Multispectral Scanner Research Articles

Evolution of Yellow River Delta Coastline Based on Remote Sensing from 1976 to 2014, China

Coastal regions are threatened by natural processes, such as erosion driven by storm surges and the effect of jetties, as well as by human behavior. The coastline of the Yellow River Delta (YRD) was monitored using the general high-tide line method, which combines Remote sensing (RS) and geographic information system (GIS) technology, using multi-spectral scanner (MSS), thematic mapper (TM), and enhanced thematic mapper plus (ETM+) images of the YRD from 1976 to 2014 as a data source. The results demonstrated that the shape and length of the YRD coastline has changed dramatically since 1976. The course of the Diaokouhe channel has resulted in mainly inland erosion in the north, and is primarily marine erosion; therefore, it was termed an erosion-type estuary. However, the coastline of the Qingshuigou course has moved seaward, demonstrating an accretion stage, and was therefore termed an accretion-type estuary. The coastline advanced forward before 1997 and shrank after 2003 in the southern part of the river mouth, which was due to the shift in the river mouth in 1996. It has continually extended outward in the northern part of the river mouth from 2003 onward. The coastline in the southern part of the river mouth has moved randomly, with the occurrence of both erosion and sedimentation caused by land reclamation and sea wave intrusion. In most cases, the coastline has extended offshore, especially in the northern part of the river mouth. The YRD coastline has changed frequently and rapidly from 1992 to 2014. The river mouth channel, river water and sediments, and precipitation were the major factors affecting the YRD. The YRD coastline was mainly in an accretion stage during flow periods. The erosion rate decreased and tended to be stable during a dry period. The coastline was basically stable when dry periods occurred over a long period. The location of Yellow River ports and sea erosion were the main factors driving coastline changes. The coastline was mainly influenced by the flow path of the Yellow River, with recent human activity also becoming a factor.

Remote sensing and geographic information systems techniques in studies on treeline ecotone dynamics

We performed a meta-analysis on over 100 studies applying remote sensing (RS) and geographic information systems (GIS) to understand treeline dynamics. A literature search was performed in multiple online databases, including Web of Knowledge (Thomson Reuters), Scopus (Elsevier), BASE (Bielefeld Academic Search Engine), CAB Direct, and Google Scholar using treeline-related queries. We found that RS and GIS use has steadily increased in treeline studies since 2000. Spatial-resolution RS and satellite imaging techniques varied from low-resolution MODIS, moderate-resolution Landsat, to high-resolution WorldView and aerial orthophotos. Most papers published in the 1990s used low to moderate resolution sensors such as Landsat Multispectral Scanner and Thematic Mapper, or SPOT PAN (Panchromatic) and MX (Multispectral) RS images. Subsequently, we observed a rise in high-resolution satellite sensors such as ALOS, GeoEye, IKONOS, and WorldView for mapping current and potential treelines. Furthermore, we noticed a shift in emphasis of treeline studies over time: earlier reports focused on mapping treeline positions, whereas RS and GIS are now used to determine the factors that control treeline variation.

Spatial and Temporal Variability of Open-Ocean Barrier Islands along the Indus Delta Region

Barrier islands (BIs) have been designated as the first line of defense for coastal human assets against rising sea level. Global mean sea level may rise from 0.21 to 0.83 m by the end of 21st century as predicted by the Intergovernmental Panel on Climate Change (IPCC). Although the Indus Delta covers an area of 41,440 km² surrounded by a chain of BIs, this may result in an encroachment area of 3750 km2 in Indus Delta with each 1 m rise of sea level. This study has used a long-term (1976 to 2017) satellite data record to study the development, movement and dynamics of BIs located along the Indus Delta. For this purpose, imagery from Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) sensors was used. From all these sensors, the Near Infrared (NIR) band (0.7–0.9 µm) was used for the delineation and extraction of the boundaries of 18 BIs. It was found that the area and magnitude of these BIs is so dynamic, and their movement is so great that changes in their positions and land areas have continuously been changing. Among these BIs, 38% were found to be vulnerable to oceanic factors, 37% were found to be partially vulnerable, 17% remained partially sustainable, and only 8% of these BIs sustained against the ocean controlling factors. The dramatic gain and loss in area of BIs is due to variant sediment budget transportation through number of floods in the Indus Delta and sea-level rise. Coastal protection and management along the Indus Delta should be adopted to defend against the erosive action of the ocean.

Land Use/Land Cover (LULC) Using Landsat Data Series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco

The study of land use/land cover (LULC) has become an increasingly important stage in the development of forest ecosystems strategies. Hence, the main goal of this study was to describe the vegetation change of Azrou Forest in the Middle Atlas, Morocco, between 1987 and 2017. To achieve this, a set of Landsat images, including one Multispectral Scanner (MSS) scene from 1987; one Enhanced Thematic Mapper Plus (ETM+) scene from 2000; two Thematic Mapper (TM) scenes from 1995 and 2011; and one Landsat 8 Operational Land Imager (OLI) scene from 2017; were acquired and processed. Ground-based survey data and the normalized difference vegetation index (NDVI) were used to identify and to improve the discrimination between LULC categories. Then, the maximum likelihood (ML) classification method was applied was applied, in order to produce land cover maps for each year. Three classes were considered by the classification of NDVI value: low-density vegetation; moderate-density vegetation, and high-density vegetation. Our study achieved classification accuracies of 66.8% (1987), 99.9% (1995), 99.8% (2000), 99.9% (2011), and 99.9% (2017). The results from the Landsat-based image analysis show that the area of low-density vegetation was decreased from 27.4% to 2.1% over the past 30 years. While, in 2017, the class of high-density vegetation was increased to 64.6% of the total area of study area. The results of this study show that the total forest cover remained stable. The present study highlights the importance of the image classification algorithms combined with NDVI index for better understanding the changes that have occurred in this forest. Therefore, the findings of this study could assist planners and decision-makers to guide, in a good manner, the sustainable land development of areas with similar backgrounds.

Forest Cover Change Analysis Using Remote Sensing Techniques in Madhupur Sal Forest of Bangladesh

Deforestation, degradation, damages, transformation and over exploitation of forests are the common problem in different parts of the world. Timely monitoring and assessment of forest resources may help to address and identify the above mentioned problems and thus proper guidance may be given the forest resources manager for rational planning and management of forests. Apart from the conventional methods of forest monitoring, remote sensing with its unique capability of synoptic viewing, real time and repetitive nature offers a potential tool for monitoring and evaluation of forest resources and hence remote sensing technology has been successfully used in various studies like forest inventory, monitoring of forest cover changes and forest damage assessment. In the present research forest cover change analysis in ‘Madhupur Sal Forest’ located in central part of Bangladesh has been investigated using satellite remote sensing data and spatial analysis. Transformation of ‘Sal forest’ to other landuse has been studied using the Landsat MSS (Multi Spectral Scanner) data of 1973 and Landsat 8 OLI (Operational Land Imager) data of 2015. Driving forces behind the transformation of ‘Sal forest’ has also been investigated through GPS (Global Positioning System) based ground verification and interview with the people living in the locality.J. Environ. Sci. & Natural Resources, 10(2): 73-78 2017

Feasibility, Acceptability, and Adoption of Digital Fingerprinting During Contact Investigation for Tuberculosis in Kampala, Uganda: A Parallel-Convergent Mixed-Methods Analysis.

BackgroundIn resource-constrained settings, challenges with unique patient identification may limit continuity of care, monitoring and evaluation, and data integrity. Biometrics offers an appealing but understudied potential solution.ObjectiveThe objective of this mixed-methods study was to understand the feasibility, acceptability, and adoption of digital fingerprinting for patient identification in a study of household tuberculosis contact investigation in Kampala, Uganda.MethodsDigital fingerprinting was performed using multispectral fingerprint scanners. We tested associations between demographic, clinical, and temporal characteristics and failure to capture a digital fingerprint. We used generalized estimating equations and a robust covariance estimator to account for clustering. In addition, we evaluated the clustering of outcomes by household and community health workers (CHWs) by calculating intraclass correlation coefficients (ICCs). To understand the determinants of intended and actual use of fingerprinting technology, we conducted 15 in-depth interviews with CHWs and applied a widely used conceptual framework, the Technology Acceptance Model 2 (TAM2).ResultsDigital fingerprints were captured for 75.5% (694/919) of participants, with extensive clustering by household (ICC=.99) arising from software (108/179, 60.3%) and hardware (65/179, 36.3%) failures. Clinical and demographic characteristics were not markedly associated with fingerprint capture. CHWs successfully fingerprinted all contacts in 70.1% (213/304) of households, with modest clustering of outcomes by CHWs (ICC=.18). The proportion of households in which all members were successfully fingerprinted declined over time (ρ=.30, P<.001). In interviews, CHWs reported that fingerprinting failures lowered their perceptions of the quality of the technology, threatened their social image as competent health workers, and made the technology more difficult to use.ConclusionsWe found that digital fingerprinting was feasible and acceptable for individual identification, but problems implementing the hardware and software lead to a high failure rate. Although CHWs found fingerprinting to be acceptable in principle, their intention to use the technology was tempered by perceptions that it was inconsistent and of questionable value. TAM2 provided a valuable framework for understanding the motivations behind CHWs’ intentions to use the technology. We emphasize the need for routine process evaluation of biometrics and other digital technologies in resource-constrained settings to assess implementation effectiveness and guide improvement of delivery.

Evaluating changes in treeline position and land surface phenology in Sikkim Himalaya

Mountain ecosystems of the Himalaya have warmed more rapidly in recent decades than other areas of the globe. Landscape-level delineation of treeline position and its dynamics can give insight regarding climatic variability. Landsat-2 Multispectral Scanner (MSS), Resourcesat-2 Linear Imaging Self Scanning Sensor (LISS-III) and National Oceanic and Atmospheric Administration- Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived normalized difference vegetation index (NDVI) was used to study the long-term treeline dynamics. The treeline has shifted vertically 301 ± 66 m upward in 37 years at a rate of c. 81 m decade−1. The minimum air temperature has increased at a rate of 0.3 °C decade−1 (p < 0.001) depicting the favourable scenario for treeline growth in this temperature limited ecosystem. The annual cumulative precipitation has decreased at a rate of 206.5 mm decade−1. The length of growing season (LOS) has increased and the start (SOS) and end of growing season (EOS) has got earlier in the 1977 treeline. The elevation shifts and phenological changes of the treeline were observed in the warming scenario.

Spatio-Temporal Analysis of Land Use/Land Cover Changes in an Ecologically Fragile Area—Alappuzha District, Southern Kerala, India

Concomitant with careless human interference in the delicate environmental balance, the Earth’s surface is witnessing a variety of changes in land use and land cover (LULC). Acquisition of a sound understanding of LULC is an important aspect of maintaining a sustainable, benign, healthy environment. The present work highlights a spatiotemporal study on the LULC features of Alappuzha District, an ecologically fragile area in southern Kerala, a state in South India. The study area faces diverse environmental challenges including decline of landforms, rising sea levels, population expansion and anthropogenic encroachments on the ecological balance. This investigation compiles an audited account of the modifications, in each class of LULC, using geospatial technologies. We interpreted satellite imagery from the Landsat 8 and the Landsat multispectral scanner for the years 1973 and 2017. The LULC aspects were categorized into seven classes: waterbody, waterlogged area, mixed vegetation, built-up land, uncultivated area, paddy field and sandy area. Our findings affirm that the expansiveness of the built-up land area is directly proportional to the growth of the population. Advanced technologies such as remote sensing and geographic information system accentuate alterations in land use patterns over time and the extent to which the changes affect the human population and the natural habitat. We verified the results of our research by assessment of accuracy and ground truth confirmation of the LULC features.

Urban object extraction using Dempster Shafer feature-based image analysis from worldview-3 satellite imagery

ABSTRACTA detailed and up-to-date land use of the urban environment is essentially required in many applications. Very high-resolution (VHR), Multispectral Scanner System (MSS) Worldview-3 (WV-3) satellite imagery provides detailed information on urban characteristics, which should be professionally mined. In this research, WV-3 was processed by machine learning (ML) methods to extract the most accurate urban features. Fuze-Go panchromatic sharpening in conjunction with atmospheric and topographic correction was initially utilized to increase the image quality and colour contrast. Three image analysis approaches including, current pixel-based image analysis (PBIA), object-based image analysis (OBIA) and new feature-based image analysis (FBIA) were implemented on WV-3 image. The k-nearest neighbour (k-NN), Naive Bayes (NB), support vector machine (SVM) classifiers were represented by PBIA, the Decision Tree (DT) classifier was examined as OBIA and the Dempster–Shafer (DS) fusion classifier was manifested for the first time as FBIA. In order to engage DS as FBIA, four types of Belief Masses, namely, Precision, Recall, Overall Accuracy, and kappa coefficient (ĸ) were implemented and compared to assign the most likelihood urban features. All the applied classifiers were also trained on the first site and then tested on another site to examine the transferability. The accuracy, reliability, and computational time of all classifiers were examined by confusion matrix and McNemar assessment. Results show improvements on the detailed urban extraction obtained using the proposed FBIA with 92.2% overall accuracy in compared with PBIA and OBIA. The FBIA result of urban extraction is more consistent when transferred to another study area and consumes much lesser time than OBIA. Also, the precision mass belief measurement achieved highest efficiency regarding receiver operating characteristic (ROC) curve rate.

Assessment of land use and land cover changes from 1979 to 2017 and biodiversity & land management approach in Quirimbas National Park, Northern Mozambique, Africa

National parks are established with the aim of guaranteeing and protecting natural biodiversity and ecosystems, in a multi-level and integrated approach. Biodiversity conservation, management of protected areas and sustainable development with strategies targeting rural populations are current challenges in Africa, in the context of exponential human population growth, overexploitation of natural resources, forest destruction and climate change. The Quirimbas National Park (QNP) has been suffering from severe and constant threats originating from different sources and changes in land use and land cover. These changes, and in the context of global climate change, pose permanent challenges to the managers of this conservation area of Mozambique. The research aimed to analyze the historical and recent LULC over the last 38 years, to provide consistent and scientific information for decision making on biodiversity conservation approaches; to identify the main changes and their impacts on the ecosystem to implement/develop appropriate mitigation strategies. A combined and integrated methodological approach has been developed from satellite imagery analyzes of Landsat 2 and 5 MSS (Multispectral Scanner); Landsat 5 TM (Thematic Mapper), Landsat 8 OLI (Operational Land Imager), and fieldwork (field observation and communities and QNP staff meetings). The results show that the overall map classification obtained was between very good and excellent: 1979 - Kappa 71.84%, Overall Accuracy 86.55%; 1989 - Kappa 83.49%, Overall Accuracy 93.01%; 1999 - Kappa 85.03%, Overall Accuracy 90.07%; 2009 - Kappa 79.57%, Overall Accuracy 86.42%; 2017 - Kappa 80.24%, Overall Accuracy 86.95%. For 38 years, the QNP lost about 301,761.7ha, corresponding to 41.67% of the total QNP coverage land. The main causes are associated with intensive agriculture, human settlements, population growth, illegal exploitation of forest resources and miners inside of the Quirimbas National Park. The impact extends from territory reduction and fragmentation to vegetation and animal biodiversity loss, human-wildlife conflicts, habitat connectivity loss, species isolation and scaring, and basic resources scarcity for the community's livelihoods.

Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future.

Calculating long-term changes in Lake Chapala’s area and water volume using remote sensing and field data

Mexico’s largest freshwater body, Lake Chapala, is the main source of drinking water for over 4-million inhabitants. This study used satellite and field data to obtain annual estimates of Lake Chapala’s area and water volume from 1973 to 2009, as well as water intakes from 1934 to 2013. Twenty-one Landsat images (multispectral scanner system, 2, 5, and 7) for the period of 1973 to 2009 were processed. Gap filling correction was applied to Landsat 7 enhanced thematic mapper plus scan line corrector-off images. Binary water/no-water segmentation was obtained with a Markov random field model using the normalized difference water index. An area-based model was developed to calculate water volume, which used the lake’s area estimated based on the images as well as an exponential function obtained from area-elevation curves. Three of the resulting water/no-water layers were validated against reference points taken from aerial photography. Overall accuracies of 77% to 90% were obtained. A correlation coefficient of 0.9 resulted when comparing the results from using remote sensing to calculate water volume against field measurements. Given the observed reduction in volume, we concluded that it will be difficult to continue to use the lake as the main source of freshwater for the Guadalajara metropolitan area without substantial interventions.

Abstract LB-352: Identification of myeloid-derived suppressor cells in solid tumors by multiplex IHC

Abstract In healthy individuals, immature myeloid cells undergo differentiation into Macrophages, Dendritic Cells or Neutrophils. In individuals with several types of cancers, Tumor-Derived Factors promote a pathological differentiation of these immature cells into Myeloid-Derived Suppressor Cells (MDSCs) that phenotypically resemble either Macrophages (M-MDSCs) or Neutrophils (PMN-MDSCs). Rather than supporting antitumoral immunity like their regular counterparts, these abnormal, highly immunosuppressive cells, impair antitumoral immunity and promote tumor development. In addition to MDSCs, the tumor micro-environment is also infiltrated by Tumor-Associated Macrophages (TAMs), which can be roughly divided in two main subgroups, M1- and M2-polarized Macrophages, that exhibit anti- or pro-tumor activity, respectively. Over the last years, MDSCs have been recognized as valuable prognostic and/or predictive biomarkers, and there is a raising need for methods to phenotype these cells in clinical trials. Myeloid cell subpopulations are all closely related, and as a consequence, the phenotyping of each subset is complex, due to the high number of markers needed to discriminate them from the others. Recent advances have been made to define standard protocols to discriminate M- and PMN-MDSCs from TAMs and physiological Macrophages or Neutrophils combining flow cytometry and T-cell suppression assays. There are differences in the composition of subpopulations at the systemic level and those that infiltrate the tumor micro-environment, and a method for accurate phenotyping of MDSCs on tissue sections by ImmunoHistoChemistry (IHC) still need to be defined, but has been hampered by the limited number of antibodies that could be associated on a single slide. Using multiplex IHC and multispectral imaging, we have designed an approach to phenotype each myeloid cell population in the tumor micro-environment, in different solid tumor types. Antibodies to cell surface markers, cytoplasmic proteins contributing to the immune-regulatory activity and transcription factors, have first been validated as simplex IHC markers, tested as parts of multiplex panels, and evaluated in various combinations, to define a phenotyping strategy. A new Image Analysis Software has been designed to assess the co-expression of each of the markers for individual cells even for stains that do not strictly colocalize at the pixel level (e.g. nuclear and membrane proteins). Using a successive gating strategy, cells displaying MDSCs and M1/M2-polarized macrophages phenotypes have been numbered in sections from various tumor types. Our method is compatible with standard automated IHC platforms and an automated multispectral scanner, and provides a solution for the analysis of MDSCs in FFPE samples in clinical trials. Citation Format: Renaud BURRER, Océane ARIBO, Assil BENCHAABEN, Maroua TLIBA, Domenico LAZZARO, Jean-Philippe COTON. Identification of myeloid-derived suppressor cells in solid tumors by multiplex IHC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-352.

Classification of Airborne Multispectral Lidar Point Clouds for Land Cover Mapping

Airborne light detection and ranging (lidar) data are widely used for high-resolution land cover mapping. The lidar elevation data are typically used as complementary information to passive multispectral or hyperspectral imagery to enable higher land cover classification accuracy. In this paper, we examine the capabilities of a recently developed multispectral airborne laser scanner, manufactured by Teledyne Optech, for direct classification of multispectral point clouds into ten land cover classes including grass, trees, two classes of soil, four classes of pavement, and two classes of buildings. The scanner, Titan MW, collects point clouds at three different laser wavelengths simultaneously, opening the door to new possibilities in land cover classification using only lidar data. We show that the recorded intensities of laser returns together with spatial metrics calculated from the three-dimensional (3D) locations of laser returns are sufficient for classifying the point cloud into ten distinct land cover classes. Our classification methods achieved an overall accuracy of 94.7% with a kappa coefficient of 0.94 using the support vector machine (SVM) method to classify single-return points and an overall accuracy of 79.7% and kappa coefficient of 0.77 using a rule-based classifier on multireturn points. A land cover map is then generated from the classified point cloud. We show that our results outperform the common approach of rasterizing the point cloud prior to classification by ∼4% in overall accuracy, 0.04 in kappa coefficient, and by up to 16% in commission and omission errors. This improvement however comes at the price of increased complexity and computational burden.

Spectral Imaging and Archival Data in Analysing Madonna of the Rabbit Paintings by Manet and Titian.

A concise insight into the outputs provided by the latest prototype of visible-near infrared (Vis-NIR) multispectral scanner (National Research Council-National Institute of Optics, CNR-INO, Italy) is presented. The analytical data acquired on an oil painting Madonna of the Rabbit by É. Manet are described. In this work, the Vis-NIR was complemented with X-ray fluorescence (XRF) mapping for the chemical and spatial characterization of several pigments. The spatially registered Vis-NIR data facilitated their processing by spectral correlation mapping (SCM) and artificial neural network (ANN) algorithm, respectively, for pigment mapping and improved visibility of pentimenti and of underdrawing style. The data provided several key elements for the comparison with a homonymous original work by Titian studied within the ARCHive LABoratory (ARCHLAB) transnational access project.

Spectral Imaging and Archival Data in Analysing Madonna of the Rabbit Paintings by Manet and Titian

AbstractA concise insight into the outputs provided by the latest prototype of visible‐near infrared (Vis‐NIR) multispectral scanner (National Research Council‐National Institute of Optics, CNR‐INO, Italy) is presented. The analytical data acquired on an oil painting Madonna of the Rabbit by É. Manet are described. In this work, the Vis‐NIR was complemented with X‐ray fluorescence (XRF) mapping for the chemical and spatial characterization of several pigments. The spatially registered Vis‐NIR data facilitated their processing by spectral correlation mapping (SCM) and artificial neural network (ANN) algorithm, respectively, for pigment mapping and improved visibility of pentimenti and of underdrawing style. The data provided several key elements for the comparison with a homonymous original work by Titian studied within the ARCHive LABoratory (ARCHLAB) transnational access project.

Predicting Selected Forest Stand Characteristics with Multispectral ALS Data

In this study, the potential of multispectral airborne laser scanner (ALS) data to model and predict some forest characteristics was explored. Four complementary characteristics were considered, namely, aboveground biomass per hectare, Gini coefficient of the diameters at breast height, Shannon diversity index of the tree species, and the number of trees per hectare. Multispectral ALS data were acquired with an Optech Titan sensor, which consists of three scanners, called channels, working in three wavelengths (532 nm, 1064 nm, and 1550 nm). Standard ALS data acquired with a Leica ALS70 system were used as a reference. The study area is located in Southern Norway, in a forest composed of Scots pine, Norway spruce, and broadleaf species. ALS metrics were extracted for each plot from both elevation and intensity values of the ALS points acquired with both sensors, and for all three channels of the ALS multispectral sensor. Regression models were constructed using different combinations of metrics. The results showed that all four characteristics can be accurately predicted with both sensors (the best R2 being greater than 0.8), but the models based on the multispectral ALS data provide more accurate results. There were differences regarding the contribution of the three channels of the multispectral ALS. The models based on the data of the 532 nm channel seemed to be the least accurate.

Shifts in Forest Structure in Northwest Montana from 1972 to 2015 Using the Landsat Archive from Multispectral Scanner to Operational Land Imager

There is a pressing need to map changes in forest structure from the earliest time period possible given forest management policies and accelerated disturbances from climate change. The availability of Landsat data from over four decades helps researchers study an ecologically meaningful length of time. Forest structure is most often mapped utilizing lidar data, however these data are prohibitively expensive and cover a narrow temporal window relative to the Landsat archive. Here we describe a technique to use the entire length of the Landsat archive from Multispectral Scanner to Operational Land Imager (M2O) to produce three novel outcomes: (1) we used the M2O dataset and standard change vector analysis methods to classify annual forest structure in northwestern Montana from 1972 to 2015, (2) we improved the accuracy of each yearly forest structure classification by applying temporal continuity rules to the whole time series, with final accuracies ranging from 97% to 68% respectively for two and six-category classifications, and (3) we demonstrated the importance of pre-1984 Landsat data for long-term change studies. As the Landsat program continues to acquire Earth imagery into the foreseeable future, time series analyses that aid in classifying forest structure accurately will be key to the success of any land management changes in the future.

GIS-Based Analysis and Modeling of Coastline Erosion and Accretion along the Coast of Sindh Pakistan

To investigate coastline changes, more research remains to be done on the development and use of innovative and functional geospatial techniques and procedures. This research focused on the integration of remote sensing, Geographic Information Systems (GIS) and modelling techniques to provide meaningful interrelationship on the spatial and temporal dynamics of coastline changes along Keti-Bunder and Kharo-Chann in Indus Delta. Data were extracted from Multi Spectral Scanner (MSS), Thematic Mapper (TM) and (Advanced Space borne Thermal Emission and Reflection Radiometer) ASTER images having the duration of thirty eight years starting from 1973 to 2011. Object based image analysis technique was used to extract the water on the criterion of (Normalized Difference Water Index) NDWI, (Modified Normalized Difference Water Index) MNDWI and threshold level slicing. Topographic maps were also used to extract the topographic information. Digital Shoreline Analysis System (DSAS) was used to analyse and predict rates of coastline change. The correlation between the calculated values of shoreline using linear regression equation 2011 and the values which was extracted from the remote sensing data of the same year having the average percentage accuracy is almost 92%. It means linear regression can be used to model the coastal erosion and accretion. The linear regression results highlighted temporal changes which are likely to occur along the coastline. Results showed that erosion rate of KetiBunder and Kharo-Chann is 16.54 and 63.79 meter per year along the coastline respectively. The areas which can be eroded and accreted in year 2020 will be almost 28.6 square kilometres and 3.6 square kilometres respectively.

Haze removal for new generation optical sensors

ABSTRACTHaze and cloud contamination is a common problem in optical remote-sensing imagery, as it can lead to the inaccurate estimation of physical properties of the surface derived from remote sensing and reduced accuracy of land cover classification and change detection. Haze optimized transform (HOT) is a methodology applicable to radiometric compensation of additive haze effects in visible bands that exhibits a spatially complex distribution over an image. The generic approach of HOT allows for the use of older satellite imaging sensors that include at least two visible bands (e.g. Landsat Thematic Mapper (TM) and Landsat Multispectral Scanner (MSS) sensors). This study proposes modifications to extend HOT applicability to new sensors. The improvements and extended functionality adapt the method to the higher radiometric resolution specifications of newer generation sensors and use percentile-based minimum in the correction procedure to avoid causing fake minimum. Alternative filters are also evaluated to smooth raw HOT output and the cloud mask is generated as an additional output. A Landsat 8 scene of Los Angeles is used to demonstrate the improved methodology. The methodology is applicable to sensors such as QuickBird, Worldview 2/3. More than 20 additional scenes were used to evaluate the effectiveness of the methodology.