Multispectral and elevation data (LiDAR) for habitat mapping using pixel and object-based classifications: Odiel saltmarshes (Huelva)

Los ecosistemas de marismas ocupan una estrecha franja del litoral, que es controlada principalmente por la posición del nivel del mar y el rango mareal. Sin embargo, la naturaleza de otros factores que actúan a otras escalas espacio-temporales (meso-escala), como la dinámica de la línea de costa (la cual afecta a procesos de erosión y sedimentación) y las modificaciones antrópicas de la franja litoral, potencian que la naturaleza y la extensión de los impactos sobre estos ecosistemas y su consecuente respuesta sea globalmente variable y localmente compleja. De ese modo, para la gestión de estos sistemas es esencial el entendimiento de factores de control a nivel local, así como el seguimiento y cartografía de los diferentes hábitats de marismas para analizar la respuesta del sistema y los cambios en el mismo. Sin embargo, la poca diferencia espectral entre diferentes especies vegetales de marismas mareales junto con la pequeña escala espacial a la que se observan patrones espaciales de vegetación hace la identificación de diferentes clases de vegetación una tarea bastante complicada. El uso de datos espectrales y altimétricos de alta resolución (Light Detection and Ranging - LiDAR) procedentes de un vuelo fotogramétrico combinado (2013) han mejorado el reconocimiento de especies predominantes para su clasificación. Sin embargo, la selección de la técnica de clasificación también influye considerablemente en los resultados. Este trabajo se basa en el estudio comparativo de dos técnicas semiautomáticas de clasificación de imágenes (basada en píxeles y en objetos) para reconocer patrones espaciales de cambio dentro de la marisma mediante la generación de un mapa de hábitat. El presente estudio se ha aplicado al Paraje Natural de Marismas del Odiel (Suroeste de España), donde se han recopilado datos en campo para la validación de los resultados obtenidos.Coastal ecosystems are considered to be sensitive to changes in environmental forcing, particularly sea level rise. Saltmarsh ecosystems occupy a discrete lateral and vertical position that is fundamentally controlled by the position of sea level, but the nature of other mesoscale factors such as shoreline dynamic (which affects erosion and sedimentation processes) and anthropogenic modifications to the coastal zone ensure that the nature and extent of impacts and response are globally variable, and locally complex. Thus, the understanding of these control factors at local scales as well as the monitoring and mapping of the system changes is essential for management purposes. However, accurately mapping detailed features within the marsh land from remotely sensed is a challenge due to the low spectral contrast between plant species and the small scale of vegetation patterns. The use of elevation and spectral data (here gathered in a combined LiDAR and photogrammetric flight in 2013) has improved saltmarsh vegetation recognition through remote sense techniques. However, the classification technique selection is also an important factor that influences the final results. This work is based on the comparative study of two semiautomatic classification techniques (object-based and pixel based) for saltmarsh habitat mapping. To test the methods over saltmarsh environments, the two classification techniques have been applied to the Odiel saltmarshes (protected area in SW Spain), where field data were collected for the accuracy assessment

COMPARISON OF VERY NEAR INFRARED (VNIR) WAVELENGTH FROM EO-1 HYPERION AND WORLDVIEW 2 IMAGES FOR SALTMARSH CLASSIFICATION

Saltmarsh is one of the important communities of wetlands. Due to a range of pressures, it has been declared as an EEC (Ecological Endangered Community) in Australia. In order to correctly identify different saltmarsh species, development of distinct spectral characteristics is essential to monitor this EEC. This research was conducted to classify saltmarsh species based on spectral characteristics in the VNIR wavelength of Hyperion Hyperspectral and Worldview 2 multispectral remote sensing data. Signal Noise Ratio (SNR) and Principal Component Analysis (PCA) were applied in Hyperion data to test data quality and to reduce data dimensionality respectively. FLAASH atmospheric correction was done to get surface reflectance data. Based on spectral and spatial information a supervised classification followed by Mapping Accuracy (%) was used to assess the classification result. SNR of Hyperion data was varied according to season and wavelength and it was higher for all land cover in VNIR wavelength. There was a significant difference between radiance and reflectance spectra. It was found that atmospheric correction improves the spectral information. Based on the PCA of 56 VNIR band of Hyperion, it was possible to segregate 16 bands that contain 99.83 % variability. Based on reference 16 bands were compared with 8 bands of Worldview 2 for classification accuracy. Overall Accuracy (OA) % for Worldview 2 was increased from 72 to 79 while for Hyperion, it was increased from 70.47 to 71.66 when bands were added orderly. Considering the significance test with z values and kappa statistics at 95% confidence level, Worldview 2 classification accuracy was higher than Hyperion data

Improving the potential of pixel-based supervised classification in the absence of quality ground truth data

The accuracy of classified results is often measured in comparison with reference or “ground truth” information. However, in inaccessible or remote natural areas, sufficient ground truth data may not be cost-effectively acquirable. In such cases investigative measures towards the optimisation of the classification process may be required. The goal of this paper was to describe the impact of various parameters when applying a supervised Maximum Likelihood Classifier (MLC) to SPOT 5 image analysis in a remote savanna biome. Pair separation indicators and probability thresholds were used to analyse the effect of training area size and heterogeneity as well as band combinations and the use of vegetation indices. It was found that adding probability thresholds to the classification may provide a measure of suitability regarding training area characteristics and band combinations. The analysis illustrated that finding a balance between training area size and heterogeneity may be fundamental to achieving an optimum classified result.Furthermore, results indicated that the addition of vegetation index values introduced as additional image bands could potentially improve classified products and that threshold outcomes could be used to illustrate confidence levels when mapping classified results

Maximizing the relationship of yield to site-specific management zones with object-oriented segmentation of hyperspectral images

Quick and low cost delineation of site-specific management zones (SSMZ) would improve applications of precision agriculture. In this study, a new method for delineating SSMZ using object-oriented segmentation of airborne imagery was demonstrated. Three remote sensing domains—spectral, spatial, and temporal- are exploited to improve the SSMZ relationship to yield. Common vegetation indices (VI), and first and second derivatives ([Formula: see text], [Formula: see text]) from twelve airborne hyperspectral images of a cotton field for one season [Formula: see text] were used as input layers for object-oriented segmentation. The optimal combination of VI, SSMZ size and crop phenological stage were used as input variables for SSMZ delineation, determined by maximizing the correlation to segmented yield monitor maps. Combining narrow band vegetation indices and object-oriented segmentation provided higher correlation between VI and yield at SSMZ scale than that at pixel scale by reducing multi-resource data noise. VI performance varied during the cotton growing season, providing better SSMZ delineation at the beginning and middle of the season (days after planting (DAP) 66–143).The optimal scale determined for SSMZ delineation was approximately 240 polygons for the study field, but the method also provided flexibility enabling the setting of practical scales for a given field. For a defined scale, the optimal single phenological stage for the study field was near July 11 (DAP 87) early in the growing season. SSMZs determined from multispectral VIs at a single stage were also satisfactory; compared to hyperspectral indices, temporal resolution of multi-spectral data seems more important for SSMZ delineation

A comparison of multispectral aerial and satellite imagery for mapping intertidal seaweed communities

Habitat‐forming seaweeds are vital components of marine ecosystems, supporting immense diversity and providing ecosystem services. Reports of major changes in the distribution and abundance of large brown seaweeds in the north‐east Atlantic are an increasing cause for concern, but a lack of consistent monitoring over time is a key impediment in obtaining reliable evidence of change. There is an urgent need to recognize change rapidly and efficiently in marine communities, which are increasingly affected by pressures of human population growth, climate change, and ocean acidification. Here, the potential for remote monitoring of seaweed habitats is investigated using freely available, high‐resolution aerial and satellite imagery. Three sources of imagery were used: (i) Channel Coastal Observatory (CCO) aerial imagery; (ii) aerial images from the Bing webmap server; and (iii) RapidEye multispectral satellite data. The study area, the Thanet Coast, is an area of chalk outcrop in south‐east England of high conservation status, and includes three Marine Conservation Zones. Eight habitat classes, including brown, red, and green algal zones, were recognized based on ground‐truthing surveys. A multi‐class classification model was developed to predict habitat classes based on the chromatic signature derived from the aerial images. The model based on the high‐resolution CCO imagery gave the best outcome (with a kappa value of 0.89). Comparing predictions for images in 2001 and 2013 revealed habitat changes, but it is unclear as to what extent these are natural variability or real trends. This study demonstrates the potential value for long‐term monitoring with remote‐sensing data. Repeated, standardized coastal aerial imaging surveys, such as those performed by CCO, permit the rapid assessment and re‐assessment of habitat extent and change. This is of value to the conservation management of protected areas, particularly those defined by the presence or extent of specific habitats

Object-Based Classification of Vegetation at Stordalen Mire near Abisko by using High-Resolution Aerial Imagery

The focus of this work is to investigate and apply the remote sensing method of object-based image analysis (OBIA) for vegetation classification of a permafrost underlain peatland in sub-arctic Sweden, by using aerial imagery of high resolution. Since the northern landscapes are an important source of naturally stored CH4 and CO2, their contribution to the global carbon cycle is a focus in research about climate change and the global methane exchange. Climate change affects permafrost soils by future increases in the mean temperature and precipitation. It further influences the depth of the frozen layer, and the thickness of the active layer above permafrost increases. This complex relationship results into a changing future landscape distribution of the vegetation at permafrost peatlands. The change has an effect on the exchange of CH4 in particular permafrost areas. For that reason, knowledge about the vegetation distribution of plant communities is interesting for ecological studies. In this work, the observed area is Stordalen mire, which is situated in Swedish Lapland. At this peatland, a landscape change is currently visible as it occurs as variations in the vegetation pattern above the permafrost and by an obvious permafrost thaw. A number of studies focus on the mire and the place has a long history of research in climate change. So far, there is no detailed vegetation map of Stordalen available, indicating the relative spatial distribution of vegetation. Therefore, the main aim is to use a suitable technique to derive a detailed vegetation map by supervised classification. To carry out the information needed, digital aerial photography of high spatial resolution was used. The extraction of thematic information from that data was done by a combination of OBIA methods. Remote sensing has the capability to explore distant regions, and the usage of digital aerial imagery of high resolution allowed to captures the small size of structures of vegetation. It was possible to identify single plant communities from the data, and this information was taken out as vegetation classes. The presented work includes the preprocessing of the data, the segmentation of image objects, establishment of classification controls, set up of training areas, and the image classification to the vegetation map, finally with an evaluation of the results. The resulted map is a contribution to apply OBIA as a method for landscape analysis in future research about northern peatlands. Key words: Physical Geography and Ecosystem Analysis, Object-Based Image Analysis, OBIA, Vegetation Classification, Permafrost, Arctic Peatland, Remote Sensing, Aerial Photography, Environmental Monitoring, Landscape Analysis Advisor: Andreas Persson Master degree project 30 credits in Geomatics, 2014 Department of Physical Geography and Ecosystems Science, Lund University Student thesis series INES nr 323Northern peatlands play an important role in the observation of global climate variations. Permafrost in the ground is melting slowly, because the earth becomes warmer. This tends to result in increasing methane gas emissions from the ground. The impact of degradation is visible in a changing land cover distribution at the peatlands, because the landscape faces a change into moister conditions. Understanding the relation to climate conditions of the future is a challenge. No accurate information about the particular vegetation of peatlands exists. More knowledge about the vegetation cover would help to understand the effects from loosing permafrost in peatlands. It is also a useful source for a long- term observation of landscape changes. In our work, the objective was to derive a map that shows a detailed vegetation distribution of a permafrost underlain area. This was done by a classification of an aerial photography. Because Swedish Lapland is far from populated places, remote sensing is a welcome method to observe the ground vegetation from above. This is commonly done by interpreting photos taken by an aircraft. Our method involves an approach of object- based image analysis. We classify the information from an aircraft-taken image in a meaningful context into groups of vegetation covering the land surface. We produce a detailed vegetation map that helps to explore the vegetation and to observe changes. Background Remains from the last ice age characterize the subarctic landscape of Swedish Lapland. Large areas are still underlain by a constantly frozen ground, permafrost. The regions of permafrost exhibit wetlands that are often peatlands. We know that the permafrost ground layer is sensitive to changes in local climates. An obvious effect, the trend to warmer temperatures and to more rainfall, is that the landscape changes slowly by that. Landscape transformation is highly visible by ongoing ground degradation. This causes an increase in the disturbance of wetlands. The plant cover is affected, as vegetation pattern is connected to the conditions in the ground. In subarctic regions, the plant species distribution covering a peatland is defined by water access. The production of methane, a greenhouse gas, is also connected to water accessibility. Water covered permafrost drives the release of methane to the atmosphere. A better knowledge about ongoing processes within the peatland ecosystems and permafrost disappearance is important. Study Site and Data Northernmost Sweden, Lapland, is a region where peatlands are commonly found. We use an aerial photography that shows the area of a peatland that is underlain by permafrost. The digital image was taken during the growing season. Our study site, Stordalen, is a peatland close to the Abisko Scientific Research Station near lake Torneträsk. Stordalen is a peatland which has a frozen permafrost ground at different stages of degradation. Permafrost is already melting in some places, which suits the area for a closer observation. The used image has a high pixel resolution of 8 cm, which is in higher resolution than other available data of the area. Such high resolution enables to identify a high level of details of the vegetation cover. Image Classification A classification based on aerial imagery is a common technique. Traditional methods are based on a classification of every single pixel the image contains, and hereby considering the spectral information only. But this approach may lead to some problems, as one plant growing naturally consists of more than one pixel in a high- resolution image, as ours. The imagination that most plants covering the ground, e.g. patches of mosses, are naturally larger than 8 cm helps to understand this. We used the approach of an object-based interpretation, which enables a deeper influence on the classification, on the other hand. By the help of parameters and rule setting, object-based allows to capture typical pixel values that form a plant. Our method of object creation enables to include structures of different and very small sizes, so the plant cover is described following the natural shape. Main plant species are collected in vegetation groups that describe the type of plants by their growth form. Such a group is based on common characteristics. This classification method proposes the comparability of the vegetation classes. Using objects also enables new possibilities to analyze the vegetation. Results and Conclusions A map showing the distribution of vegetation at Stordalen peatland was obtained from the image. The work included tests of different parameters, the adjustment of the object size, shape and location. Typically, a vegetation object was identified by the spectral response of the plant surface in the image. A visual inspection, based on field knowledge, helped to determine the best parameters. The evaluation was done by comparing information from a field survey to the classification results. It was possible to produce a map that contains vegetation classes that follow the natural growth. Therefore, the combination of an object-based image analysis with high resolution data is of use to map the vegetation of a peatland. More knowledge about the actual vegetation, to evaluate the classification result, could improve future map results. Key words: Physical Geography and Ecosystem Analysis, Object-Based Image Analysis, OBIA, Vegetation Classification, Permafrost, Arctic Peatland, Remote Sensing, Aerial Photography, Environmental Monitoring, Landscape Analysis Original title: Marco Giljum (2014) Object-Based Classification of Vegetation at Stordalen Mire near Abisko by using High-Resolution Aerial Imagery Advisor: Andreas Persson Master degree project 30 credits in Geomatics, 2014 Department of Physical Geography and Ecosystems Science, Lund University Student thesis series INES nr 32

Improving the potential of pixel-based supervised classification in the absence of quality ground truth data

The accuracy of classified results is often measured in comparison with reference or “ground truth” information. However, in inaccessible or remote natural areas, sufficient ground truth data may not be cost-effectively acquirable. In such cases investigative measures towards the optimisation of the classification process may be required. The goal of this paper was to describe the impact of various parameters when applying a supervised Maximum Likelihood Classifier (MLC) to SPOT 5 image analysis in a remote savanna biome. Pair separation indicators and probability thresholds were used to analyse the effect of training area size and heterogeneity as well as band combinations and the use of vegetation indices. It was found that adding probability thresholds to the classification may provide a measure of suitability regarding training area characteristics and band combinations. The analysis illustrated that finding a balance between training area size and heterogeneity may be fundamental to achieving an optimum classified result. Furthermore, results indicated that the addition of vegetation index values introduced as additional image bands could potentially improve classified products and that threshold outcomes could be used to illustrate confidence levels when mapping classified results.http://www.sajg.org.za/index.php/sajgam2016Centre for Geoinformation ScienceGeography, Geoinformatics and Meteorolog