Accuracy Of Imagery Research Articles

Assessing Geometric and Radiometric Accuracy of DJI P4 MS Imagery Processed with Agisoft Metashape for Shrubland Mapping

The rise in inexpensive Unmanned Aerial Systems (UAS) and accessible processing software offers several advantages in forest ecosystem monitoring and management. The increase in usability of such tools can result in the simplification of workflows, potentially impacting the quality of the generated data. This study offers insights into the precision and reliability of the DJI Phantom 4 Multispectral (P4MS) UAS for mapping shrublands using the Agisoft Metashape (AM) for image processing. Geometric accuracy was evaluated using ground control points (GCPs) and different configurations. The best configuration was then used to produce orthomosaics. Subsequently, the orthomosaics were transformed into reflectance orthomosaics using various radiometric correction methods. These methods were further assessed using reference panels. The method producing the most accurate reflectance values was then chosen to create the final reflectance and Normalised Difference Vegetation Index (NDVI) maps. Radiometric accuracy was assessed through a multi-step process. Initially, precision was measured by comparing reflectance orthomosaics and NDVI derived from images taken on consecutive days. Finally, reliability was evaluated by comparing the NDVI with NDVI from a reference camera, the MicaSense Altum AL0, produced with images acquired on the same days. The results demonstrate that the P4MS is both precise and reliable for shrubland mapping. Reflectance maps and NDVI generated in AM exhibit acceptable geometric and radiometric accuracy when geometric calibration is performed with at least one GCP and radiometric calibration utilises images of reflectance panels captured at flight height, without relying on incident light sensor (ILS) data.

Design of Exterior Orientation Parameters Variation Real-Time Monitoring System in Remote Sensing Cameras

The positional accuracy of satellite imagery is essential for remote sensing cameras. However, vibrations and temperature changes during launch and operation can alter the exterior orientation parameters of remote sensing cameras, significantly reducing image positional accuracy. To address this issue, this article proposes an exterior orientation parameter variation real-time monitoring system (EOPV-RTMS). This system employs lasers to establish a full-link active optical monitoring path, which is free from time and space constraints. By simultaneously receiving star and laser signals with the star tracker, the system monitors changes in the exterior orientation parameters of the remote sensing camera in real time. Based on the in-orbit calibration geometric model, a new theoretical model and process for the calibration of exterior orientation parameters are proposed, and the accuracy and effectiveness of the system design are verified by ground experiments. The results indicate that, under the condition of a centroid extraction error of 0.1 pixel for the star tracker, the EOPV-RTMS achieves a measurement accuracy of up to 0.6″(3σ) for a single image. Displacement variation experiments validate that the measurement error of the system deviates by at most 0.05″ from the theoretical calculation results. The proposed EOPV-RTMS provides a new design solution for improving in-orbit calibration technology and image positional accuracy.

Evaluating spectral properties of invasive plant species in Kentucky recreation areas

ABSTRACT This study examines the spectral characteristics of two invasive plant species, Amur honeysuckle (Lonicera maackii) and Callery ‘Bradford’ pear (Pyrus calleryana Decne), in central Kentucky’s recreational areas. Using a combination of spectrometer, satellite (National Agriculture Imagery Program (NAIP)) and unmanned aerial vehicle (UAV) data, this study investigated the correlation between leaf locations and normalized difference vegetation index (NDVI), as well as the impact of terrain features on vegetation indices. Significant variations in NDVI and normalized difference red-edge (NDRE) index values across study areas and leaf positions were observed. Terrain attributes, particularly slope and aspect direction, significantly influenced vegetative indices in one study site. Weak to moderate correlations were found between the spectrometer and UAV-derived NDVI and NDRE values. Additionally, the study highlighted the feasibility of upscaling UAV data for NAIP imagery comparison, reaffirming the superior accuracy of UAV imagery in assessing vegetation health. This integration of remote sensing techniques offers valuable insights into the spectral characteristics of invasive plants, essential for their effective mapping and management.

Comparative Analysis of Machine Learning Techniques and Data Sources for Dead Tree Detection: What Is the Best Way to Go?

In Central Europe, the extent of bark beetle infestation in spruce stands due to prolonged high temperatures and drought has created large areas of dead trees, which are difficult to monitor by ground surveys. Remote sensing is the only possibility for the assessment of the extent of the dead tree areas. Several options exist for mapping individual dead trees, including different sources and different processing techniques. Satellite images, aerial images, and images from UAVs can be used as sources. Machine and deep learning techniques are included in the processing techniques, although models are often presented without proper realistic validation.This paper compares methods of monitoring dead tree areas using three data sources: multispectral aerial imagery, multispectral PlanetScope satellite imagery, and multispectral Sentinel-2 imagery, as well as two processing methods. The classification methods used are Random Forest (RF) and neural network (NN) in two modalities: pixel- and object-based. In total, 12 combinations are presented. The results were evaluated using two types of reference data: accuracy of model on validation data and accuracy on vector-format semi-automatic classification polygons created by a human evaluator, referred to as real Ground Truth. The aerial imagery was found to have the highest model accuracy, with the CNN model achieving up to 98% with object classification. A higher classification accuracy for satellite imagery was achieved by combining pixel classification and the RF model (87% accuracy for Sentinel-2). For PlanetScope Imagery, the best result was 89%, using a combination of CNN and object-based classifications. A comparison with the Ground Truth showed a decrease in the classification accuracy of the aerial imagery to 89% and the classification accuracy of the satellite imagery to around 70%. In conclusion, aerial imagery is the most effective tool for monitoring bark beetle calamity in terms of precision and accuracy, but satellite imagery has the advantage of fast availability and shorter data processing time, together with larger coverage areas.

Association between visuo-spatial working memory and gait motor imagery

Although motor imagery and working memory (WM) appear to be closely linked, no previous studies have demonstrated direct evidence for the relationship between motor imagery and WM abilities. This study investigated the association between WM and gait motor imagery and focused on the individual differences in young adults. This study included 33 participants (mean age: 22.2 ± 0.9 years). We used two methods to measure the ability of different WM domains: verbal and visuo-spatial WM. Gait motor imagery accuracy was assessed via the mental chronometry paradigm. We measured the times participants took to complete an actual and imagined walk along a 5 m walkway, with three different path widths. The linear mixed effects model analysis revealed that visuo-spatial WM ability was a significant predictor of the accuracy of gait motor imagery, but not of verbal WM ability. Specifically, individuals with lower visuo-spatial WM ability demonstrated more inaccuracies in the difficult path-width conditions. However, gait motor imagery was not as accurate as actual walking even in the easiest path width or in participants with high visuo-spatial WM ability. Further, visuo-spatial WM ability was significantly correlated with mental walking but not with actual walking. These results suggest that visuo-spatial WM is related to motor imagery rather than actual movement.

Quantifying Intermittent Flow Regimes in Ungauged Basins: Optimization of Remote Sensing Techniques for Ephemeral Channels Using a Flexible Statistical Classification

Intermittent and ephemeral channels are a critical component of the global hydrologic network. The dominant feature in dryland environments, ephemeral channel transmission loss facilitates aquifer recharge. Characterizing flow intermittency improves groundwater storage estimates; however, limited gauging of intermittent systems impedes this understanding. This research develops an improved classification for surface flow, optimized for ephemeral systems using linear discriminant function analysis and remotely sensed imagery. It further applies this methodology to assess temporal and spatial flow patterns across the Souss channel, an ungauged, ephemeral system in central Morocco. Linear discriminant function analysis demonstrates high predictive accuracy for Landsat imagery, with significantly improved classification success as compared to the Modified Normalized Difference Water Index. Application to the Souss channel from 1984 to 2022 points to a decreasing trend in flow frequency. Despite this change, flow events remain concentrated within the wet season, critical for regional aquifer recharge. Spatial flow characteristics further support sustained infiltration, with the majority of events focused within the upstream channel section during both dry and wet seasons. Decreased occurrence moving downstream highlights the likely impact of additional factors such as transmission loss, evapotranspiration, and agricultural abstraction contributing to channel intermittency.

Optimizing Observation Plans for Identifying Faxon Fir (Abies fargesii var. Faxoniana) Using Monthly Unmanned Aerial Vehicle Imagery

Faxon fir (Abies fargesii var. faxoniana), as a dominant tree species in the subalpine coniferous forest of Southwest China, has strict requirements regarding the temperature and humidity of the growing environment. Therefore, the dynamic and continuous monitoring of Faxon fir distribution is very important to protect this highly sensitive ecological environment. Here, we combined unmanned aerial vehicle (UAV) imagery and convolutional neural networks (CNNs) to identify Faxon fir and explored the identification capabilities of multispectral (five bands) and red-green-blue (RGB) imagery under different months. For a case study area in Wanglang Nature Reserve, Southwest China, we acquired monthly RGB and multispectral images on six occasions over the growing season. We found that the accuracy of RGB imagery varied considerably (the highest intersection over union (IoU), 83.72%, was in April and the lowest, 76.81%, was in June), while the accuracy of multispectral imagery was consistently high (IoU > 81%). In April and October, the accuracy of the RGB imagery was slightly higher than that of multispectral imagery, but for the other months, multispectral imagery was more accurate (IoU was nearly 6% higher than those of the RGB imagery for June). Adding vegetation indices (VIs) improved the accuracy of the RGB models during summer, but there was still a gap to the multispectral model. Hence, our results indicate that the optimized time of the year for identifying Faxon fir using UAV imagery is during the peak of the growing season when using a multispectral imagery. During the non-growing season, RGB imagery was no worse or even slightly better than multispectral imagery for Faxon fir identification. Our study can provide guidance for optimizing observation plans regarding data collection time and UAV loads and could further help enhance the utility of UAVs in forestry and ecological research.

Soil Moisture Detection of Rainfed Farm Through Planetscope Image

Information on soil moisture is very important in activities cultivation of crops, especially in rainfed land which is very vulnerable to climate change. The need for a method for estimating soil water content is very urgent because of the amount of time and energy required by the gravimetric method. One way that can be applied is with Planetscope Imagery. Planetscope sensors are inexpensive so they are increasingly being used for science and environmental applications, including for land cover classification. The research was carried out on rainfed land, the slopes of Mount Lawu - Central Java, at different altitudes, in the highlands, the middle plains, and the lowlands, with descriptive explorative research method, using purposive sampling to obtain an image to determine the Color Digital Number value. The results of the color digital number are used for correlation and regression analysis to determine the relationship between the actual soil moisture content and the digital number on the Planetscope image. The T-test was used to determine whether there was a significant difference between the results of the actual moisture content and the predicted moisture content. The results showed that the accuracy of Planetscope imagery in predicting soil moisture content in the highlands, middle plains, and lowlands was 86.16%; 89.07%, and 95%. These results indicate the high accuracy of PlanetScope images in predicting soil moisture content in rainfed rice fields in the toposequence of Mount Lawu, Central Java.

Convolutional Neural Network-Driven Improvements in Global Cloud Detection for Landsat 8 and Transfer Learning on Sentinel-2 Imagery

A stable and reliable cloud detection algorithm is an important step of optical satellite data preprocessing. Existing threshold methods are mostly based on classifying spectral features of isolated individual pixels and do not contain or incorporate the spatial information. This often leads to misclassifications of bright surfaces, such as human-made structures or snow/ice. Multi-temporal methods can alleviate this problem, but cloud-free images of the scene are difficult to obtain. To deal with this issue, we extended four deep-learning Convolutional Neural Network (CNN) models to improve the global cloud detection accuracy for Landsat imagery. The inputs are simplified as all discrete spectral channels from visible to short wave infrared wavelengths through radiometric calibration, and the United States Geological Survey (USGS) global Landsat 8 Biome cloud-cover assessment dataset is randomly divided for model training and validation independently. Experiments demonstrate that the cloud mask of the extended U-net model (i.e., UNmask) yields the best performance among all the models in estimating the cloud amounts (cloud amount difference, CAD = −0.35%) and capturing the cloud distributions (overall accuracy = 94.9%) for Landsat 8 imagery compared with the real validation masks; in particular, it runs fast and only takes about 41 ± 5.5 s for each scene. Our model can also actually detect broken and thin clouds over both dark and bright surfaces (e.g., urban and barren). Last, the UNmask model trained for Landsat 8 imagery is successfully applied in cloud detections for the Sentinel-2 imagery (overall accuracy = 90.1%) via transfer learning. These prove the great potential of our model in future applications such as remote sensing satellite data preprocessing.

MSRA-G: Combination of multi-scale residual attention network and generative adversarial networks for hyperspectral image classification

Deep learning-based technology has been introduced to increase the classification accuracy of hyperspectral imagery (HSI). Nevertheless, it is still a challenging issue to derive a satisfying classification accuracy from limited training samples. A novel method (MSRA-G) that combines multi-scale residual attention (MSRA) with Generative Adversarial Networks (GANs) was proposed. In view of the low classification accuracy with limited training samples, the is first used to generate more separable synthetic samples. A network is then proposed to extract multi-scale context information for improving HSI classification. The proposed method constructs two multi-scale feature extraction modules to identify high-level spatial–spectral features based on the 3D–2D hybrid network. In addition, the residual connection mode and the attention mechanism are combined to establish the channel and spatial residual attention modules. Different weights are assigned to different features in the channel dimension and spatial dimension, and the features are selectively learned. Furthermore, to verify the performance of MSRA-G, experiments were carried out on three publicly available HSI datasets of Indian Pines, University of Pavia and Salinas Valley. The experimental results show that our proposed MSRA-G is superior to several popular classification models. It can still achieve satisfactory classification accuracies, even in the case of insufficient training samples.

Multiscale Convolutional Transformer for EEG Classification of Mental Imagery in Different Modalities.

A new kind of sequence-to-sequence model called a transformer has been applied to electroencephalogram (EEG) systems. However, the majority of EEG-based transformer models have applied attention mechanisms to the temporal domain, while the connectivity between brain regions and the relationship between different frequencies have been neglected. In addition, many related studies on imagery-based brain-computer interface (BCI) have been limited to classifying EEG signals within one type of imagery. Therefore, it is important to develop a general model to learn various types of neural representations. In this study, we designed an experimental paradigm based on motor imagery, visual imagery, and speech imagery tasks to interpret the neural representations during mental imagery in different modalities. We conducted EEG source localization to investigate the brain networks. In addition, we propose the multiscale convolutional transformer for decoding mental imagery, which applies multi-head attention over the spatial, spectral, and temporal domains. The proposed network shows promising performance with 0.62, 0.70, and 0.72 mental imagery accuracy with the private EEG dataset, BCI competition IV 2a dataset, and Arizona State University dataset, respectively, as compared to the conventional deep learning models. Hence, we believe that it will contribute significantly to overcoming the limited number of classes and low classification performances in the BCI system.

GOOGLE EARTH PLATFORM AS A GEOGRAPHIC INFORMATION SYSTEM AND ITS CAPABILITIES IN THE STUDY OF DISCIPLINES OF SPECIALTY 193 "GEODESY AND LAND MANAGEMENT"

With the rapid development of information technology, there is a need to revise modern education and new approaches to learning. Nowadays, anyone can take advantage of access to Earth observation satellite data. The development of satellite technologies is one of the main and pressing issues of the world. Scientists are increasingly analyzing satellite images in their work. So every year the demand in this direction increased, as did the very accuracy of satellite imagery. One of the platforms with free access to constantly updated satellite imagery is Google Earth and its more advanced versions of Google Earth Pro and Google Earth Engine, which we will consider for solving a certain number of problems. Google Earth is an unusual platform that most people perceive as a virtual globe, but if we consider it as a whole, including extended versions, then from a virtual globe we get a full-fledged geographic information system. The solution of some geodetic problems can be attributed to geoinformation analysis. In scientific sources, the topic of using Google Earth for geodetic purposes is very relevant. With the help of GIS, many issues are solved in the following industries related to 193 specialties, namely: land management; land cadastres; land inventory; mapping, including thematic; remote sensing; design and many other narrower issues. The purpose of this article is to form a list of geodetic and design tasks that can be solved using the Google Earth platform and its improved versions Google Earth Pro, Google Earth Engine when studying the disciplines of specialty 193 "Geodesy and land management". The article discusses modern approaches to learning, which will soon become a new reality for us. The most useful feature for research turned out to be time travel. With this feature, we can explore land changes since 1985, which is a hot topic in the discipline of land management. The Google Earth platform is not a new project, it has evolved since 2005, constantly optimizing its capabilities with the addition of more interesting and useful features. A lot of their own maps have already been created through this platform, and large companies use Google Earth as a demonstration of their projects. Keywords: Google Earth, geoinformation system, satellite images, platform capabilities, modern education.

An automatic method for subpixel registration of KMSS-M imagery based on coarse-resolution actualized reference

The paper describes a method for automatic subpixel-accurate geographic referencing of imagery acquired by KMSS-M with 60 m spatial resolution, based on leveraging a coarse, reconstructed, cloud-free and daily updated MODIS surface reflectance reference image. The method is based on maximizing Pearson's correlation value when determining an optimal local displacement of the distorted image fragment by comparing with the reference image. To assess the effectiveness of the method when used over continental-scale and heterogeneous areas, three experiments were carried out providing quantitative estimates of imagery registration errors: an experiment with model datasets, an experiment to estimate the absolute registration error of MODIS reference imagery, and an experiment to estimate the registration error of geocorrected KMSS-M data. Experimental evaluation of the method based on model datasets of decameter-resolution Sentinel-2 (MSI) imagery demonstrated its robustness when used over a variety of environmental conditions over a one year-long observation period. The average georeferencing error of MODIS coarse-resolution reference was shown to be less than 20 meters in Red and Near-infrared bands. Corrected KMSS-M imagery evaluation over the Russian Grain Belt within 2020 has shown, on average, the subpixel referencing accuracy both in Red and Near-infrared bands, while the average absolute georeferencing error of the original uncorrected KMSS-M imagery was shown to be about 3 kilometers. Subpixel registration accuracy of KMSS-M imagery, corrected with MODIS-based coarse-resolution reference, opens new prospects for using multi-temporal analysis of this multispectral surface reflectance data in a variety of scientific and practical applications associated with vegetation cover satellite monitoring. The technological flexibility of the method ensures its applicability to data from other satellite systems for Earth optical remote sensing.

Application of remote sensing in the description of fluvial system in dryland: A case study of Golmud distributive fluvial system, Qaidam Basin, NW China

Distributive fluvial systems (DFS) are widespread in drylands in the northwestern China. Analyzing differences in fluvial morphology in drylands is beneficial for studying ancient rock records and the extra-terrestrial surface environment. The remote sensing image, characterized by real-time and possibility of repeated observations, is a vital tool for recording and comparing fluvial systems in drylands. Satellite remote sensing technology is a method of investigating fluvial morphologies. Due to the limited accuracy of satellite imagery, there are few reports on the detailed description of the fluvial system in drylands of NW China. We analyze the pattern of fluvial morphology changes in the Golmud distributive fluvial system (DFS) in the Qaidam Basin, northwestern China, using satellite remote sensing and unmanned aerial vehicles (UAV). Firstly, we use Google Earth real-time image data, historical image data, and radar digital elevation data to extract geomorphological information; then the UAV remote sensing image data were used to interpret fluvial network information; finally, we use the gray-scale differential vector method to describe the fluvial morphologies. Three zones have been identified in the Golmud DFS: the proximal, the medial, and the distal, by comparing the differences in topographic and geomorphic characteristics, fluvial morphologies, and sedimentary characteristics of the Golmud DFS. The proximal slope is higher than the other two zones, and the geomorphic features are mainly gravel gobi. The proximal fluvial morphologies are mainly large braided rivers, and sediments are more gravelly and less sandy. The medial slope is relatively small, and the geomorphic features are mostly oasis plains. The medial fluvial morphologies are mainly meandering rivers associating with braided rivers, and sediments are more sandy and less gravelly. The distal slope is the lowest, and the geomorphic features are mostly oasis plains, lakes, and marsh plains. The distal fluvial morphologies are mainly meandering rivers, and sediments are sandy and muddy. Comparison of the DFS from proximal to medial to distal in Golmud confirmed the potential of remote sensing image technology in identifying the fluvial morphologies and sedimentary facies distribution in dryland.

PERFORMANCE EVALUATION OF BUILDING FAÇADE RECONSTRUCTION FROM UAS IMAGERY

Abstract. Unmanned Aircraft Systems (UASs) coupled with low-cost cameras are rapidly becoming a cost-effective alternative for surveying and mapping, particularly for civil, construction, and environmental engineering applications. The proliferation of UASs provide unique opportunities to map and model surfaces at unprecedented spatial and temporal resolutions. Although, UASs have been extensively evaluated for mapping and modeling, limited research has been performed on assessing the accuracy of UAS imagery for building façade reconstruction. In this study, a performance evaluation of UAS mapping for building façade reconstruction is performed. Our results suggest that there are many aspects that impact the accuracy of UAS photogrammetry for building façade reconstruction. In specific, the texture, contrast, and subtle details influence the generated point cloud, thus complicating the building façade reconstruction. The best results were obtained by strategizing the flying height and camera angle; where, the mean, median, and standard deviation for the cloud-to-cloud (C2C) absolute distances were 0.023, 0.014, and 0.023 meters, respectively, by applying a flying height of 150’ with an orbit at 80’. Therefore, it may be concluded that UAS photogrammetric mapping can meet sub-decimeter accuracy for building façade reconstruction, if proper planning, data collection and processing procedures are followed.

Position Accuracy Assessment of a UAV-Mounted Sequoia+ Multispectral Camera Using a Robotic Total Station

Remote sensing data in agriculture that are originating from unmanned aerial vehicles (UAV)-mounted multispectral cameras offer substantial information in assessing crop status, as well as in developing prescription maps for site-specific variable rate applications. The position accuracy of the multispectral imagery plays an important role in the quality of the final prescription maps and how well the latter correspond to the specific spatial characteristics. Although software products and developed algorithms are important in offering position corrections, they are time- and cost-intensive. The paper presents a methodology to assess the accuracy of the imagery obtained by using a mounted target prism on the UAV, which is tracked by a ground-based total station. A Parrot Sequoia+ multispectral camera was used that is widely utilized in agriculture-related remote sensing applications. Two sets of experiments were performed following routes that go along the north–south and east–west axes, while the cross-track error was calculated for all three planes, but also three-dimensional (3D) space. From the results, it was indicated that the camera’s D-GNSS receiver can offer imagery with a 3D position accuracy of up to 3.79 m, while the accuracy in the horizontal plane is higher compared to the vertical ones.

A Machine Learning Approach to Waterbody Segmentation in Thermal Infrared Imagery in Support of Tactical Wildfire Mapping

Wildfire research is working toward near real-time tactical wildfire mapping through the application of computer vision techniques to airborne thermal infrared (IR) imagery. One issue hindering automation is the potential for waterbodies to be marked as areas of combustion due to their relative warmth in nighttime thermal imagery. Segmentation and masking of waterbodies could help resolve this issue, but the reliance on data captured exclusively in the thermal IR and the presence of real areas of combustion in some of the images introduces unique challenges. This study explores the use of the random forest (RF) classifier for the segmentation of waterbodies in thermal IR images containing a heterogenous wildfire. Features for classification are generated through the application of contextual and textural filters, as well as normalization techniques. The classifier’s outputs are compared against static GIS-based data on waterbody extent as well as the outputs of two unsupervised segmentation techniques, based on entropy and variance, respectively. Our results show that the RF classifier achieves very high balanced accuracy (>98.6%) for thermal imagery with and without wildfire pixels, with an overall F1 score of 0.98. The RF method surpassed the accuracy of all others tested, even with heterogenous training sets as small as 20 images. In addition to assisting automation of wildfire mapping, the efficiency and accuracy of this approach to segmentation can facilitate the creation of larger training data sets, which are necessary for invoking more complex deep learning approaches.

Quantification of landscape metrics effects on downscaled urban land surface temperature accuracy of satellite imagery

Improving the spatial resolution of Land Surface Temperature (LST) obtained from satellite images is of great importance in various applications, especially in urban areas. Aiming to bridge the gap in the research background, this study attempted to quantify the effect of landscape metrics on the accuracy of urban LST downscaling. The research data included a collection of satellite images from Landsat and Terra satellites and auxiliary data of nine European cities, namely Lisbon, Madrid, Bucharest, Vienna, Prague, Paris, Copenhagen, Stockholm, and Helsinki. In the first stage, surface biophysical, topographic, and thematic property maps affecting the spatial distribution of LST were prepared. Secondly, the effect of the endogenous (influence of intra-pixel properties) and exogenous (influence of landscape metrics) variables of each surface property on LST was evaluated in warm and cold seasonal conditions. Finally, the effect of landscape metrics on the accuracy of LST downscaling from 960 m to 30 m was quantified. The results showed that the mean absolute correlation coefficient (r) values between the endogenous (exogenous) effects of surface biophysical, topographic, and thematic properties and LST in the cold season were 0.25 (0.12), 0.19 (0.06), and 0.09 (0.04), respectively. For the warm season, these values were 0.55 (0.24), 0.11 (0.03), and 0.23 (0.09), respectively. The mean r (root-mean-square error (RMSE)) between the actual LST and downscaled LST (DLST) obtained by considering only the endogenous effects in the cold and warm seasons were 0.79 (0.66) and 0.87 (1.38 °C), respectively. Considering the simultaneous effect of endogenous and exogenous variables in LST downscaling, these values reached 0.83 (0.47) and 0.91 (0.99 °C), respectively. The greatest effect of landscape metrics on the accuracy of LST downscaling was associated with built-up lands. Accounting for landscape metrics in the downscaling process significantly increases the accuracy of downscaled LST.

Using crutches during walking possibly reduces gait imagery accuracy among healthy young and older adults.

[Purpose] Although crutches are widely used in the field of rehabilitation to improve gait performance, patients usually have difficulties using them, and this may increase their risks for falls. This study aimed to define the accuracy of gait imagery during walking with and without crutches, in healthy young and older adults, using the mental chronometry method. [Participants and Methods] Overall, 99 healthy young (mean age, 20.2 ± 1.0 years) and 39 healthy older adults (mean age, 71.3 ± 2.9 years) performed the imagery and execution tasks, which involved walking through a distance of 10 meters both with and without crutches. Using the mental chronometry method, the accuracy of the motor imagery was defined as the difference between the imagery time and the actual execution time. Two-way analysis of variance and one-sample t-tests were performed to evaluate the accuracy of the gait imagery. [Results] Both the young and older adults significantly overestimated their gait speeds when using crutches; the overestimation was larger among the older adults. [Conclusion] The overestimations indicate that participants estimated their gait speeds with crutches to be faster than their actual speeds. Therefore, using crutches decreased the accuracy of gait imagery and might therefore increase an individual’s risk of falling during walking.

Geometric Accuracy Verification of GF-7 Satellite Stereo Imagery Without GCPs

The GaoFen-7 (GF-7) satellite is the China’s first civil optical stereo-mapping satellite with submeter resolution. GF-7 is also equipped with a laser altimeter, which can obtain sparse laser altimetry points (LAPs) to improve the vertical accuracy of stereo images. Verifying the geometric accuracy of GF-7 imagery without ground-control points (GCPs) is of great significance for evaluating the mapping capability of GF-7. In this study, 147 stereo images and 804 LAPs of the GF-7 satellite covering Jiangsu Province, Hebei Province, and Sichuan Province in China were selected as experimental data. Subsequently, 455 high-precision independent check points were used to verify the geometric accuracy of the GF-7 images. The results show that, without GCPs, the root-mean-square errors (RMSEs) in the horizontal direction of the experimental images in flat, hilly, mountainous, and high-mountainous terrains, and over the entire area were 4.91, 3.81, 4.38, 6.39, and 4.91 m, respectively. The RMSEs in the vertical direction were 3.42, 4.68, 3.00, 4.41, and 3.52 m, respectively. After performing combined block adjustments of the LAPs and stereo images, the RMSEs of the experimental images in the vertical direction decreased to 0.43, 0.66, 0.76, 1.14, and 0.68 m, respectively, whereas those in the horizontal direction saw only minor changes. Therefore, in the absence of GCPs, the geometric accuracy of GF-7 imagery can fully meet the accuracy requirements of China’s 1:10 000 scale stereo mapping by using the GF-7 LAPs as control points.