Moderate Spatial Resolution Image Research Articles

Remote Sensing-Based Dynamic Monitoring of Immovable Cultural Relics, from Environmental Factors to the Protected Cultural Site: A Case Study of the Shunji Bridge

This paper explores the ability of remote sensing techniques to monitor immovable cultural relics on multiple scales. The Shunji Bridge, a destroyed cultural relic, located in the Jinjiang River Basin, Fujian Province, China, was studied in terms of the environmental factors at the macroscale and the protected cultural site at the microscale. At the macroscale, moderate spatial resolution images of the Jinjiang River Basin were processed in the Google Earth Engine (GEE) platform to extract environmental factors, such as land cover and vegetation cover. At the microscale, Google Earth time series images were used to extract attribute information to reflect the spatial and temporal changes in the Shunji Bridge before, during and after its destruction. Quantitative assessment of the Shunji Bridge was performed to assess the degree of the impacts that different factors had on the immovable cultural relic. Spatial analysis methods were applied to trace back to the source of the bridge destruction and to track the situation after the bridge was destroyed. The causes of the destruction of the bridge are revealed at both the macro- and microscales. This study provides technical support for the natural disaster risk assessment of immovable cultural relics. The findings of this research can provide suggestions for the protection of immovable cultural relics.

Improving Urban Land Cover/Use Mapping by Integrating A Hybrid Convolutional Neural Network and An Automatic Training Sample Expanding Strategy

Moderate spatial resolution (MSR) satellite images, which hold a trade-off among radiometric, spectral, spatial and temporal characteristics, are extremely popular data for acquiring land cover information. However, the low accuracy of existing classification methods for MSR images is still a fundamental issue restricting their capability in urban land cover mapping. In this study, we proposed a hybrid convolutional neural network (H-ConvNet) for improving urban land cover mapping with MSR Sentinel-2 images. The H-ConvNet was structured with two streams: one lightweight 1D ConvNet for deep spectral feature extraction and one lightweight 2D ConvNet for deep context feature extraction. To obtain a well-trained 2D ConvNet, a training sample expansion strategy was introduced to assist context feature learning. The H-ConvNet was tested in six highly heterogeneous urban regions around the world, and it was compared with support vector machine (SVM), object-based image analysis (OBIA), Markov random field model (MRF) and a newly proposed patch-based ConvNet system. The results showed that the H-ConvNet performed best. We hope that the proposed H-ConvNet would benefit for the land cover mapping with MSR images in highly heterogeneous urban regions.

A novel spatial–spectral extraction method for subpixel surface water

ABSTRACTThe spectral index provides a feasible and efficient method for extracting land surface water from satellite images. However, existing methods at the pixel scale consider only water bodies but disregard complicated mixtures. Emerging subpixel–scale methods improve water mapping a detailed description, but they still fail to achieve precise identification of various types of mixed pixels. This deficiency restricts the further development and application of the spectral index method. In this study, we proposed a novel spatial–spectral extraction method that utilizes double thresholds in describing pixels at the subpixel scale. This method combines spatial and spectral attributes to refine and decompose mixed water–land pixels. In this manner, a stepwise approach is adopted to achieve the optimal result instead of the common single–judging method. The experiments use moderate spatial–resolution images, such as those taken by Landsat–8 Operational Land Imager, GF–1’s wide field view multispectral camera and the Sentinel–2A satellite. Results confirm that the proposed method can be applied to common moderate spatial–resolution satellite images. This method achieves better accuracy than existing methods. The insensitivity of threshold selection ensures that the proposed method maintains high precision in actual applications.

Content-based search of earth observation data archives using open-access multitemporal land cover and terrain products

Public Earth Observation (EO) data archives, e.g., MODIS, Landsat, and Sentinels, are valuable sources of information for a broad range of applications. For decision-supporting applications used in urban planning, land management, and sustainable development, images covering regions similar to the study area are prerequisites for high-accuracy decision making. These desirable images cannot be quickly searched for in the EO data archives via image metadata alone but can be obtained through content-based image retrieval methods. Land cover (LC) information, traditionally obtained through image segmentation or classification processing, is typically used in existing methods. Image processing is time consuming and has various accuracy levels for heterogeneous images, thus decreasing retrieval efficiency and accuracy. Additionally, the monotemporal LC information used has a limited ability to distinguish among confusable regions with different terrain, e.g., forests located on flatlands or mountains, and to obtain regions, e.g., urban regions, with similar growth rates. In this study, we employ free multiple-year 30 m LC products, a terrain product, and the Google Earth Engine (GEE) platform to accurately and efficiently locate the desired heterogeneous moderate spatial resolution images from various public EO data archives. Regions similar to the query region are detected with two-stage similarity calculations: First, monotemporal pixel-based LC and terrain information are used to filter out the most dissimilar regions; second, object-based LC change and terrain information are used to locate similar regions. Then, the desired images covering these detected similar regions are obtained from EO data archives via image metadata, e.g., geographical location and acquisition time. The experimental results of the two representative query regions show that our method can be used to obtain the desired images within several minutes and has higher accuracy than the LandEx method and a simplified method using only monotemporal LC information. The main contribution of our study is to reveal that LC changes and terrain information are helpful for improving the retrieval accuracy achieved from monotemporal LC information alone. Our method has great operability, with no need to perform EO data acquisition, image processing of raw EO images, or management of computational resources. Our method is conducive to making full use of images in various public EO archives to improve the decision making quality of decision-supporting applications.

Mapping Paddy Rice Using a Convolutional Neural Network (CNN) with Landsat 8 Datasets in the Dongting Lake Area, China

Rice is one of the world’s major staple foods, especially in China. Highly accurate monitoring on rice-producing land is, therefore, crucial for assessing food supplies and productivity. Recently, the deep-learning convolutional neural network (CNN) has achieved considerable success in remote-sensing data analysis. A CNN-based paddy-rice mapping method using the multitemporal Landsat 8, phenology data, and land-surface temperature (LST) was developed during this study. First, the spatial–temporal adaptive reflectance fusion model (STARFM) was used to blend the moderate-resolution imaging spectroradiometer (MODIS) and Landsat data for obtaining multitemporal Landsat-like data. Subsequently, the threshold method is applied to derive the phenological variables from the Landsat-like (Normalized difference vegetation index) NDVI time series. Then, a generalized single-channel algorithm was employed to derive LST from the Landsat 8. Finally, multitemporal Landsat 8 spectral images, combined with phenology and LST data, were employed to extract paddy-rice information using a patch-based deep-learning CNN algorithm. The results show that the proposed method achieved an overall accuracy of 97.06% and a Kappa coefficient of 0.91, which are 6.43% and 0.07 higher than that of the support vector machine method, and 7.68% and 0.09 higher than that of the random forest method, respectively. Moreover, the Landsat-derived rice area is strongly correlated (R2 = 0.9945) with government statistical data, demonstrating that the proposed method has potential in large-scale paddy-rice mapping using moderate spatial resolution images.

Assessing tropical cyclone impacts using object-based moderate spatial resolution image analysis: a case study in Bangladesh

ABSTRACTThe environmental and societal impacts of tropical cyclones could be reduced using a range of management initiatives. Remote sensing can be a cost effective, accurate, and potential tool for mapping the multiple impacts caused by tropical cyclones using high-to-moderate spatial resolution (5–30 m) satellite imagery to provide data on the following essential parameters – evacuation, relief, and management of natural resources. This study developed and evaluated an approach for assessing the impacts of tropical cyclones through object-based image analysis and moderate spatial resolution imagery. Pre- and post-cyclone maps of artificial and natural features are required for assessing the overall impacts in the landscape that could be acquired by mapping specific land cover types. We used the object-based approach to map land-cover types in pre- and post-cyclone Satellite Pour l’Observation de la Terre (SPOT) 5 image data and the post-classification comparison technique to identify changes in the particular features in the landscape. Cyclone Sidr (2007) was used to test the applicability of this approach in Sarankhola Upazila in Bangladesh. The object-based approach provided accurate results for classifying features from pre- and post-cyclone satellite images with an overall accuracy of 95.43% and 93.27%, respectively. Mapped changes identified the extent, type, and form of cyclone induced impacts. Our results indicate that 63.15% of the study area was significantly affected by cyclone Sidr. The majority of mapped damage was found in vegetation, cropped lands, settlements, and infrastructure. The damage results were verified through the high spatial resolution satellite imagery, reports and pictures that were taken after the cyclone. The methods developed may be used in future to assess the multiple impacts caused by tropical cyclones in Bangladesh and other similar environments for the purposes of tropical cyclone disaster management.

Crop Area Estimation from UAV Transect and MSR Image Data Using Spatial Sampling Method

Using remote sensing data to estimate crop area is efficient to a wide range of end-users, including government agencies, farmers and researchers. Moderate spatial resolution (MSR) image data are widely used to estimate crop area. But its accuracy can’t meet the demands of precision. Spatial sampling techniques integrated the strengths of remote sensing and sampling survey are being widely used. This method need large sample size which is cannot be guaranteed by remote sensing due to weather. The Unmanned Aerial Vehicle (UAV) can be used as an effective way to guarantee enough sample size. This paper proposed a spatial sampling method using MSR image classification results and UAV transects, a stratified random sampling method was proposed, area-scale (from MSR image classification) was used as auxiliary variable to guide the distribution of UAV transects, which had proved that 2% sampling ratio can make the crop area estimation accuracy more than 95% with a 95% confidence interval.

Combining per-pixel and object-based classifications for mapping land cover over large areas

A plethora of national and regional applications need land-cover information covering large areas. Manual classification based on visual interpretation and digital per-pixel classification are the two most commonly applied methods for land-cover mapping over large areas using remote-sensing images, but both present several drawbacks. This paper tests a method with moderate spatial resolution images for deriving a product with a predefined minimum mapping unit (MMU) unconstrained by spatial resolution. The approach consists of a traditional supervised per-pixel classification followed by a post-classification processing that includes image segmentation and semantic map generalization. The approach was tested with AWiFS data collected over a region in Portugal to map 15 land-cover classes with 10 ha MMU. The map presents a thematic accuracy of 72.6 ± 3.7% at the 95% confidence level, which is approximately 10% higher than the per-pixel classification accuracy. The results show that segmentation of moderate-spatial resolution images and semantic map generalization can be used in an operational context to automatically produce land-cover maps with a predefined MMU over large areas.

Fractional Vegetation Cover Estimation In Urban Environments

Abstract. Quality of life in urban environments is closely related to vegetation cover. The Urban growth and its related environmental problems, planners are forced to implement policies to improve the quality of urban environment. Thus, vegetation mapping for planning and managing urban is critical. Given the spectral complexity of the urban environment and the sparse vegetation in these areas, to generate a reliable map of coverage Vegetation in these areas requires the use of high spatial resolution images. But given the size of cities and the rapid changes in vegetation status, Mapping of vegetation using these images will have cost much. In this study, using a moderate spatial resolution image with the help of a small part of high spatial resolution image vegetation cover in a Metropolitan area is obtained. We make use of Ikonos image to get Fractional vegetation cover (FVC) and used as a vicarious validation of FVC. Then using linear and nonlinear regression and neural network between the FVC derived from the Ikonos image and vegetation indices on Landsat image, the relationship was established. A number of pixels were randomly selected from the images for the model validation. The results show that the neural network, nonlinear regression and linear regression models are more accurate for the estimation of FVC respectively.

A patch spectral purification method to extract field patch average parameters from moderate resolution data

Satellite remote sensing data with high spatial and temporal resolutions are required in the dynamic monitoring of agricultural information. However, currently it is not yet possible to embrace the advantages of both high spatial resolution and high temporal resolution in a single sensor. Combining the merits from different remote sensing data will greatly benefit agricultural applications. In this paper, a patch spectral purification (PSP) method is developed to estimate the pure reflectance of field patches by combining high and moderate spatial resolution image data. The PSP method adopts the linear mixing model to build equations from moderate spatial resolution images, estimates initial values of field patch reflectance by statistical means to provide constraints to the solution and solves the equation sets based on Bayes' theorem. We chose one farm in Xinjiang, China as the study area and took 250 m MODIS image data as an example to test the performance of the method. The sensitivity analysis and the validation indicate that the PSP method is capable of giving a good estimation of pure field patch reflectance from mixed pixels of 250 m MODIS images in the study area.

High spatial resolution remote sensing for environmental monitoring and management preface:

This special feature in the journal of Spatial Science brings together a collection of papers showing environmental monitoring and management applications of high spatial resolution remotely sensed image data. As illustrated by the papers in this special feature, and a growing number of papers in ecology, environmental management and remote sensing journals (Butler, 2006; Mumby et aL, 2001; Zanoni and Goward, 2003), there is an increasing need for spatial information derived from multi-spectral sensors at the scales of traditional aerial photography. The application of moderate spatial resolution image data has produced limited results for mapping and monitoring small features (< 10 m) within terrestrial and aquatic habitats, Such as individual tree crowns and their associated biophysical variables, coral reef structures and seagrass distribution. With nominal spatial resolutions less than 5 m x 5 m, there now exists the capacity to acquire fully radiometrically and geometrically corrected data to map and monitor complex structures and patterns of small features. High spatial resolution airborne and satellite digital image sources in various forms are now easily obtainable for agencies responsible for monitoring natural and built environments. Spatial data, including airborne and satellite images, are globally accessible thanks to Virtual Globes, such as GoogleEarth and Microsoft's Virtual Earth.

Monitoring the composition of urban environments based on the vegetation-impervious surface-soil (VIS) model by subpixel analysis techniques

The majority of the world's population now resides in urban environments and information on the internal composition and dynamics of these environments is essential to enable preservation of certain standards of living. Remotely sensed data, especially the global coverage of moderate spatial resolution satellites such as Landsat, Indian Resource Satellite and Système Pour l'Observation de la Terre (SPOT), offer a highly useful data source for mapping the composition of these cities and examining their changes over time. The utility and range of applications for remotely sensed data in urban environments could be improved with a more appropriate conceptual model relating urban environments to the sampling resolutions of imaging sensors and processing routines. Hence, the aim of this work was to take the Vegetation-Impervious surface-Soil (VIS) model of urban composition and match it with the most appropriate image processing methodology to deliver information on VIS composition for urban environments. Several approaches were evaluated for mapping the urban composition of Brisbane city (south-east Queensland, Australia) using Landsat 5 Thematic Mapper data and 1:5000 aerial photographs. The methods evaluated were: image classification; interpretation of aerial photographs; and constrained linear mixture analysis. Over 900 reference sample points on four transects were extracted from the aerial photographs and used as a basis to check output of the classification and mixture analysis. Distinctive zonations of VIS related to urban composition were found in the per-pixel classification and aggregated air-photo interpretation; however, significant spectral confusion also resulted between classes. In contrast, the VIS fraction images produced from the mixture analysis enabled distinctive densities of commercial, industrial and residential zones within the city to be clearly defined, based on their relative amount of vegetation cover. The soil fraction image served as an index for areas being (re)developed. The logical match of a low (L)-resolution, spectral mixture analysis approach with the moderate spatial resolution image data, ensured the processing model matched the spectrally heterogeneous nature of the urban environments at the scale of Landsat Thematic Mapper data.

Integrating High and Moderate Spatial Resolution Image Data to Estimate Forest Age Structure

RÉSUMÉLa collecte de l'information spatiale pour mesurer les changements à l'état et à la condition de l'environnement est une composante fondamentale pour l'économie ou l'utilisation soutenable des forêts tropicales et subtropicales. L'âge est un attribut structural important des forêts de vieille-croissance qui influence la diversité biologique dans les forêts d'eucalyptus de l'Australie. La traduction des photographies aériennes a été traditionnellement utilisée pour tracer l'âge et la structure des peuplements de forêt, cependant, cette méthode est subjective et ne capture pas assez bien les variations d'échelle fine et horizontale qui sont nécessaires aux études écologiques. L'identification et le tracé des attributs structuraux végétatifs aux échelles fines et horizontale permettront la compilation d'information associée aux indicateurs de processus Montréal 1b et 1d, qui cherchent à déterminer les liens entre la répartition par âge et la diversité et l'abondance de populations de faune de forêt. Ce projet intégre des mesures des attributs structuraux dérivés d'un modèle d'altitude de la voûte avec les résultats d'un modèle d'analyse de mélanges géometrique-optique/spectral pour tracer la répartition par âge de forêt à l'échelle horizontale. La disponibilité de données à multiple-échelle permet le transfert des attributs à haute résolution pour la surveillance à l'échelle horizontale. Des données d'image multispectrales ont été obtenues à partir d'un capteur DMSV (vidéo digital multispectral) au-dessus de la forêt St. Mary's State Forest, au sud-est du Queensland, en Australie. Des niveaux locaux de variance de scène pour différents types de forêt calculés à partir des données DMSV ont été employés pour optimiser les données de densité d'arbre et de taille de la voûte dans un modèle géométrique-optique appliqué aux données du cartographe thématique de Landsat (TM).Des données aéroportées de module de balayage à laser obtenues au-dessus de la zone de projet ont été employées pour calibrer un filtre digital pour extraire des tailles d'arbre à partir d'un modèle digital d'élévation dérivé des stereopairs balayés de couleur. Les évaluations modelées de la taille d'arbre, de la taille de couronne, et de densité ont été utilisées pour produire une classification à étape de succession de forêt à l'échelle horizontale. Les résultats obtenus (exacts à 72%), limités dans la validation, démontrent quand même le potentiel de la méthodologie à multiple-échelle pour fournir l'information spatiale qui assisterait les objectifs de la politique de sylviculture (c.- à-d., la surveillance de la répartition par âge de forêt).

Monitoring Growth in Rapidly Urbanizing Areas Using Remotely Sensed Data

Urbanization and the ability to manage for a sustainable future present numerous challenges for geographers and planners in metropolitan regions. Remotely sensed data are inherently suited to provide information on urban land cover characteristics, and their change over time, at various spatial and temporal scales. Data models for establishing the range of urban land cover types and their biophysical composition (vegetation, soil, and impervious surfaces) are integrated to provide a hierarchical approach to classifying land cover within urban environments. These data also provide an essential component for current simulation models of urban growth patterns, as both calibration and validation data. The first stages of the approach have been applied to examine urban growth between 1988 and 1995 for a rapidly developing area in southeast Queensland, Australia. Landsat Thematic Mapper image data provided accurate (83% adjusted overall accuracy) classification of broad land cover types and their change over time. The combination of commonly available remotely sensed data, image processing methods, and emerging urban growth models highlights an important application for current and next generation moderate spatial resolution image data in studies of urban environments.