Traditional Pixel-based Classification Research Articles

Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images

Wheat plays an important role in China’s and the world’s food supply, and it is closely related to economy, culture and life. The spatial distribution of wheat is of great significance to the rational planning of wheat cultivation areas and the improvement of wheat yield and quality. The current rapid development of remote sensing technology has greatly improved the efficiency of traditional agricultural surveys. The extraction of crop planting structure based on remote sensing images and technology is a popular topic in many researches. In response to the shortcomings of traditional methods, this research proposed a method based on the fusion of the pixel-based and object-oriented methods to map the spatial distribution of winter wheat. This method was experimented and achieved good results within Shandong Province. The resulting spatial distribution map of winter wheat has an overall accuracy of 92.2% with a kappa coefficient of 0.84. The comparison with the actual situation shows that the accuracy of the actual recognition of winter wheat is higher and better than the traditional pixel-based classification method. On this basis, the spatial pattern of winter wheat in Shandong was analyzed, and it was found that the topographic undulations had a great influence on the spatial distribution of wheat. This study vividly demonstrates the advantages and possibilities of combining pixel-based and object-oriented approaches through experiments, and also provides a reference for the next related research. Moreover, the winter wheat map of Shandong produced in this research is important for yield assessment, crop planting structure adjustment and the rational use of land resources.

Detection of Benggang in Remote Sensing Imagery through Integration of Segmentation Anything Model with Object-Based Classification

Benggang is a type of erosion landform that commonly occurs in the southern regions of China, posing significant threats to local farmland and human safety. Object-based classification (OBC) can be applied with high-resolution (HR) remote sensing images for detecting Benggang areas on a large spatial scale, offering essential data for aiding in the remediation efforts for these areas. Nevertheless, traditional image segmentation methods may face challenges in accurately delineating Benggang areas. Consequently, the extraction of spatial and textural features from these areas can be susceptible to inaccuracies, potentially compromising the detection accuracy of Benggang areas. To address this issue, this study proposed a novel approach that integrates Segment Anything Model (SAM) and OBC for Benggang detection. The SAM was used to segment HR remote sensing imagery to delineate the boundaries of Benggang areas. After that, the OBC was employed to identify Benggang areas based on spectral, geometrical, and textural features. In comparison to traditional pixel-based classification using the random forest classifier (RFC-PBC) and OBC based on the multi-resolution segmentation (MRS-OBC), the proposed SAM-OBC exhibited superior performance, achieving a detection accuracy of 85.46%, a false alarm rate of 2.19%, and an overall accuracy of 96.48%. The feature importance analysis conducted with random forests highlighted the GLDV Entropy, GLDV Angular Second Moment (ASM), and GLCM ASM as the most pivotal features for the identification of Benggang areas. Due to its inability to extract and utilize these textural features, the PBC yielded suboptimal results compared to both the SAM-OBC and MRS-OBC. In contrast to the MRS, the SAM demonstrated superior capabilities in the precise delineation of Benggang areas, ensuring the extraction of accurate textural and spatial features. As a result, the SAM-OBC significantly enhanced detection accuracy by 34.12% and reduced the false alarm rate by 2.06% compared to the MRS-OBC. The results indicate that the SAM-OBC performs well in Benggang detection, holding significant implications for the monitoring and remediation of Benggang areas.

Research on the identification of land types and tree species in the Engebei ecological demonstration area based on GF-1 remote sensing

Identifying forest types is crucial in satellite remote sensing monitoring. Research focusing on the identification of forest tree species using high-resolution remote sensing images is on the rise. Traditional pixel-based classification methods have not been successful in fully utilizing the rich spatial and texture feature information present in the image. Further, these methods are plagued by “classification noise”, which impedes the precise extraction of information regarding forest tree species. The object-oriented classification method, on the other hand, offers an extraction methodology rooted in image segmentation object features. It efficiently leverages the spectral, texture, and spatial geometry information of high-resolution remote sensing images. In this study, an initial application of the object-oriented, multi-level information extraction was conducted on ground objects within the Engebei ecological demonstration area. This was performed utilizing Gaofen (GF)-1 remote sensing images. Following this, a multi-scale segmentation algorithm was utilized to establish different segmentation scales relative to the features of the different objects. The optimal segmentation scale, shape factor, and compactness factor were determined to be 222/0.5/0.5, 167/0.5/0.6, and 81/0.4/0.6, respectively, for the three levels. Subsequently, training samples were used to select optimal features during the classification process. The nearest neighbor classification and random forest classification were performed respectively. The results indicate an overall classification accuracy of 88.58% and 87.26% for the nearest neighbor classification of remote sensing images in 2020 and 2021 respectively. Meanwhile, the overall classification accuracy for the random forest classification was 92.95% and 92.02% respectively. The Kappa coefficients for the nearest neighbor classification were 0.86 and 0.85, whereas those for the random forest classification were 0.92 and 0.90. These outcomes underscore the utility of the object-oriented classification method in enhancing the accuracy of forest type identification.

Integrated Method for Classifying Medium Resolution Satellite Remotely Sensed Imagery into Land Use Map

There are several remotely sensed images of varied resolutions available. As a result, several classification techniques exist, which are roughly classified as pixel-based and object-based classification methods. Based on the foregoing, this study provided an integrated method of deriving land use from a coarse satellite image. Location coordinates of the land uses were acquired with a handheld Global Positioning System (GPS) instrument as primary data. The study classified the image quantitatively (pixel-based) into built-up, water, riparian, cultivated, and uncultivated land cover classes with no mixed pixels, and then qualitatively into educational, commercial, health, residential, and security land use classes that were conflicting due to spectral similarity. The total accuracy and kappa coefficient of the pixel-based land cover classification were 92.5% and 94% respectively. The results showed that residential land use occupied an area of 5500.01ha, followed by educational (2800.69ha); security (411.27ha); health (133.88ha); and commercial (109.01ha) respectively. The integrated method produces a crisp-appearance like the object-based image classification method. It eliminates the "salt and pepper" appearance that a traditional pixel-based classification would have. The output can be a vector or raster model depending on the purpose for which it is created.

Endmember variability based abundance estimation of red and black soil over sparsely vegetated area using AVIRIS-NG hyperspectral image

Visible-near-and-short-wave-infrared hyperspectral images (HSI) have proven helpful for mapping the soil types over bare soils pixels. However, the accuracy of the traditional pixel-based classification methods decreases due to the spectral mixing between the significant agricultural features such as vegetation, soil and crop residue. In this context, spectral unmixing algorithms are handy for estimating the sub-pixel abundances of soil over sparsely vegetated areas. Most of the spectral unmixing methods focus on analyzing the HSI by considering the pixels as independent entities. However, in the HSIs of agricultural fields, the endmembers are spatially and temporally variable. It is also known that there exists a strong spatial correlation among neighbourhood pixels over agricultural fields. Therefore, both endmember variability and the neighbourhood spatial-spectral information are critical to estimate soil abundance accurately. Here we address the issue of estimating the abundances of two major soil types with spectral variability. The proposed approach combines the endmember bundle extraction with the spectral-spatial weighted unmixing approach (SSWU-SV). Experimental analysis has been carried out on the airborne HSI acquired by airborne-visible-and-infrared-imaging- spectrometer-next-generation (AVIRIS-NG) sensor. The quantitative analysis reveals that the proposed method consistently achieves a better unmixing performance than the traditional linear mixing model in terms of spectral angle mapper (SAM), and root-mean-square error (RMSE). Our results also indicate the saturation of normalized difference vegetation index (NDVI) at high vegetative fractions obtained from SSWU-SV.

Attention-Based Multiscale Sequential Network for PolSAR Image Classification

Polarimetric synthetic aperture radar (PolSAR) images classification is an important topic for PolSAR images understanding and interpretation. However, traditional pixel-based PolSAR image classification that takes image pixel as a processing unit cannot make full use of spatial information and, thus, may not obtain the satisfactory classification results. Hence, this letter proposed an attention-based multiscale sequential network for PolSAR images classification increasing the multiscale spatial information between pixels by way of spatial sequence. Specifically, the long short-term memory (LSTM) network is introduced to convert the time sequence into spatial sequence to extract the spatial features. Then, to obtain the more abundant spatial features and select more important spatial information, an attention-based multiscale spatial-enhanced LSTM (AMSE-LSTM) is proposed to enhance the relationship between pixel spatial information. Finally, a new mixed loss function is defined to improve the classification performance. Experimental results with two real PolSAR data show that compared with state-of-the-art methods, the proposed method can achieve a much better performance and overall classification accuracy.

Classification of tree species in a heterogeneous urban environment using object-based ensemble analysis and World View-2 satellite imagery

Urban trees are valuable in, inter alia, ameliorating air pollution and mitigating the effects associated with urban heat islands. The dearth of tree cover maps is a major challenge for urban planners in the management of urban trees. This work adopts remote sensing approaches to provide urban tree cover maps which can strengthen urban landscape management. Whereas traditional pixel-based classification approaches have been commonly used in image classification, they are not well-suited for urban tree mapping due to their failure to fully explore the image’s spatial and spectral characteristics. Object-based classification techniques produce improved accuracies using additional variables. This study depicts the capability of object-based image analysis (OBIA) in mapping common urban trees using very high-resolution (VHR) WorldView-2 (WV-2) imagery. The study tests the utility of WV-2 bands and other feature variables in the object-based mapping of common urban trees and other land cover classes. Furthermore, the study compares the utility of Support Vector Machine (SVM) and Random Forest (RF) in the object-based mapping of common urban trees and other land cover classes. The results show that the Normalized Difference Vegetation Index (NDVI), NIR 1 and NIR 2 bands were important in the classification of common urban trees and other land cover classes. The RF classifier performed better than SVM, with an overall accuracy of 91.9% as compared to 87.3% for SVM. The results of this study offer insight to urban authorities with knowledge on the segmentation parameters, classification methods and feature variables for mapping urban trees, valuable in urban tree management.

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.

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.

Detection of anthracnose in tea plants based on hyperspectral imaging

Anthracnose (Gloeosporium theae-sinesis Miyake) is an important and common foliar disease in tea plants and is a severe threat to tea quality and production. Hyperspectral imaging technology enables non-invasive, objective detection of the damages ca by foliar disease and offers significant potential for plant disease prevention and phenotyping. This study proposes a novel method for detecting anthracnose in tea plants based on hyperspectral imaging. By analyzing the spectral sensitivity, we identified disease-sensitive bands at 542, 686, and 754 nm and used these bands to create two new disease indices: the Tea Anthracnose Ratio Index (TARI) and the Tea Anthracnose Normalized Index (TANI). Based on an optimized set of spectral features, a strategy combining unsupervised classification and adaptive two-dimensional thresholding was developed to detect disease scabs. Compared with traditional pixel-based classification methods, the proposed method was not affected by leaf background differences and thereby provides an effective means for disease identification and damage analysis. The validation results gave an overall accuracy of 98% for identifying the disease at the leaf level and 94% at the pixel level. These results suggest that automated and accurate detection of anthracnose-infected tea leaves is possible by using hyperspectral imaging for practical tea-plant protection.

Geo-parcel-based crop classification in very-high-resolution images via hierarchical perception

ABSTRACTThe basic application of remote sensing is classifying surface objects in images. Traditional pixel-based or object-based classification methods are poorly suited to very high-resolution (VHR) images captured by remote sensors with high spatial resolutions. In the field of computer vision, deep learning has recently achieved great advances in natural image processing. Inspired by this, we propose a methodology guided by hierarchical perception to classify crops in VHR images based on geo-parcels. Geo-parcel-based crop classification is used in agriculture and in refined farmland management. The proposed methodology can be divided into three steps: zoning, location and quality. In the first step, the image is divided into blocks based on the road network. In the second step, geographical entities are extracted from every block defined in the zoning step. In the last step, the geographical entity types are identified based on the texture information. These steps provide mutual constraints. In each step, the information is extracted by neural networks that have been adapted to the VHR images. The experimental results indicate that our methodology performs well, with a precision greater than 90%. Furthermore, our methodology combines deep learning techniques and theory regarding image perception by humans, providing a valuable method for processing remote sensing information.

Fine Monitoring of Wetlands at Provincial Large-Scale Using Object-Based Technique and Medium-Resolution Image

Due to extreme similarity of wetland spectra, a significant uncertainty lies in the accuracy of the traditional pixel-based classification, which is a bottleneck in the extraction of wetland information. Object-based image analysis (OBIA) has brought opportunity for fine monitoring of wetland information. However, previous studies on monitoring wetlands have focused mainly on exploratory experiments involving high-resolution images for small areas. The application of OBIA in various medium-resolution images for large areas needs further verification. Here, Landsat and China's HJ-CCD images, a new OBIA mixed binary decision tree, tasseled cap transformation, and field samples were used to refine monitoring of changes in wetlands in Hubei Province. The results showed that while the overall accuracy and Kappa coefficient of the extracted wetland information for 2000 were, respectively, 88.98% and 0.87, the overall accuracy and Kappa coefficient of the detected change were 94.75% and 89.41. This indicated that OBIA performed well with medium-resolution HJ-CCD and Landsat images in monitoring changes in wetlands at provincial scale. The area of wetlands in Hubei increased by 171.03 km2 during 2000–2010. Lakes and reservoirs/ponds increased the most in the province, with respective contributions to the total wetland area of 40.21% (108.97 km2) and 59.17% (160.37 km2). At administrative unit scale, Shiyan Prefecture (157.53 km2) and Fangxian County (317.33 km2) contributed the most to the increase of wetland area. The main reason for the increase in wetland area in Hubei in 2000–2010 was the implementation of major ecological projects during that decade.

Effectiveness of Natura 2000 areas for environmental protection in 21 European countries

To assess the dynamics of changes in CORINE Land Cover classes in areas of the Natura 2000 ecological network, three landscape metrics were examined. Traditional pixel-based classification, with majority rules aggregation based on the example of the CORINE Land Cover dataset, was applied. To identify the possible differences in the considered metrics, according to the year as well as to the country, statistical analysis between the linear mixed model and the variance model with repeated measurements was performed. The results of both tests are very similar. In the 1990–2012 period, the share of CORINE Land Cover class “Artificial areas” in all tested areas increased by 21.1% (the highest growth ratios were recorded in Poland, Portugal, Slovenia and in Spain). On the other hand, such countries as Slovakia, Romania, Germany, Lithuania and Estonia are characterised by the loss of artificial areas. At the same time, the share of “Forest and semi-natural areas” as well as “Water bodies” increased slightly. Negative trends that took place in the periods 1990–2000 and 2000–2006 were effectively stopped in the subsequent period, 2006–2012. Overall, for all the analysed countries, a minimal loss in environmental and landscape diversity was observed. Our results may be used as a basis for drawing conclusions on the effectiveness of environmental and landscape management systems in various countries. They might also constitute the starting point for detailed analysis of the management process.

Changed Detection of Landsat 8 Imagery using Object Based Image Analysis with Particle Swarm Optimization

This paper addresses the problem of classification of hyperspectral remote sensing image and detection of any changes in the land use pattern using it. Traditional pixel-based classification approaches often fail to achieve acceptable accuracy in classifying Landsat images because of its complexity. Therefore, the present work aims to apply object-based image analysis (OBIA), which is a concept that combines segmentation and classification together into one unit. We propose a hybrid OBIA architecture, augmented with particle swarm optimisation (PSO) technique to fine tune different hyperparameters involved with it. We present its success on a classification problem where two sets of Landsat-8 images captured in the year 2016 and 2017 are considered as input and OBIA is applied for classifying the images into four major land use classes and detect any changes in these classes over a period of time. The results are then compared with best pixel-based classification approach known as random forest (RF) classifier to determine its effectiveness in classification of hyperspectral images. Statistical measures like precision, overall accuracy and kappa coefficient are used as a parameter for comparison.

Enhancing Land Cover Mapping through Integration of Pixel-Based and Object-Based Classifications from Remotely Sensed Imagery

Pixel-based and object-based classifications are two commonly used approaches in extracting land cover information from remote sensing images. However, they each have their own inherent merits and limitations. This study, therefore, proposes a new classification method through the integration of pixel-based and object-based classifications (IPOC). Firstly, it employs pixel-based soft classification to obtain the class proportions of pixels to characterize the land cover details from pixel-scale properties. Secondly, it adopts area-to-point kriging to explore the class spatial dependence between objects for each pixel from object-based soft classification results. Thirdly, the class proportions of pixels and the class spatial dependence of pixels are fused as the class occurrence of pixels. Last, a linear optimization model on objects is built to determine the optimal class label of pixels within each object. Two remote sensing images are used to evaluate the effectiveness of IPOC. The experimental results demonstrate that IPOC performs better than the traditional pixel-based hard classification and object-based hard classification methods. Specifically, the overall accuracy of IPOC is 7.64% higher than that of pixel-based hard classification and 4.64% greater than that of object-based hard classification in the first experiment, while the overall accuracy improvements in the second experiment are 3.59% and 3.42%, respectively. Meanwhile, IPOC produces less salt and pepper effect than the pixel-based hard classification method and generates more accurate land cover details and small patches than the object-based hard classification method.

Research on extraction of ginseng planting distribution information based on object-oriented classification--by case study of Fusong country in Jilin

Due to the particularity of ginseng cultivation, the soil fertility of cultivated ginseng is seriously depleted, so that the cultivated ginseng land can not be reused in the short term, and the land area available for cultivating ginseng becomes less and less with the growth of ginseng cultivation time. Therefore, in order to effectively manage ginseng cultivation, and achieve the sustainable use of ginseng land, it is necessary to obtain accurate information on the distribution of ginseng planting space. In this study, the object-oriented classification method based on rule set was used to extract ginseng planting area based on the ZY-3 satellite data in Fusong county, Jilin province. Firstly, multi-scale segmentation of ZY-3 remote sensing image in the study area was made, and the optimal segmentation scale was determined on the basis of multi-scale segmentation results. Secondly, a spectral curve according to the different feature type samples was generated. The similarities and differences between ginseng plot and other types of surface features were analyzed, and a rule set based on the results of spectral analysis was established to achieve the final extraction. The results show that the object-oriented classification method based on rule set can effectively extract the ginseng planting plots in the study area, and solve the problem that the extraction result is broken compared with the traditional pixel-based classification method.

Updating Landsat-based forest cover maps with MODIS images using multiscale spectral-spatial-temporal superresolution mapping

With the high deforestation rates of global forest covers during the past decades, there is an ever-increasing need to monitor forest covers at both fine spatial and temporal resolutions. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat series images have been used commonly for satellite-derived forest cover mapping. However, the spatial resolution of MODIS images and the temporal resolution of Landsat images are too coarse to observe forest cover at both fine spatial and temporal resolutions. In this paper, a novel multiscale spectral-spatial-temporal superresolution mapping (MSSTSRM) approach is proposed to update Landsat-based forest maps by integrating current MODIS images with the previous forest maps generated from Landsat image. Both the 240m MODIS bands and 480m MODIS bands were used as inputs of the spectral energy function of the MSSTSRM model. The principle of maximal spatial dependence was used as the spatial energy function to make the updated forest map spatially smooth. The temporal energy function was based on a multiscale spatial-temporal dependence model, and considers the land cover changes between the previous and current time. The novel MSSTSRM model was able to update Landsat-based forest maps more accurately, in terms of both visual and quantitative evaluation, than traditional pixel-based classification and the latest sub-pixel based super-resolution mapping methods The results demonstrate the great efficiency and potential of MSSTSRM for updating fine temporal resolution Landsat-based forest maps using MODIS images.

Land-use scene classification: a comparative study on bag of visual word framework

With successful launch of high spatial resolution (HSR) sensors, highly detailed spatial information is provided for remote sensing research. This improvement has allowed researchers to monitor environmental changes on a small spatial scale. However traditional pixel-based classification approaches are not able to interpret high spatial resolution remote sensing imagery effectively. Bag of visual words (BoVW) framework, on the other hand, is becoming one of the most popular approaches to validate the performance of remote sensing image datasets. While pixel-based approaches may not fully describe very high-resolution remote sensing images, BoVW model is narrowing the gap between low-level features and high-level semantic features by generating an intermediate description of image features. This paper presents a comparative study to evaluate the potential of using different coding approaches of BoVW model to solve the land-use scene classification problem. Initially, this work summarizes different configurations of BoVW framework in coding and clustering. Later, we perform an extensive evaluation of BoVW on land-use scene classification and retrieval. Finally we draw several conclusions regarding different coding strategies of BoVW, codebook size and number of training images. The approach is validated on two commonly used datasets in remote sensing, UC Merced a 21-class land-use dataset and RSDataset a 19-class satellite scene dataset.

Learning-Based Spatial–Temporal Superresolution Mapping of Forest Cover With MODIS Images

Forest mapping from satellite sensor imagery provides important information for the timely monitoring of forest growth and deforestation, bioenergy potential assessment, and modeling of carbon flux, among others. Due to the daily global revisit rate and wide swath width, MODerate-resolution Imaging Spectroradiometer (MODIS) images are used commonly for satellite-derived forest mapping at both regional and global scales. However, the spatial resolution of MODIS images is too coarse to observe fine spatial variation in forest cover. The last few decades have seen the production of several fine-spatial-resolution satellite-derived global forest cover maps, such as Hansen’s global tree canopy cover map of 2000, which includes abundant spectral, temporal, and spatial prior information about forest cover at a fine spatial resolution. In this paper, a novel learning-based spatial–temporal superresolution mapping approach is proposed to integrate both current MODIS images and prior maps of Hansen’s tree canopy cover, to map present forest cover with a fine spatial resolution. The novel approach is composed of three main stages: 1) automatic generation of 240-m forest proportion images from both 240- and 480-m MODIS images using a nonlinear learning-based spectral unmixing method; 2) downscaling the 240-m forest proportion images to 30 m to predict the class possibilities at the subpixel scale using a temporal-example learning-based downscaling method; and 3) final production of the fine-spatial-resolution forest map by solving a regularization-based optimization problem. The novel approach produced more accurate fine-spatial-resolution forest cover maps in terms of both visual and quantitative evaluation than traditional pixel-based classification and the latest subpixel based superresolution mapping methods. The results show the great efficiency and potential of the novel approach for producing fine-spatial-resolution forest maps from MODIS images.

Review on the Use of Remote Sensing for Urban Forest Monitoring

Urban forests are vital in urban areas because they clean the air, absorb water, and protect the environment from intense heat. Destruction of the urban forest by increased urbanization is a considerable threat to the ecosystem. Hence, urban planners must obtain and manage information about urban forests, but the complexity of urban areas has made these tasks difficult. With developments in remote-sensing technologies, the monitoring and detection of urban forests can be achieved without performing any field measurements. In this study, different remote-sensing imageries and various methods are evaluated to obtain urban forest information. This review demonstrates that very high resolution (VHR) satellite imagery, such as from WorldView-2, is the most efficient data that can be used to obtain urban forest information. The use of the combination of LiDAR data with VHR imagery increases the accuracy of information, particularly about tree crown delineation. Traditional pixel-based classification methods are not effectively applicable to obtain urban tree information because of significant spectral variability in urban areas. An object-based classification technique, which uses spatial, textural, and color information, can be a potential method to detect urban forest and tree species discrimination. The new VHR imaging method, which uses the object-based technique, is recommended to overcome limitations of collecting urban forest information.