Application of multi-temporal high-resolution imagery and GPS in a study of the motion of a canyon rim landslide

2005 ◽  
Vol 59 (4) ◽  
pp. 212-221 ◽  
Author(s):  
John Chadwick ◽  
Stephen Dorsch ◽  
Nancy Glenn ◽  
Glenn Thackray ◽  
Karen Shilling
Keyword(s):  
High Resolution ◽  
High Resolution Imagery ◽  
Multi Temporal

scholarly journals Cloud Computation Using High-Resolution Images for Improving the SDG Indicator on Open Spaces

2020 ◽  
Vol 12 (7) ◽  
pp. 1144
Author(s):  
Rosa Aguilar ◽  
Monika Kuffer
Keyword(s):  
High Resolution ◽  
Open Space ◽  
Local Planning ◽  
Open Spaces ◽  
High Resolution Imagery ◽  
Coarse Spatial Resolution ◽  
Multi Temporal ◽  
High Resolution Images ◽  
Cloud Computation

Open spaces are essential for promoting quality of life in cities. However, accelerated urban growth, in particular in cities of the global South, is reducing the often already limited amount of open spaces with access to citizens. The importance of open spaces is promoted by SDG indicator 11.7.1; however, data on this indicator are not readily available, neither globally nor at the metropolitan scale in support of local planning, health and environmental policies. Existing global datasets on built-up areas omit many open spaces due to the coarse spatial resolution of input imagery. Our study presents a novel cloud computation-based method to map open spaces by accessing the multi-temporal high-resolution imagery repository of Planet. We illustrate the benefits of our proposed method for mapping the dynamics and spatial patterns of open spaces for the city of Kampala, Uganda, achieving a classification accuracy of up to 88% for classes used by the Global Human Settlement Layer (GHSL). Results show that open spaces in the Kampala metropolitan area are continuously decreasing, resulting in a loss of open space per capita of approximately 125 m2 within eight years.

Object-based feature selection for crop classification using multi-temporal high-resolution imagery

2018 ◽  
Vol 40 (5-6) ◽  
pp. 2053-2068 ◽  
Author(s):  
Qian Song ◽  
Mingtao Xiang ◽  
Ciara Hovis ◽  
Qingbo Zhou ◽  
Miao Lu ◽  
...  
Keyword(s):  
Feature Selection ◽  
High Resolution ◽  
High Resolution Imagery ◽  
Object Based ◽  
Multi Temporal ◽  
Selection For ◽  
Crop Classification

Photogrammetric processing and application of multi-temporal satellite imagery stereopairs

2021 ◽  
Vol 974 (8) ◽  
pp. 36-44
Author(s):  
R.V. Permyakov
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Weather Conditions ◽  
3D Models ◽  
High Resolution Imagery ◽  
Multi Temporal ◽  
Unfavourable Weather ◽  
High Resolution Satellite Imagery ◽  
Very High Resolution Imagery ◽  
Very High

Stereopairs of very-high resolution satellite imagery constitute one of the key high-accurate data sources on heights. A stereophotogrammetric technique is a key method of processing these data. Despite that a number of spacecrafts gathering very-high-resolution imagery in a stereo mode constantly increases, the area of the Earth regularly covered by such data and stored in the archives of RSD operators remains relatively small and, as a rule, is limited only to large urban agglomerations. The new collection may not suit the customer for several reasons. Firstly, the materials of the new stereo collection are more expensive than those of the archived one. Secondly, due to unfavourable weather conditions and a busy schedule of satellites, the completion of the new collection may go beyond the deadline specified by the customer. Well known and brand-new criteria to form multi-temporal, stereopairs are analyzed. The specific of photogrammetric processing multi-temporal stereopairs is demonstrated. Application of multi-temporal stereopairs is described. In conclusion it is confirmed that 3D-models and high accurate DTMs can be generated basing on stereo models from multi-temporal satellite imagery in the absence of the following data

scholarly journals Identification of Abandoned Jujube Fields Using Multi-Temporal High-Resolution Imagery and Machine Learning

2021 ◽  
Vol 13 (4) ◽  
pp. 801
Author(s):  
Xingrong Li ◽  
Chenghai Yang ◽  
Hongri Zhang ◽  
Panpan Wang ◽  
Jia Tang ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Industrial Structure ◽  
Insect Pests ◽  
Vegetation Indices ◽  
Texture Features ◽  
Good Tool ◽  
High Resolution Imagery ◽  
Abandoned Land ◽  
Multi Temporal

The jujube industry plays a very important role in the agricultural industrial structure of Xinjiang, China. In recent years, the abandonment of jujube fields has gradually emerged. It is critical to inventory the abandoned land soon after it is generated to adjust agricultural production better and prevent the negative impacts from the abandonment (such as outbreaks of diseases, insect pests, and fires). High-resolution multi-temporal satellite remote sensing images can be used to identify subtle differences among crops and provide a good tool for solving this problem. In this research, both field-based and pixel-based classification approaches using field boundaries were used to estimate the percentage of abandoned jujube fields with multi-temporal high spatial resolution satellite images (Gaofen-1 and Gaofen-6) and the Random Forest algorithm. The results showed that both approaches produced good classification results and similar distributions of abandoned fields. The overall accuracy was 91.1% for the field-based classification and 90.0% for the pixel-based classification, and the Kappa was 0.866 and 0.848 for the respective classifications. The areas of abandoned land detected in the field-based and pixel-based classification maps were 806.09 ha and 828.21 ha, respectively, accounting for 8.97% and 9.11% of the study area. In addition, feature importance evaluations of the two approaches showed that the overall importance of texture features was higher than that of vegetation indices and that 31 October and 10 November were important dates for abandoned land detection. The methodology proposed in this study will be useful for identifying abandoned jujube fields and have the potential for large-scale application.

Mapping and Monitoring of the Land Use/Cover Changes in the Wider Area of ltanos, Crete, Using Very High Resolution EO Imagery With Specific Interest in Archaeological Sites

2019 ◽  
Author(s):  
Sawyer Reid stippa ◽  
George Petropoulos ◽  
Leonidas Toulios ◽  
Prashant K. Srivastava
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Archaeological Site ◽  
Digital Photography ◽  
Archaeological Sites ◽  
Large Area ◽  
High Resolution Imagery ◽  
Site Mapping ◽  
Rule Sets

Archaeological site mapping is important for both understanding the history as well as protecting them from excavation during the developmental activities. As archaeological sites generally spread over a large area, use of high spatial resolution remote sensing imagery is becoming increasingly applicable in the world. The main objective of this study was to map the land cover of the Itanos area of Crete and of its changes, with specific focus on the detection of the landscape’s archaeological features. Six satellite images were acquired from the Pleiades and WorldView-2 satellites over a period of 3 years. In addition, digital photography of two known archaeological sites was used for validation. An Object Based Image Analysis (OBIA) classification was subsequently developed using the five acquired satellite images. Two rule-sets were created, one using the standard four bands which both satellites have and another for the two WorldView-2 images their four extra bands included. Validation of the thematic maps produced from the classification scenarios confirmed a difference in accuracy amongst the five images. Comparing the results of a 4-band rule-set versus the 8-band showed a slight increase in classification accuracy using extra bands. The resultant classifications showed a good level of accuracy exceeding 70%. Yet, separating the archaeological sites from the open spaces with little or no vegetation proved challenging. This was mainly due to the high spectral similarity between rocks and the archaeological ruins. The satellite data spatial resolution allowed for the accuracy in defining larger archaeological sites, but still was a difficulty in distinguishing smaller areas of interest. The digital photography data provided a very good 3D representation for the archaeological sites, assisting as well in validating the satellite-derived classification maps. All in all, our study provided further evidence that use of high resolution imagery may allow for archaeological sites to be located, but only where they are of a suitable size archaeological features.

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