Object-based rice mapping using time-series and phenological data

Remote sensing techniques are often used in mapping rice, but high quality time-series remote sensing data are difficult to obtain due to the cloudy weather of rice growing areas and long satellite revisit interval. As such, rice mapping is usually based on mono-temporal Landsat TM/ETM+ data, which have large uncertainties due to the spectral similarity of different vegetation types. Moreover, conventional pixel-based classification method is unable to meet the required accuracy for rice mapping. Therefore, this study proposes a new strategy for mapping rice in cloud-prone areas using fused data of Landsat-8 OLI time-series and phenological parameters, based on the object-based method. We determine the critical growth stages of paddy rice from observed phenological data and MODIS-NDVI time-series data. The spatial and temporal adaptive reflectance fusion model (STARFM) is used to blend the MODIS and Landsat data to obtain a multi-temporal Landsat-like dataset for classification. Finally, an object-oriented algorithm is used to extract rice paddies from the Landsat-like, time-series dataset. The validation experiments show that the proposed method can provide high accuracy rice mapping, with an overall accuracy of 92.38% and a kappa coefficient of 0.85.     

Is the phenology-based algorithm for mapping deciduous rubber plantations applicable in an emerging region of northern Laos?

Updated information of rubber plantations is essential for assessing socioeconomic and environmental impacts, especially in the emerging region of northern tropics. Here, a phenological method was modified to detect rubber plantations using Landsat Operational Land Imager (OLI) imagery in Phongsaly Province of northern Laos, where it begun a rubber boom in the mid-2000s due to geo-economic cooperation. It highlighted the landscape and pixel differences of deciduous rubber plantations in the tri-temporal phases (i.e., pre-defoliation, defoliation, and foliation) during the dry season due to phenological changes. Six commonly used vegetation indices (VIs), including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), Atmospherically Resistant Vegetation Index (ARVI), Normalized Burn Ratio (NBR and NBR2) derived from OLI imagery during 2013–2016 were compared to determine the most suitable VI for discriminating the phenological differences of rubber plantations from natural forests. Then, the Differences of Normalized Burn Ratio (DNBR) was applied to generate the 30 m map of rubber plantations in 2016, by combining two masks of Landsat-derived forest and suitable elevation for rubber trees cultivation. The resultant map of rubber plantations had a classification accuracy of 93.7% and the Kappa coefficient of 0.848. Our study demonstrated the usefulness of the Landsat-derived tri-temporal phenological DNBR approach in an emerging region of northern Laos, despite requiring more scenes compared with single- and double temporal window methods.     

A robust object-based shadow detection method for cloud-free high resolution satellite images over urban areas and water bodies

Unwanted contrast in high resolution satellite images such as shadow areas directly affects the result of further processing in urban remote sensing images. Detecting and finding the precise position of shadows is critical in different remote sensing processing chains such as change detection, image classification and digital elevation model generation from stereo images. The spectral similarity between shadow areas, water bodies, and some dark asphalt roads makes the development of robust shadow detection algorithms challenging. In addition, most of the existing methods work on pixel-level and neglect the contextual information contained in neighboring pixels. In this paper, a new object-based shadow detection framework is introduced. In the proposed method a pixel-level shadow mask is built by extending established thresholding methods with a new C₄ index which enables to solve the ambiguity of shadow and water bodies. Then the pixel-based results are further processed in an object-based majority analysis to detect the final shadow objects. Four different high resolution satellite images are used to validate this new approach. The result shows the superiority of the proposed method over some state-of-the-art shadow detection method with an average of 96% in F-measure.     

Degradation monitoring in Silvo-pastoral systems: A case study of the Mediterranean region of Turkey

To identify policies that will promote positive effects and mitigate negative ones of grazing is a major challenge in the Silvo-pastoral system. This paper presents the role of examining land-cover change trajectories by remote sensing imagery in grazing policy monitoring. The study was conducted for Duzlercami forest ecosystem located in the Mediterranean geographical region of Turkey and administrated by the General Directorate of Forestry (GDF) of the Ministry of Forestry and Water Affairs. Time series land-cover datasets from Landsat images between 1988 and 2016 were collected and classified. To link the conversions among trajectories and grazing policy, class level landscape metrics derived from the classified images were used. To validate the approach, yearly grazing-plans managed by GDF and populations of livestock were used. Results of this research have indicated that even though there is a yearly grazing plan, overgrazing can happen on the pilot site, and it can be easily identified by the destruction of woody vegetation. The notable correlation (r²?=?0.89) between degraded woody vegetation and cattle population has occurred in the last 30?years in the landscape, and Landsat imagery can effectively support the grazing policy mapping and monitoring.     

Hierarchical object oriented classification using very high resolution imagery and LIDAR data over urban areas

Urban land cover information extraction is a hot topic within urban studies. Heterogeneous spectra of high resolution imagery—caused by the inner complexity of urban areas—make it difficult. In this paper a hierarchical object oriented classification method over an urban area is presented. Combining QuickBird imagery and light detection and ranging (LIDAR) data, nine kinds of land cover objects were extracted. The Spectral Shape Index (SSI) method is used to distinguish water and shadow from black body mask, with 100% classification accuracy for water and 95.56% for shadow. Vegetation was extracted by using a Normalized Difference Vegetation Index (NDVI) image at first, and then a more accurate classification result of shrub and grassland is obtained by integrating the height information from LIDAR data. The classification accuracy of shrub was improved from 85.25% to 92.09% and from 82.86% to 97.06% for grassland. More granularity of this classification can be obtained by using this method. High buildings and low buildings can, for example, be distinguished from the original building class. Road class can also be further classified into roads and crossroads. The comparison of the classification accuracy between this method and the traditional pixel-based method indicates that the total accuracy is improved from 69.12% to 89.40%.     

Field-based landcover classification using TerraSAR-X texture analysis

The present study aims to evaluate the field-based approach for the classification of landcover using high-resolution SAR data. TerraSAR-X (TSX) strip mode imagery, coupled with digital ortho-photos (DOPs) with 20 cm spatial resolution was used for landcover classification and parcel mapping respectively. Different filtering and analysis techniques were applied to extract textural information from the TSX image in order to assess the enhancement of the classification accuracy. Several attributes of parcels were derived from the available TSX images in order to define the most suitable parameters discriminating between different landcover types. Then, these attributes were further statistically analysed in order to define separability and thresholds between different landcover types. The results showed that textural analysis resulted in high classification accuracy. Hence, this paper confirms that integrated landcover classification using the textural information of TerraSAR-X has a high potential for landcover mapping.     

Land use/land cover classification using time series Landsat 8 images in a heavily urbanized area

It is of great significance to timely, accurately, and effectively monitor land use/cover in city regions for the reasonable development and utilization of urban land resources. The remotely sensed dynamic monitoring of Land use/land cover (LULC) in rapidly developing city regions has increasingly depended on remote-sensing data at high temporal and spatial resolutions. However, due to the influence of revisiting periods and weather, it is difficult to acquire enough time-series images with high quality at both high temporal and spatial resolution from the same sensor. In this paper we used the temporal-spatial fusion model ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) to blend Landsat8 and MODIS data and obtain time-series Landsat8 images. Then, land cover information is extracted using an object-based classification method. In this study, the proposed method is validated by a case study of the Changsha City. The results show that the overall accuracy and Kappa coefficient were 94.38% and 0.88, respectively, and the user/producer accuracies of vegetation types were all over 85%. Our approach provides an accurate and efficient technical method for the effective extraction of land use/cover information in the highly heterogeneous regions.     

基于图论分割的多光谱图像非监督分类方法

针对传统基于像素的多光谱遥感图像分类方法存在的"麻点"现象、采样成本高等问题,提出了一种基于图论分割的非监督分类方法,首先采用基于图论的分割算法,按局部邻近相似像素点分割成若干子区域,再以分割后子区域为基本单元,整体进行模糊 C均值聚类,最终实现对多光谱图像的非监督分类.实验证明,该方法结合了局部邻近像素点的相互关系以及相似区域的整体特征,有效解决了麻点问题,具有较高的分类精度和算法效率,降低了采样成本.     

Evaluating the statistical performance of less applied algorithms in classification of worldview-3 imagery data in an urbanized landscape

In recent decade, analyzing the remotely sensed imagery is considered as one of the most common and widely used procedures in the environmental studies. In this case, supervised image classification techniques play a central role. Hence, taking a high resolution Worldview-3 over a mixed urbanized landscape in Iran, three less applied image classification methods including Bagged CART, Stochastic gradient boosting model and Neural network with feature extraction were tested and compared with two prevalent methods: random forest and support vector machine with linear kernel. To do so, each method was run ten time and three validation techniques was used to estimate the accuracy statistics consist of cross validation, independent validation and validation with total of train data. Moreover, using ANOVA and Tukey test, statistical difference significance between the classification methods was significantly surveyed. In general, the results showed that random forest with marginal difference compared to Bagged CART and stochastic gradient boosting model is the best performing method whilst based on independent validation there was no significant difference between the performances of classification methods. It should be finally noted that neural network with feature extraction and linear support vector machine had better processing speed than other.     

Landsat thematic mapper studies of land cover spatial variability related to hydrology

The Landsat 4 and 5 Thematic Mapper (TM) provides increased spatial, spectral, and radiometric capability relative to the Multispectral Scanner (MSS). Visual inspection of TM imagery confirms this. Land cover detail is evident that would be of use in watershed management and planning activities. Specific studies have been conducted in Georgia, West Virginia, Michigan and Maryland to compare MSS and TM for urbanizing watersheds, wetlands, and floodplain mapping situations. These studies show that only modest improvements in classification accuracy (Anderson Level I/II) have been achieved using existing classification approaches. An attempt to identify the visibly apparent interstate highways and secondary and residential streets in TM data via conventional approaches failed due to an inability to derive separable spectral signatures. The basis for a non-parametric approach to classification is presented in which roads are identified by locating linear local minima in the greenness transformed dimension. Preliminary results indicate that such a method provides more reliable road locations than MSS or TM used singly.     

Examining LiDAR – WorldView-3 data synergy to generate a detailed stand map in a mixed forest in the north-west of Turkey

Although stand delineation approach based on aerial photographs and field survey produces high accuracy maps, it is labour-intensive and time consuming. Furthermore, conventional forest stand maps may have some uncertainties that can hardly be verified due to the experiments and skills of photo-interpreters. Therefore, researchers have been seeking more objective and cost-effective methods for forest mapping. LiDAR (Light Detection and Ranging) data have a high potential to automatically delineate forest stands. Unlike optical sensors, LiDAR height data provides information about both the horizontal and vertical structural characteristics of forest stands. However, it deprives of spectral data that may be successfully used in separating tree species. In this study, we investigate the potential of LiDAR – WorldView-3 data synergy for the automatic generation of a detailed forest stand map which can be used for a tactical forest management plan. Firstly, image segmentation was applied to LiDAR data alone and LiDAR/WorldView-3 data set in order to obtain the most suitable image objects representing forest stands. Visual inspection of the segmentation results showed that image objects based on the LiDAR/WorldView-3 data set were more compatible with the reference forest stand boundaries. After the segmentation process, the LiDAR and LiDAR/WorldView-3 data sets were independently classified using object-based classification method. We tested two levels of classification. The first was a detailed classification with 14 classes considering reference stand types. The second was the rough classification with 9 classes where some stand types were combined. The mean, standard deviation and texture features of LiDAR metrics and spectral information were used in the classification. The accuracy assessment results of LiDAR data showed that the Overall Accuracy (OA) was calculated as 0.31 and 0.43, and the Kappa Index (KIA) was calculated as 0.26 and 0.32 for the detailed and rough classifications, respectively. For the LiDAR/WorldView-3 data set, the OA values were calculated as 0.50 and 0.61, and the KIA were calculated as 0.46 and 0.55 for the detailed and rough classifications, respectively. These results showed that the forest stand map derived from the LiDAR/WorldView-3 data synergy is more compatible with the reference forest stand map. In conclusion, it can be said that the forest stand maps produced in this study may provide strategic forest planning needs, but it is not sufficient for tactical forest management plans.     

The origin and successional status of anthropogenic dwarf shrub heath in Newfoundland

There are 1.2 × 10⁶ ha of heath vegetation on the island of Newfoundland. Some of this heath is of climatic or edaphic origin, but most of it has originated because of increasing frequency of fire and cutting following settlement of the area by European man since discovery in 1497. Modern fire suppression has been successful in reducing the size and frequency of fires in the last 40–50 years, however, natural forest regeneration is still not apparent in the anthropogenic heath.The ecotone between the forest-heath appears to have stabilized following an initial influx of trees up to 10 m from the forest margin. Analysis of the age structure of the invading trees demonstrates that after a period of 10 years new tree establishment is reduced to less than 5 percent of the total establishment. This decreased establishment over time is correlated with a coincidental increase in dwarf shrub heath vegetation that creates undesirable germination conditions for tree seed entering the heath. Even with ideal seedbed conditions for germination, the short distance of seed dispersal (10–20 m) and the excessively slow growth rates of trees, limit forest encroachment to a rate of 10 cm per year. At this rate more than a thousand years would be required for successional encroachement of even 2–3 ha of heathland. The successional status of the heath vegetation is considered to be permanent even though traditional concepts of secondary succession and chronosequence would predict the recovery of a climax forest vegetation.     

Canadian landsat studies for monitoring hydrologic conditions and coastal environments: A summary

This report summarizes a major paper reviewing Canadian work using Landsat imagery for studying changes in lakes and in coastal environments. The nature of environmental change is discussed. For lakes and coastal environments, it is suggested that change is either seasonal, long term, short term or constant; examples of each are given. There is also an important distinction between natural and man-induced change. Outlined in the paper are studies of the filling of the LG2 Reservoir, water level and vegetation changes in the Peace-Athabasca Delta, possible vegetation changes due to the construction of Roberts Bank Port near Vancouver and measurement of surface suspended sediment concentration in the Bay of Fundy using chromaticity analysis.     

Comparison of decision tree methods for finding active objects

The automated classification of objects from large catalogs or survey projects is an important task in many astronomical surveys. Faced with various classification algorithms, astronomers should select the method according to their requirements. Here we describe several kinds of decision trees for finding active objects by multi-wavelength data, such as REPTree, Random Tree, Decision Stump, Random Forest, J48, NBTree, AdTree. All decision tree approaches investigated are in the WEKA package. The classification performance of the methods is presented. In the process of classification by decision tree methods, the classification rules are easily obtained, moreover these rules are clear and easy to understand for astronomers. As a result, astronomers are inclined to prefer and apply them, thus know which attributes are important to discriminate celestial objects. The experimental results show that when various decision trees are applied in discriminating active objects (quasars, BL Lac objects and active galaxies) from non-active objects (stars and galaxies), ADTree is the best only in terms of accuracy, Decision Stump is the best only considering speed, J48 is the optimal choice considering both accuracy and speed.     

Comparison of Landsat OLI,ASTER, and Sentinel 2A data in lithological mapping : A Case study of Rich area (Central High Atlas,Morocco)

The eastern part of the Rich area consists of the massive Paleozoic and Meso-Cenozoic cover formations that present the geodynamic development of the study area, where is characterized by various carbonate facies of Jurassic age. The geographical characteristic of the study area leaves the zone difficult to map by conventional methods. The objective of this work focuses on the mapping of the constituent lithological units of the study area using multispectral data of Landsat OLI, ASTER, and Sentinel 2A MSI. The processing of these data is based on a precise methodology that distinguishs and highlights the limits of the different lithological units that have an approximate similarity of spectral signature. Three techniques were used to enhance the image including Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA). Lithological mapping was performed using two types of supervised classification : Maximum likelihood classifier (MLC) and Support Vector Machine (SVM).The results of processing data show the effectiveness of Sentinel 2A data in mapping of lithological units than the ASTER and Landsat OLI data. The classification evaluation of two methods of the Sentinel 2A MSI image showed that the SVM method give a better classification with an overall accuracy of 93,93% and a Kappa coefficient of 0.93, while the MLC method present an overall accuracy of 82,86% and a Kappa coefficient of 0.80. The results of mapping obtained show a good correlation with the geological map of the study area as well as the efficiency of remote sensing in identification of different lithological units in the Central High Atlas.     

An optimal selection of probability distribution functions for unsupervised land cover classification of PALSAR-2 data

Different types of classification techniques are available in the literature for the classification of Synthetic Aperture Radar (SAR) data into various land cover classes. Various SAR images are available for land cover classification such as ALOS PALSAR (PALSAR-1, PALSAR-2), RADARSAT and ENVISAT. In this paper, we have attempted to explore probability distribution function (pdf) based land cover classification using PALSAR-2 data. Over 20 different statistical distribution functions are analyzed for different classes based on statistical parameters. Probability distribution functions are selected based on Chi-squared goodness of fit test for each individual class. A decision tree based classifier is developed for classification based on the selected pdf functions and its statistical parameters. The proposed classification approach has an accuracy of 83.93%.     

Towards a high spatial resolution limit for pixel-based interpretations of optical remote sensing data

The divergence of horizontal radiation in vegetation canopies is generally considered to be of negligible consequence in algorithms designed for the physically-based interpretation of space borne observations. However, non-zero horizontal radiation balances are likely to occur if the internal variability of a vegetation target and the typical distances that photons may travel horizontally within such three-dimensional (3-D) media extend to spatial scales that are similar to or larger than those of the nominal footprint of the measuring sensor. Detailed radiative transfer simulations in 3-D coniferous forest environments are presented to document the typical distances that photons may travel in such media, and to quantify the impact that the resulting net horizontal fluxes may have with respect to the local and domain-averaged canopy reflectance. Based on these simulations it is possible to identify a fine spatial resolution limit beyond which pixel-based interpretations of remote sensing data over tall forested areas should be avoided because the horizontal radiation transport at the surface may contribute to 10% or more of the measured reflectance signature of the target pixel.     

Shortwave infrared detection of vegetation

Shortwave infrared sensors were included on Thematic Mapper to observe vegetation reflected radiance patterns that are related to leaf water content. However, there was some uncertainty whether these measurements would increase the information content of multispectral measurements beyond that provided by visible and near infrared measurements. Analysis of field measurements for corn and soybeans observed throughout the growing season shows that shortwave infrared measurements enhance discrimination between these species, particularly in mid-season. Modeling the canopy reflectances shows that differential leaf absorptance can produce the observed pattern. Analysis of coincident aerial photography suggests that within canopy shadowing is also important. Too few studies of leaf optical properties have been conducted to permit generalization of the results to other vegetation species but the results do show that shortwave infrared measurements contribute new information about vegetation not previously available in visible and near infrared measurements.     

Detection and mapping vegetation cover based on the Spectral Angle Mapper algorithm using NOAA AVHRR data

Satellite data, taken from the National Oceanic and Atmospheric Administration (NOAA) have been proposed and used for the detection and the cartography of vegetation cover in North Africa. The data used were acquired at the Analysis and Application of Radiation Laboratory (LAAR) from the Advanced Very High Resolution Radiometer (AVHRR) sensor of 1 km spatial resolution. The Spectral Angle Mapper Algorithm (SAM) is used for the classification of many studies using high resolution satellite data. In the present paper, we propose to apply the SAM algorithm to the moderate resolution of the NOAA AVHRR sensor data for classifying the vegetation cover. This study allows also exploiting other classification methods for the low resolution. First, the normalized difference vegetation index (NDVI) is extracted from two channels 1 and 2 of the AVHRR sensor. In order to obtain an initial density representation of vegetal formation distribution, a methodology, based on the combination between the threshold method and the decision tree, is used. This combination is carried out due to the lack of accurate data related to the thresholds that delimit each class. In a second time, and based on spectral behavior, a vegetation cover map is developed using SAM algorithm. Finally, with the use of low resolution satellite images (NOAA AVHRR) and with only two channels, it is possible to identify the most dominant species in North Africa such as: forests of the Liege oaks, other forests, cereal’s cultivation, steppes and bar soil.