Pixel-based Methods Research Articles

A Fast Selection Method of Landmarks for Terrain Matching Navigation

Under fixed imaging conditions, the landmark selection method based feature traversal analysis has high computational complexity. The hierarchical statistical significance detection method uses global statistical information for feature analysis to overcome the computational complexity problem caused by feature traversal analysis. The frequency domain de-correlation method can remove repeat mode in the image by adaptive Gaussian filtering on the amplitude-frequency characteristics. In this paper, combined the hierarchical statistical saliency detection method with the frequency domain de-correlation method, a fast landmark selection algorithm based on saliency analysis is proposed. Based on the proposed algorithm, the automatic landmark selection architecture for terrain matching navigation was constructed. The selection of landmark points was carried out in the Qin-ling Mountains and the Guangdong and Guangxi hills. The results show that compared with the feature or pixel-based landmark selection method, the landmark selection efficiency of the proposed method is improved by 2 to 3 orders of magnitude. The correct matching rate of candidate landmarks selected in the Qinling Mountains and the Guangdong and Guangxi hills are 73.9% and 88.3% respectively.

Unmixing of hyperspectral data for mineral detection using a hybrid method, Sar Chah-e Shur, Iran

This study aims to detect indicative minerals by spectral unmixing of the Hyperion and HyMap datasets in the Sar Chah-e Shur area. The mineral endmembers and their abundances were therefore determined using a series of hyperspectral processing algorithms. The virtual dimensionality methods including principal component analysis (PCA), minimum noise fraction (MNF), singular valued decomposition (SVD), Harsanyi-Farrand-Chang (HFC)/ (NWHFC), and Hyperspectral signal subspace identification by minimum error (HySime) were applied to estimate the number of endmembers. Five pure pixel-based methods including pixel purity index (PPI), sequential maximum angle convex cone (SMACC), simplex growing algorithm (SGA), N-FINDR, and vertex component analysis (VCA) were then applied for extracting the spectra of endmembers. Clay, serpentine, mica, and zeolite group minerals were identified which are consistent with the geological investigations in the region. The detected minerals were then mapped by the fully constrained least square (FCLS) method. The functionality of the methods and their performances on HyMap and Hyperion data were surveyed by several criteria including the number of recognized endmembers, the matching score of extracted endmembers with the reference spectrum, the agreement of the estimated abundances maps with the relevant lithological units on the geological map, and the average reconstruction error (ARE). Two hybrid maps were generated by combining individual methods that were found highly consistent with the geological map. The XRD analysis of three chips rock samples of two indicative lithological units was used to additionally check the efficiency of the applied methods.

Detecting the development stages of natural forests in northern Iran with different algorithms and high-resolution data from GeoEye-1.

Nowadays, taking advantage of multispectral sensors with the high spatial and spectral resolution and using a variety of plant indices and remote sensing have provided the possibility of more accurate analysis and classification of satellite data in the identification of natural phenomena. Nowadays, obtaining information about the structure of forests through remote sensing data to manage renewable resources is of interest to managers and researchers. This study produced the development maps of natural forests in northern Iran by taking advantage of GeoEye-1 data, training samples, and various algorithms through the pixel-based, object-based, and model-based methods. The classification's ultimate accuracy was calculated by each of the above methods with the overall accuracy parameters and kappa coefficient. By examining the accuracy of map classification resulting from different methods, the maximum accuracy (78%) in object-based method was estimated based on the segmentation of NDVI and the maximum likelihood algorithm. Meanwhile, some other classification methods showed much less accuracy. The results showed that the algorithms following the structural patterns for pixel distribution classification provided a higher accuracy. Also, these results showed the high potential of high-resolution data from GeoEye-1 in the production of forest development maps and the effect of choosing the appropriate algorithm in the production of higher accuracy maps.

Monitoring surface water area variations of reservoirs using daily MODIS images by exploring sub-pixel information

Information on the temporal variation of surface water area of reservoirs is fundamental for water resource management and is often monitored by satellite remote sensing. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery is an attractive data source for the routine monitoring of reservoirs, however, the accuracy is often limited due to the negative impacts associated with its coarse spatial resolution and the effects of cloud contamination. Methods have been proposed to solve these two problems independently but it remains challenging to address both problems simultaneously. To overcome this, this paper proposes a new approach that aims to monitor reservoir surface water area variations accurately and timely from daily MODIS images by exploring sub-pixel scale information. The proposed approach used estimates of reservoir water areas obtained from cloud-free and relatively fine spatial resolution Landsat images and water fraction images by spectral unmixing of coarse MODIS imagery as reference data. For each MODIS pixel, these reference reservoir water areas and their corresponding pixel water fractions were used to construct a linear regression equation, which in turn may be applied to predict the time series of reservoir water areas from daily MODIS water fraction images. The proposed approach was assessed with 21 reservoirs, where the correlation coefficients between reservoir water areas predicted by the common pixel-based analysis method and altimetry water levels were all less than 0.5. With the proposed sub-pixel analysis method, the resultant correlation coefficients were much improved, with eleven values larger than 0.5 including six values larger than 0.8 and the highest value of 0.94. The results show that the proposed sub-pixel analysis method is superior to the pixel based analysis method. The proposed method makes it possible to directly estimate the whole reservoir water area from, potentially, an individual cloud-free MODIS pixel, and is a promising way to improve the accuracy in the usability of MODIS images for the monitoring of reservoir surface water area variations.

Comparison of Pixel-Based and Object-Based Classification Methods in Determination of Wetland Coastline

Tuz Lake and its surrounding lakes (Tersakan Lake, Duden Lake, Bolluk Lake, Esmekaya Lake, Kopek Lake, Akgol) are placed in the Central Anatolia region. These lakes maintain the ecosystem integrity and make a good habitat for numerous bird species, especially flamingos. The Duden Lake is located within the boundaries of the Tuz Lake Special Environmental Protection Area and was declared as a protected area in 1992. The surface and underground water around Kulu District of Konya feed the Duden Lake, which is tectonically formed through the Kulu Stream. The lake with the average area of 860 hectares is unfortunately in risk of extinction. Remote sensing has been the most useful tool to obtain spatial and temporal information about wetlands and it provides up-to-date, accurate, and cost-effective information. Remote sensing methods and applications are used quite effectively on wetlands. The traditional pixel-based classification method is applied to different satellite images in wetlands over many decades, and the usage of object-based classification method has started recently comparing to the pixel-based one. This study aimed to determine the coastline of the wetlands. Sentinel 2 satellite images, which provide free access and high spatial resolution, are used to observe the coastline of Duden Lake through the usage of pixel-based and object-based classification methods in all the seasons. The applicability of the methods in determination of shallow wetland coastline is studied and evaluated. The results of the pixel-based and the object-based classification images are compared by accuracy assessment.

Pixel-Based Approach for Generating Original and Imitating Evolutionary Art

We proposed a pixel-based evolution method to automatically generate evolutionary art. Our method can generate diverse artworks, including original artworks and imitating artworks, with different artistic styles and high visual complexity. The generation process is fully automated. In order to adapt to the pixel-based method, a von Neumann neighbor topology-modified particle swarm optimization (PSO) is employed to the proposed method. The fitness functions of PSO are well prepared. Firstly, we come up with a set of aesthetic fitness functions. Next, the imitating fitness function is designed. Finally, the aesthetic fitness functions and the imitating fitness function are weighted into one single object function, which is used in the modified PSO. Both the original outputs and imitating outputs are shown. A questionnaire is designed to investigate the subjective aesthetic feeling of proposed evolutionary art, and the statistics are shown.

Driving forces of NPP change in debris flow prone area: A case study of a typical region in SW China

Net primary productivity (NPP) is one of the essential indicators of regional eco-environment security. In ecologically fragile areas, debris flow prone areas are extremely sensitive to human and natural impacts. In this study, our goal was to explore the main driving forces and their characteristics that caused changes in NPP. This was achieved by collecting and analyzing multi-source data of a typical region in SW China. A pixel-based spatial analysis method was conducted in this study to quantify the co-evolution characteristics between NPP and the main driving forces. Our results showed that the overall NPP in the region exhibits an increasing trend. The main driving forces are NDVI and population density, which can explain >70% of the environmental states. In addition, co-evolution characteristics were obvious; generally, NDVI had a positive effect and population density had a negative effect. However, extremely significant negative correlations between NPP and NDVI were found in 12.01% of the study area. Therefore, carrying out reasonable ecological restoration and applicable human activities in debris flow prone areas could ensure eco-environment security and sustainable development. This study contributes to improving our understanding of eco-environment evolution of critical zones and provides a scientific basis for regional eco-environment security construction and eco-treatment of debris flow.

High-resolution multispectral mapping facies on glacier surface in the Arctic using WorldView-3 data

Glaciers are important and sensitive part of our environment which can be used as indicator of global warming and climate change. Glacier facies represent distinct regions of a glacier surface characterized by near surface structure and density that develop as a function of spatial variations in surface melt and accumulation. The facies mapping aids in delineating different zones of the glacier, which are useful in computing glacier mass balance and modeling. In this study we tested traditional and advanced classification techniques on the Edithbreen glacier situated in Ny-lesund, Svalbard, using WorldView-3 and Landsat 8 OLI. The comparison of the accuracy was conducted using error matrices. Six measures of accuracy were derived from the error matrices and were compared with each other to find the method delivering the most adequate output for facies mapping. The pixel-based approach applied to Landsat-8 data yielded higher accuracies (>80%) when compared to that. The object-oriented classification revealed a much better accuracy and high kappa coefficient for both low and high-resolution datasets. The study clearly indicates that the object-oriented classification provides better results for glacier facies classification when high spatial resolution is used, but for lower spatial resolution, pixel-based methods are adequate.

Cork oak woodland land-cover types classification: a comparison between UAV sensed imagery and field survey

ABSTRACT This work assesses the use of aerial imagery for the vegetation cover characterization in cork oak woodlands. The study was conducted in a cork oak woodland in central Portugal during the summer of 2017. Two supervised classification methods, pixel-based and object-based image analysis (OBIA), were tested using a high spatial resolution image mosaic. Images were captured by an unmanned aerial vehicle (UAV) equipped with a red, green, blue (RGB) camera. Four different vegetation covers were distinguished: cork oak, shrubs, grass and other (bare soil and tree shadow). Results have been compared with field data obtained by the point-intercept (PI) method. Data comparison reveals the reliability of aerial imagery classification methods in cork oak woodlands. Results show that cork oak was accurately classified at a level of 82.7% with pixel-based method and 79.5% with OBIA . 96.7% of shrubs were identified by OBIA, whereas there was an overestimation of 21.7% with pixel approach. Grass presents an overestimation of 22.7% with OBIA and 12.0% with pixel-based method. Limitations rise from using only spectral information in the visible range. Thus, further research with the use of additional bands (vegetation indices or height information) could result in better land-cover type classification.

Assessing the Impact of Stochastic Perturbations in Cloud Microphysics using GOES-16 Infrared Brightness Temperatures

AbstractIn this study, infrared brightness temperatures (BTs) are used to examine how applying stochastic perturbed parameter (SPP) methodology to the widely used Thompson–Eidhammer cloud microphysics scheme impacts the cloud field in high-resolution forecasts. Modifications are made to add stochastic perturbations to three parameters controlling cloud generation and dissipation processes. Two five-member ensembles are generated, one using the microphysics parameter perturbations (SPP-MP) and another where white noise perturbations were added to potential temperature fields at initialization time (Control). The impact of the SPP method was assessed using simulated and observed GOES-16 BTs. This analysis uses pixel-based and object-based methods to assess the impact on the cloud field. Pixel-based methods revealed that the SPP-MP BTs are slightly more accurate than the Control BTs. However, too few pixels with a BT lower than 270 K result in a positive bias compared to the observations. A negative bias compared to the observations is observed when only analyzing lower BTs. The spread of the ensemble BTs was analyzed using the continuous ranked probability score differences, with the SPP-MP ensemble BTs having less (more) spread during May (January) compared to the Control. Object-based analysis using the Method for Object-Based Diagnostic Evaluation revealed the upper-level cloud objects are smaller in the SPP-MP ensemble than the Control but a lower bias exists in the SPP-MP BTs compared to the Control BTs when overlapping matching objects. However, there is no clear distinction between the SPP-MP and Control ensemble members during the evolution of objects, Overall, the SPP-MP perturbations result in lower BTs compared to the Control ensemble and more cloudy pixels.

Object-based spectral quality assessment of high-resolution pan-sharpened satellite imageries: new combined fusion strategy to increase the spectral quality

Panchromatic and multispectral images produced by the earth observation satellites are fused; hence, a high-resolution multispectral image is obtained. Spectral quality of the fused images is of great importance since the quality of a large number of remote sensing products mainly depends on this feature. Due to the importance of the spectral quality of the fused images, its assessment is of particular significance as well. This article proposes an object-based strategy for the spectral quality assessment of the fused images to eliminate the limitations of the current pixel-based method. This kind of assessment is performed by focusing on homogeneous objects with similar spectral and textural behaviors. After determining an optimal metric, the object-based scheme was applied to five datasets from four types of satellite sensors and the spectral behavior of fusion methods was examined within the image classes. Although the spectral behavior of the fusion methods was not regular, the best methods in each class were determined using statistical analysis. Furthermore, a scheme was proposed to combine the results of different fusion methods to obtain a fused image with the best possible spectral quality. The obtained results indicate that this image enjoys 37% better quality than the best-fused image selected based on the pixel-based quality assessment.

A Domain Enriched Deep Learning Approach to Classify Atherosclerosis using Intravascular Ultrasound Imaging.

Intravascular ultrasound (IVUS) imaging is widely used for diagnostic imaging in interventional cardiology. The detection and quantification of atherosclerosis from acquired images is typically performed manually by medical experts or by virtual histology IVUS (VH-IVUS) software. VH-IVUS analyzes backscattered radio frequency (RF) signals to provide a color-coded tissue map, and is the method of choice for assessing atherosclerotic plaque in situ. However, a significant amount of tissue cannot be analyzed in reasonable time because the method can be applied just once per cardiac cycle. Furthermore, only hardware and software compatible with RF signal acquisition and processing may be used. We present an image-based tissue characterization method that can be applied to entire acquisition sequences post hoc for the assessment of diseased vessels. The pixel-based method utilizes domain knowledge of arterial pathology and physiology, and leverages technological advances of convolutional neural networks to segment diseased vessel walls into the same tissue classes as virtual histology using only grayscale IVUS images. The method was trained and tested on patches extracted from VH-IVUS images acquired from several patients, and achieved overall accuracy of 93.5% for all segmented tissue. Imposing physically-relevant spatial constraints driven by domain knowledge was key to achieving such strong performance. This enriched approach offers capabilities akin to VH-IVUS without the constraints of RF signals or limited once-per-cycle analysis, offering superior potential information acquisition speed, reduced hardware and software requirements, and more widespread applicability. Such an approach may well yield promise for future clinical and research applications.

A Fast Image Mosaicking Method Based on Iteratively Minimizing Cloud Coverage Areas

There are many areas with cloud coverage in remote sensing images frequently, and they will shelter the ground objects under clouds. When images with clouds are used in mosaicking, cloud coverage areas may still exist in mosaics, which constrain the application of mosaics. Therefore, a simple and fast image mosaicking method based iteratively minimizing cloud coverage areas is proposed in this letter. In the proposed method, both cloud-free images and images with clouds are used to generate a mosaic with minimal cloud coverage areas. After the generation of initial effective mosaic polygons (EMPs) for images, a quadtree iterative procedure is presented to optimize each EMP by excluding the residual cloud coverage areas in each EMP. The procedure fully takes into account the spatial distribution of cloud coverage areas, and each area with cloud coverage is minimized by as few images as possible. The experimental results show that the mosaic generated by the proposed method can achieve a better radiometric continuity and higher efficiency than the pixel-based mosaic method. Besides, the proposed method can make the full use of the cloud-free areas in images, which can improve the utilization of images and the currency of mosaics.

Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data

Wetland ecosystems have experienced dramatic challenges in the past few decades due to natural and human factors. Wetland maps are essential for the conservation and management of terrestrial ecosystems. This study is to obtain an accurate wetland map using an object-based stacked generalization (Stacking) method on the basis of multi-temporal Sentinel-1 and Sentinel-2 data. Firstly, the Robust Adaptive Spatial Temporal Fusion Model (RASTFM) is used to get time series Sentinel-2 NDVI, from which the vegetation phenology variables are derived by the threshold method. Subsequently, both vertical transmit-vertical receive (VV) and vertical transmit-horizontal receive (VH) polarization backscatters (σ0 VV, σ0 VH) are obtained using the time series Sentinel-1 images. Speckle noise inherent in SAR data, resulting in over-segmentation or under-segmentation, can affect image segmentation and degrade the accuracies of wetland classification. Therefore, we segment Sentinel-2 multispectral images to delineate meaningful objects in this study. Then, in order to reduce data redundancy and computation time, we analyze the optimal feature combination using the Sentinel-2 multispectral images, Sentinel-2 NDVI time series, phenological variables and other vegetation index derived from Sentinel-2 multispectral images, as well as time series Sentinel-1 backscatters at the object level. Finally, the stacked generalization algorithm is utilized to extract the wetland information based on the optimal feature combination in the Dongting Lake wetland. The overall accuracy and Kappa coefficient of the object-based stacked generalization method are 92.46% and 0.92, which are 3.88% and 0.04 higher than that using the pixel-based method. Moreover, the object-based stacked generalization algorithm is superior to single classifiers in classifying vegetation of high heterogeneity areas.

Seafloor mapping to support conservation planning in an ecologically unique fjord in Newfoundland and Labrador, Canada

As human impacts continue to threaten coastal habitats and ecosystems, marine benthic habitat and substrate mapping has become a key component of many conservation and management initiatives. Understanding the composition and extent of marine habitats can inform marine protected area (MPA) planning and monitoring, help identify vulnerable or rare habitats and support fisheries management. To support conservation planning in Eastern Canada, we mapped the seafloor of Newman Sound, identifying the benthoscape classes (i.e. discrete biophysical seafloor classes) of this ecologically diverse and unique fjord in Newfoundland and Labrador (NL). Mapping was achieved using multibeam echosounder (MBES) data collected using multiple platforms, seafloor videos and an unsupervised pixel-based classification method. Seven benthoscape classes were identified within the extent of the MBES coverages. Multivariate statistical analyses indicate that two benthoscape classes - mixed boulder and mud - support distinct epifaunal communities, and also capture the changes in benthic community composition between hard/shallow substrates and soft/deep substrates. Our results illustrate how benthoscape maps can inform marine spatial planning and conservation in the Newman Sound region, support monitoring and also calls for adaptive management of the adjacent Eastport MPA.

Accelerating linear system solvers for time-domain component separation of cosmic microwave background data

Component separation is one of the key stages of any modern cosmic microwave background data analysis pipeline. It is an inherently nonlinear procedure and typically involves a series of sequential solutions of linear systems with similar but not identical system matrices, derived for different data models of the same data set. Sequences of this type arise, for instance, in the maximization of the data likelihood with respect to foreground parameters or sampling of their posterior distribution. However, they are also common in many other contexts. In this work we consider solving the component separation problem directly in the measurement (time-) domain. This can have a number of important benefits over the more standard pixel-based methods, in particular if non-negligible time-domain noise correlations are present, as is commonly the case. The approach based on the time-domain, however, implies significant computational effort because the full volume of the time-domain data set needs to be manipulated. To address this challenge, we propose and study efficient solvers adapted to solving time-domain-based component separation systems and their sequences, and which are capable of capitalizing on information derived from the previous solutions. This is achieved either by adapting the initial guess of the subsequent system or through a so-called subspace recycling, which allows constructing progressively more efficient two-level preconditioners. We report an overall speed-up over solving the systems independently of a factor of nearly 7, or 5, in our numerical experiments, which are inspired by the likelihood maximization and likelihood sampling procedures, respectively.

Examining the PALSAR-2 Global forest/non-forest maps through Turkish afforestation practices

ABSTRACT Forests and afforestation have significant importance, given the present course of World’s climate resulting from our insensitive behaviours, policies, ambitions, etc. Turkish Forest Service has departments, sole purposes of which are to combat desertification through afforestation, flood control, and watershed rehabilitation works. Majority of the works is carried out in the semi-arid central part of the country, which is known as the Irano-Turanian phytogeographic region. Hydroelectric dam projects, which have extensively been conducted to produce energy and to harness the rivers when needed, have been at the centre of these efforts in the region. Afforestation works, which have long been used to safeguard dam reservoirs, have always been associated with such projects because they are implemented even prior to water deposition. They along with the country’s managed forests are looked after as the natural forest cover. Improvements in remote sensing at this point enabled the agencies to diversify their efforts in monitoring the natural resources and strengthening the decision makers’ hand in management preferences in both global and regional scales. There are various data produced through the classification of satellite imagery representing the existence or the percentage of forests in any location on Earth. In this study, Global Phased Arrayed L-band Synthetic Aperture Radar PALSAR-2/PALSAR mosaic and forest/non-forest maps of Japan Aerospace Exploration Agency (JAXA) and Global Forest Cover Change (GFCC) thematic image tiles of National Aeronautics and Space Administration (NASA), which are two of the widely accepted such data, were compared to one another and to our control results produced by a pixel-based classification method to assess the performance of afforestation works around seven major hydroelectric dam reservoirs situated within the central part of Turkey. The results showed that Global PALSAR-2/PALSAR forest/non-forest maps were somewhat capturing the trend, but overly exaggerating the ground facts up to 1064%. GFCC epochs, on the other hand, did not yield sound results in this semi-arid part of Turkey. Latter data could not even capture the successfully afforested sites.

Geographic Object-Based Image Analysis Framework for Mapping Vegetation Physiognomic Types at Fine Scales in Neotropical Savannas

Regional maps of vegetation structure are necessary for delineating species habitats and for supporting conservation and ecological analyses. A systematic approach that can discriminate a wide range of meaningful and detailed vegetation classes is still lacking for neotropical savannas. Detailed vegetation mapping of savannas is challenged by seasonal vegetation dynamics and substantial heterogeneity in vegetation structure and composition, but fine spatial resolution imagery (<10 m) can improve map accuracy in these heterogeneous landscapes. Traditional pixel-based classification methods have proven problematic for fine spatial resolution data due to increased within-class spectral variability. Geographic Object-Based Image Analysis (GEOBIA) is a robust alternative method to overcome these issues. We developed a systematic GEOBIA framework accounting for both spectral and spatial features to map Cerrado structural types at 5-m resolution. This two-step framework begins with image segmentation and a Random Forest land cover classification based on spectral information, followed by spatial contextual and topological rules developed in a systematic manner in a GEOBIA knowledge-based approach. Spatial rules were defined a priori based on descriptions of environmental characteristics of 11 different physiognomic types and their relationships to edaphic conditions represented by stream networks (hydrography), topography, and substrate. The Random Forest land cover classification resulted in 10 land cover classes with 84.4% overall map accuracy and was able to map 7 of the 11 vegetation classes. The second step resulted in mapping 13 classes with 87.6% overall accuracy, of which all 11 vegetation classes were identified. Our results demonstrate that 5-m spatial resolution imagery is adequate for mapping land cover types of savanna structural elements. The GEOBIA framework, however, is essential for refining land cover categories to ecological classes (physiognomic types), leading to a higher number of vegetation classes while improving overall accuracy.

Hybrid geological modeling: Combining machine learning and multiple-point statistics

Accurately modeling and constructing a geologically realistic subsurface model remains an outstanding problem as the morphology controls the flow behaviors. Particularly, one of the pattern-based methods, namely cross-correlation based simulation, has been proved to be an effective way to reconstruct a realistic model, at both small and large scales. However, conditioning to point data in the large-scale problems is still a crucial issue in these algorithms, since there is always a trade-off between the quality of the realizations and the degree of point data reproduction. Specifically, it is not practical to build a training image (TI) which includes all the possibilities and variabilities. Therefore, finding a pattern that can represent the point data and, at the same time, preserving the connectivities is difficult. This leads to producing highly-connected realizations with a significant mismatch or poor models with a reasonable degree of point data reproduction. To accurately reproduce the densely distributed hard data, pixel-based methods can also produce some unrealistic artifacts around the hard data. In this paper, to overcome this challenge, however, we use pattern-based methods as they often produce more disconnected geobodies when dealing with dense hard data, and proposed a hybrid algorithm using the pattern-based methods and convolutional neural network (CNN). The trained CNN model is utilized to improve the quality of conditioning to point data for the original realizations generated by the pattern-based algorithm. As such, the mismatch locations are identified, and the same regions are used in the training of CNN to mimic the procedure through which a missing region can be filled. To evaluate the performance of the proposed hybrid algorithm, it is tested on cases with different dimensions and different numbers of facies. Then, the newly improved realizations are compared with the initial realizations generated by the pattern-based algorithm. The comparison is also conducted by the flow simulation test. And it indicates that the proposed hybrid algorithm can better reproduce the point data, while the connectivities are better preserved.

Mapping Seasonal Tree Canopy Cover and Leaf Area Using Worldview-2/3 Satellite Imagery: A Megacity-Scale Case Study in Tokyo Urban Area

This study presents a methodology for developing a high-resolution (2 m) urban tree canopy leaf area inventory in different tree phenological seasons and a subsequent application of the methodology to a 625 km2 urban area in Tokyo. Satellite remote sensing has the advantage of imaging large areas simultaneously. However, mapping the tree canopy cover and leaf area accurately is still difficult in a highly heterogeneous urban landscape. The WorldView-2/3 satellite imagery at the individual tree level (2 m resolution) was used to map urban trees based on a simple pixel-based classification method. The comparison of our mapping results with the tree canopy cover derived from aerial photography shows that the error margin is within an acceptable range of 5.5% at the 3.0 km2 small district level, 5.0% at the 60.9 km2 municipality level, and 1.2% at the 625 km2 city level. Furthermore, we investigated the relationship between the satellite data (vegetation index) and in situ tree-measurement data (leaf area index) to develop a simple model to directly map the tree leaf area from the WorldView-2/3 imagery. The estimated total leaf area in Tokyo urban area in the leaf-on season (633 km2) was twice that of the leaf-off season (319 km2). Our results also showed that the estimated total leaf area in Tokyo urban area was 1.9–6.2 times higher than the results of the moderate-resolution (30 m) satellite imagery.