Multi-temporal High-resolution Imagery Research Articles

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

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.

Detection of Urban Built-Up Area Change From Sentinel-2 Images Using Multiband Temporal Texture and One-Class Random Forest

Detection of urban land expansion is important for understanding the urbanization process and improving urban planning. Spatio-temporal contextual information derived from multitemporal high-resolution imagery is useful for highlighting urban land cover changes. This article proposes a new method for detecting urban built-up area change from multitemporal high spatial resolution imagery by combining spectral and spatio-temporal features. A multiband temporal texture measured using pseudo cross multivariate variogram (PCMV) is adopted to quantify the local spatio-temporal dependence between bitemporal multispectral images. The PCMV textures at multiple scales, bitemporal spectral features, and normalized difference vegetation indices are together input to an improved one-class random forest classifier for urban built-up area change mapping. The proposed method is evaluated in urban built-up area change detection using multitemporal Sentinel-2 images of Tianjin area acquired from 2015 to 2019. It is also compared with three feature combinations and an existing postclassification comparison method based on one-class support vector machine. Experimental results demonstrate that the proposed method outperformed the traditional ones, with increases of 2.15%-7.38%, 2.07%-5.45%, 1.93%-6.76%, and 5.98%-13.11% in overall accuracy. Moreover, the proposed method also achieves the best performance using the bitemporal Sentinel-2 images over the east of Beijing area. The proposed method is promising as a simple and reliable way to detect urban built-up area change with multitemporal Sentinel-2 imagery.

Building Extraction in Multitemporal High-Resolution Remote Sensing Imagery Using a Multifeature LSTM Network

Inspired by recent promising developments by deep learning networks, this letter presents a novel approach based on a multifeature long short-term memory (LSTM) network with an optimal unit number for extracting buildings from multitemporal high-resolution optical satellite imagery. The algorithm first extracts specified multifeature data of buildings, including spectral, shape, texture, and indices from multitemporal high-resolution imagery. Next, a designed LSTM network architecture with an optimal unit number is trained using the multifeature data to extract buildings at the pixel level. The proposed network is compared with popular deep learning-based networks, including VGG, U-Net, and ResNet. Finally, postprocessing (e.g., morphological algorithm) is employed to optimize the classification results produced by deep learning. The approach is validated on a newly created data set using Gaofen-2 (GF-2) imagery that contains various buildings on a campus with different materials, shapes, sizes, heights, and directions. Experimental results indicate that the proposed approach outperforms current deep learning-based methods for building extraction and has a potential for practical applications.

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

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

ABSTRACTWith high-resolution remote-sensing data, there are numerous possible features for object description, making the selection of optimal features a time-consuming and subjective process. While substantial efforts have been made to compare the utility of feature selection metrics, less attention has been paid to the efficiency of such in the context of object-based image analysis. In this study, the statistical measurement z-score was used to ensure compatibility with objects. We assessed the feasibility of a z-score method, and then ranked and reduced input features using a backward elimination technique. The results showed that separability can be efficiently estimated based on z-score values, and the near-infrared band performed the best for crop classification. A straightforward trend was observed, and the optimal feature set was created, which was a combination of spectral, temporal, texture information and vegetation indices. These features complement one another to help increase crop map accuracy. For the 40% of the entire sample sizes, the optimal feature sets produced the best trade-off between the number of inputs and classification accuracy, with the misclassification error of 7.09%. Additionally, reliable crop maps were obtained, with the overall accuracy of 92.64%, and the z-score method showed great potential for the separability of crops at object scale using remotely sensed multi-temporal data.

Fault-Tolerant Building Change Detection From Urban High-Resolution Remote Sensing Imagery

This letter proposes a novel change detection model, focusing on building change information extraction from urban high-resolution imagery. It consists of two blocks: 1) building interest-point detection, using the morphological building index (MBI) and the Harris detector; and 2) multitemporal building interest-point matching and the fault-tolerant change detection. The proposed method is insensitive to the geometrical differences of buildings caused by different imaging conditions in the multitemporal high-resolution imagery and is able to significantly reduce false alarms. Experiments showed that the proposed method was effective for building change detection from multitemporal urban high-resolution images. Moreover, the effectiveness of the algorithm was validated by comparing with the morphological change vector analysis (CVA), parcel-based CVA, and MBI-based CVA.

Clustering of Detected Changes in High-Resolution Satellite Imagery Using a Stabilized Competitive Agglomeration Algorithm

The Geospatial Change Detection and exploitation (GeoCDX) is a fully automated system for detection and exploitation of change between multitemporal high-resolution satellite and airborne images. Overlapping multitemporal images are first organized into 256 m × 256 m tiles in a global grid reference system. The system quantifies the overall amount of change in a given tile with a tile change score as an aggregation of pixel-level changes. The tiles are initially ranked by these change scores for retrieval, review, and exploitation in a Web-based application. However, the ranking does not account for the wide variety of change types that are typically observed in the top-ranked change tiles. To automatically organize the wide variety of change patterns observed in multitemporal high-resolution imagery, we perform tile clustering using the competitive agglomeration (CA) algorithm stabilized using the fuzzy c-means (FCM) algorithm. Each resulting cluster contains tiles with a visually similar type of change. By visual inspection of these tile clusters, GeoCDX users can quickly find certain types of change without having to sift through a large number of tiles initially organized solely by their tile change score, thereby reducing the time it takes for users to discover and exploit the change pattern(s) of greatest interest to a given application (e.g., urban growth, disaster assessment, facility monitoring, etc.). The tile clusters also provide a high-level overview of the various types of change that occur between the two observations. This overview is compared with a similar yet more limited view offered by a relevance feedback tool that requires a user to select sample tiles for use as samples in the reranking process.

Comparison of pixel and sub-pixel based techniques to separate Pteronia incana invaded areas using multi-temporal high resolution imagery

Remote Sensing using high resolution imagery (HRI) is fast becoming an important tool in detailed land-cover mapping and analysis of plant species invasion. In this study, we sought to test the separability of Pteronia incana invader species by pixel content aggregation and pixel content de-convolution using multi-temporal infrared HRI. An invaded area in Eastern Cape, South Africa was flown in 2001, 2004 and 2006 and HRI of 1x1m resolution captured using a DCS 420 colour infrared camera. The images were separated into bands, geo-rectified and radiometrically corrected using Idrisi Kilimanjaro GIS. Value files were extracted from the bands in order to compare spectral values for P. incana, green vegetation and bare surfaces using the pixel based Perpendicular Vegetation Index (PVI), while Constrained Linear Spectral Unmixing (CLSU) surface endmembers were used to generate sub-pixel land surface image fractions. Spectroscopy was used to validate spectral trends identified from HRI. The PVI successfully separated the multi-temporal imagery surfaces and was consistent with the unmixed surface image fractions from CLSU. Separability between the respective surfaces was also achieved using reflectance measurements.

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

Change detection techniques using co-registered high-resolution satellite imagery and archival digital aerial photographs have been used in conjunction with GPS to constrain the magnitude and timing of previously undocumented historical motion of the Salmon Falls landslide in south-central Idaho, USA. The landslide has created natural dams of Salmon Falls Creek, resulting in the development of large lakes and a potential flooding hazard. Rapid motion (cm/year–m/year) of the relatively remote landslide was first reported in 1999, but significant horizontal motion (up to 10.8 m) is demonstrated between 1990 and 1998 by measuring changes in the locations of ground control points in a time-series of images. The total (three-dimensional) motion of the landslide prior to 2002 was calculated using the horizontal (two-dimensional) velocities obtained in the image change detection study and horizontal-to-vertical ratios of motion derived for the landslide in 2003–2004 collected from a network of autonomous GPS stations. The total historical motion that was estimated using this method averages about 12 m, which is in agreement with field observations.

Recent Landslides in Salmon Creek Canyon, Idaho

Previous articleNext article No AccessRecent Landslides in Salmon Creek Canyon, IdahoCharles A. LeeCharles A. Lee Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by The Journal of Geology Volume 46, Number 4May - Jun., 1938 Article DOIhttps://doi.org/10.1086/624667 Views: 3Total views on this site Citations: 5Citations are reported from Crossref PDF download Crossref reports the following articles citing this article:Lawrence Rosenthal Eyewitness Identification and the Problematics of Blackstonian Reform of the Criminal Law, SSRN Electronic Journal (Jan 2019).https://doi.org/10.2139/ssrn.3362968Marius Necsoiu, Ronald N. McGinnis, Donald M. Hooper New insights on the Salmon Falls Creek Canyon landslide complex based on geomorphological analysis and multitemporal satellite InSAR techniques, Landslides 11, no.66 (Oct 2014): 1141–1153.https://doi.org/10.1007/s10346-014-0523-8David R. Streutker, Nancy F. Glenn, Rupesh Shrestha A Slope-based Method for Matching Elevation Surfaces, Photogrammetric Engineering & Remote Sensing 77, no.77 (Jul 2011): 743–750.https://doi.org/10.14358/PERS.77.7.743Nancy F. Glenn, David R. Streutker, D. John Chadwick, Glenn D. Thackray, Stephen J. Dorsch Analysis of LiDAR-derived topographic information for characterizing and differentiating landslide morphology and activity, Geomorphology 73, no.1-21-2 (Jan 2006): 131–148.https://doi.org/10.1016/j.geomorph.2005.07.006John Chadwick, Stephen Dorsch, Nancy Glenn, Glenn Thackray, Karen Shilling Application of multi-temporal high-resolution imagery and GPS in a study of the motion of a canyon rim landslide, ISPRS Journal of Photogrammetry and Remote Sensing 59, no.44 (Jun 2005): 212–221.https://doi.org/10.1016/j.isprsjprs.2005.02.001