghai Urban Expansion Based on Multi-temporal Remote Sensing Images." In Sustainable Development of Water and Environment, 37–45. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16729-5_5.

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Snehmani, Mritunjay Kumar Singh, Krishnanjan Pakrasi, Anshuman Bhardwaj, and A. Ganju. "Monitoring the Status of Siachen Glacier Using Multi Temporal Remote Sensing Approach." In Geostatistical and Geospatial Approaches for the Characterization of Natural Resources in the Environment, 887–91. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-18663-4_137.

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Тези доповідей конференцій з теми "Multi-temporal remote sensing"

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Roerink, G. J., M. H. G. I. Danes, O. Gomez Prieto, A. J. W. de Wit, and A. J. H. van Vliet. "Deriving plant phenology from remote sensing." In 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp). IEEE, 2011. http://dx.doi.org/10.1109/multi-temp.2011.6005098.

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Resta, Salvatore, Nicola Acito, Marco Diani, and Giovanni Corsini. "Unsupervised mis-registration noise estimation in multi-temporal hyperspectral images." In SPIE Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2012. http://dx.doi.org/10.1117/12.974216.

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Bovenga, Fabio, Alberto Refice, Guido Pasquariello, Davide O. Nitti, and Raffaele Nutricato. "Corner reflectors and multi-temporal SAR inteferometry for landslide monitoring." In SPIE Remote Sensing, edited by Claudia Notarnicola, Simonetta Paloscia, and Nazzareno Pierdicca. SPIE, 2014. http://dx.doi.org/10.1117/12.2066833.

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Peng, Chen, Juan Wang, and Donglin Li. "Oil platform investigation by multi-temporal SAR remote sensing image." In SPIE Remote Sensing. SPIE, 2011. http://dx.doi.org/10.1117/12.897937.

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Somers, Ben, and Gregory P. Asner. "Mapping tropical rainforest canopies using multi-temporal spaceborne imaging spectroscopy." In SPIE Remote Sensing, edited by Christopher M. U. Neale and Antonino Maltese. SPIE, 2013. http://dx.doi.org/10.1117/12.2028508.

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Elsner, Paul. "Multi-temporal airborne remote sensing of intertidal sediment dynamics." In SPIE Europe Remote Sensing, edited by Ulrich Michel and Daniel L. Civco. SPIE, 2009. http://dx.doi.org/10.1117/12.830672.

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Yule, Ian J., Reddy R. Pullanagari, and G. Kereszturi. "Detecting subtle environmental change: a multi-temporal airborne imaging spectroscopy approach." In SPIE Remote Sensing, edited by Christopher M. U. Neale and Antonino Maltese. SPIE, 2016. http://dx.doi.org/10.1117/12.2240418.

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Bovenga, Fabio, Alberto Refice, Antonella Belmonte, and Guido Pasquariello. "Comparative analysis of recent satellite missions for multi-temporal SAR interferometry." In SPIE Remote Sensing, edited by Claudia Notarnicola, Simonetta Paloscia, Nazzareno Pierdicca, and Edward Mitchard. SPIE, 2016. http://dx.doi.org/10.1117/12.2240490.

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Betbeder, Julie, Sébastien Rapinel, Thomas Corpetti, Eric Pottier, Samuel Corgne, and Laurence Hubert Moy. "Multi-temporal classification of TerraSAR-X data for wetland vegetation mapping." In SPIE Remote Sensing, edited by Christopher M. U. Neale and Antonino Maltese. SPIE, 2013. http://dx.doi.org/10.1117/12.2029092.

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Bovenga, Fabio, Davide Oscar Nitti, Alberto Refice, Raffaele Nutricato, and Maria Teresa Chiaradia. "Multi-temporal DInSAR analysis with X-band high resolution SAR data: examples and potential." In Remote Sensing, edited by Claudia Notarnicola. SPIE, 2010. http://dx.doi.org/10.1117/12.866459.

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Звіти організацій з теми "Multi-temporal remote sensing"

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Lee Spangler, Lee A. Vierling, Eva K. Stand, Andrew T. Hudak, Jan U.H. Eitel, and Sebastian Martinuzzi. QUANTIFYING FOREST ABOVEGROUND CARBON POOLS AND FLUXES USING MULTI-TEMPORAL LIDAR A report on field monitoring, remote sensing MMV, GIS integration, and modeling results for forestry field validation test to quantify aboveground tree biomass and carbon. Office of Scientific and Technical Information (OSTI), April 2012. http://dx.doi.org/10.2172/1037874.

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