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

Vegetation Dynamics in Ecuador

Global forest cover has suffered a dramatic reduction during recent decades, especially in tropical regions, which is mainly due to human activities caused by enhanced population pressures. Nevertheless, forest ecosystems, especially tropical forests, play an important role in the carbon cycle functioning as carbon stocks and sinks, which is why conservation strategies are of utmost importance respective to ongoing global warming. In South America the highest deforestation rates are observed in Ecuador, but an operational surveillance system for continuous forest monitoring, along with the determination of deforestation rates and the estimation of actual carbon socks is still missing. Therefore, the present investigation provides a functional tool based on remote sensing data to monitor forest stands at local, regional and national scales. To evaluate forest cover and deforestation rates at country level satellite data was used, whereas LiDAR data was utilized to accurately estimate the Above Ground Biomass (AGB; carbon stocks) at catchment level. Furthermore, to provide a cost-effective tool for continuous forest monitoring of the most vulnerable parts, an Unmanned Aerial Vehicle (UAV) was deployed and equipped with various sensors (RBG and multispectral camera). The results showed that in Ecuador total forest cover was reduced by about 24% during the last three decades. Moreover, deforestation rates have increased with the beginning of the new century, especially in the Andean Highland and the Amazon Basin, due to enhanced population pressures and the government supported oil and mining industries, besides illegal timber extractions. The AGB stock estimations at catchment level indicated that most of the carbon is stored in natural ecosystems (forest and páramo; AGB ~98%), whereas areas affected by anthropogenic land use changes (mostly pastureland) lost nearly all their storage capacities (AGB ~2%). Furthermore, the LiDAR data permitted the detection of the forest structure, and therefore the identification of the most vulnerable parts. To monitor these areas, it could be shown that UAVs are useful, particularly when equipped with an RGB camera (AGB correlation: R² > 0.9), because multispectral images suffer saturation of the spectral bands over dense natural forest stands, which results in high overestimations. In summary, the developed operational surveillance systems respective to forest cover at different spatial scales can be implemented in Ecuador to promote conservation/ restoration strategies and to reduce the high deforestation rates. This may also mitigate future greenhouse gas emissions and guarantee functional ecosystem services for local and regional populations

Quantifying Dynamics in Tropical Peat Swamp Forest Biomass with Multi- Temporal LiDAR Datasets

Tropical peat swamp forests in Indonesia store huge amounts of carbon and are responsible for enormous carbon emissions every year due to forest degradation and deforestation. These forest areas are in the focus of REDD+ (reducing emissions from deforestation, forest degradation, and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks) projects, which require an accurate monitoring of their carbon stocks or aboveground biomass (AGB). Our study objective was to evaluate multi-temporal LiDAR measurements of a tropical forested peatland area in Central Kalimantan on Borneo. Canopy height and AGB dynamics were quantified with a special focus on unaffected, selective logged and burned forests. More than 11,000 ha were surveyed with airborne LiDAR in 2007 and 2011. In a first step, the comparability of these datasets was examined and canopy height models were created. Novel AGB regression models were developed on the basis of field inventory measurements and LiDAR derived height histograms for 2007 (r(2) = 0.77, n = 79) and 2011 (r(2) = 0.81, n = 53), taking the different point densities into account. Changes in peat swamp forests were identified by analyzing multispectral imagery. Unaffected forests accumulated on average 20 t/ha AGB with a canopy height increase of 2.3 m over the four year time period. Selective logged forests experienced an average AGB loss of 55 t/ha within 30 m and 42 t/ha within 50 m of detected logging trails, although the mean canopy height increased by 0.5 m and 1.0 m, respectively. Burned forests lost 92% of the initial biomass. These results demonstrate the great potential of repetitive airborne LiDAR surveys to precisely quantify even small scale AGB and canopy height dynamics in remote tropical forests, thereby featuring the needs of REDD+

Monitoring soil erosion in the Souss basin, Morocco, with a multiscale object-based remote sensing approach using UAV and satellite data

This article presents a multiscale approach for detecting and monitoring soil erosion phenomena (i.e. gully erosion) in the agro-industrial area around the city of Taroudannt, Souss basin, Morocco. The study area is characterized as semi-arid with an annual average precipitation of 200 mm. Water scarcity, high population dynamics and changing land use towards huge areas of irrigation farming present numerous threats to sustainability. The agro-industry produces citrus fruits and vegetables in monocropping, mainly for the European market. Badland areas strongly affected by gully erosion border the agricultural areas as well as residential areas. To counteract the significant loss of land, land-leveling measures are attempted to create space for plantations and greenhouses. In order to develop sustainable approaches to limit gully growth the detection and monitoring of gully systems is fundamental. Specific gully sites are monitored with unmanned aerial vehicle (UAV) taking small-format aerial photographs (SFAP). This enables extremely high-resolution analysis (SFAP resolution: 2-10 cm) of the actual size of the gully channels as well as a detailed continued surveillance of their growth. Transferring the methodology on a larger scale using Quickbird satellite data (resolution: 60 cm) leads to the possibility of a large-scale analysis of the whole area around the city of Taroudannt (Area extent: ca. 350 km²). The results will then reveal possible relationships of gully growth and agro-industrial management and may even illustrate further interdependencies. The main objective is the identification of areas with high gully-erosion risk due to non-sustainable land use and the development of mitigation strategies for the study area

Unmanned Aerial Vehicle – Efficient mapping tool available for recent research in polar regions

Unmanned Aerial Vehicles (UAV) have technical capabilities to extended usage in various fields ofscience. The existing UAVs are to be relatively easily accessible in the near future. It is possible to equip them with different sensors but there are still some usage limitations. This paper focuses ondemonstrating UAVs usage for research in polar regions. The research in polar regions is very specific and, due to harsh climate, limits the field work with UAVs. The options and limitations are presented in a case study performed in the Nordenskiöldbreen area, Svalbard Archipelago. In the end some derived products suitable for further analysis are presented

LIDAR DERIVED SALT MARSH TOPOGRAPHY AND BIOMASS: DEFINING ACCURACY AND SPATIAL PATTERNS OF UNCERTAINTY

As valuable and vulnerable blue carbon ecosystems, salt marshes require adaptable and robust monitoring methods that span a range of spatiotemporal scales. The application of unmanned aerial vehicle (UAV) based remote sensing is a key tool in achieving this goal. Due to the particular characteristics of tidal wetlands, however, there are challenges in obtaining research and management relevant data with the requisite level of accuracy. In this study, the spatial patterns in uncertainty stemming from scan angle, binning method, vegetation structure and platform surface morphology are examined in the context of UAV light detection and ranging (LiDAR) derived digital elevation models (DEM). The results demonstrate that overlapping the UAV flight paths sufficiently to avoid sole reliance on LIDAR data with scan angles exceeding 15 degrees is advisable. Furthermore, the spatial arrangement of halophyte species and marsh morphology has a clear influence on DEM accuracy. The largest errors were associated with sudden structural transitions at the marsh channel boundaries. The DEMmean was found to be the most accurate for bare ground, while the DEMmin was the most accurate for channels and the middle to high marsh vegetation (MAEs = −0.01m). For the low to middle vegetation, all the trialled DEMs returned a similar magnitude of mean error (MAE = ± 0.03m). The accuracy difference between the two vegetation associations examined appears to be connected to variations in coverage, height and biomass. Overall, these findings reinforce the link between salt marsh biogeomorphic complexity and the spatial distribution and magnitude of LiDAR DEM erro

Extraction of Impervious Surface Areas from High Spatial Resolution Imagery by Multiple Agent Segmentation and Classification

In recent years impervious surface areas (ISA) have emerged as a key paradigm to explain and predict ecosystem health in relationship to watershed development. The ISA data are essential for environmental monitoring and management in coastal State of Rhode Island. However, there is lack of information on high spatial resolution ISA. In this study, we developed an algorithm of multiple agent segmentation and classification (MASC) that includes submodels of segmentation, shadow-effect, MANOVA-based classification, and post-classification. The segmentation sub-model replaced the spectral difference with heterogeneity change for regions merging. Shape information was introduced to enhance the performance of ISA extraction. The shadow-effect sub-model used a split-and-merge process to separate shadows and the objects that cause the shadows. The MANOVA-based classification sub-model took into account the relationship between spectral bands and the variability in the training objects and the objects to be classified. Existing GIS data were used in the classification and post-classification process. The MASC successfully extracted ISA from high spatial resolution airborne true-color digital orthophoto and space-borne QuickBird-2 imagery in the testing areas, and then was extended for extraction of high spatial resolution ISA in the State of Rhode Island

Monitoring spatial sustainable development: Semi-automated analysis of satellite and aerial images for energy transition and sustainability indicators

Solar panels are installed by a large and growing number of households due to the convenience of having cheap and renewable energy to power house appliances. In contrast to other energy sources solar installations are distributed very decentralized and spread over hundred-thousands of locations. On a global level more than 25% of solar photovoltaic (PV) installations were decentralized. The effect of the quick energy transition from a carbon based economy to a green economy is though still very difficult to quantify. As a matter of fact the quick adoption of solar panels by households is difficult to track, with local registries that miss a large number of the newly built solar panels. This makes the task of assessing the impact of renewable energies an impossible task. Although models of the output of a region exist, they are often black box estimations. This project's aim is twofold: First automate the process to extract the location of solar panels from aerial or satellite images and second, produce a map of solar panels along with statistics on the number of solar panels. Further, this project takes place in a wider framework which investigates how official statistics can benefit from new digital data sources. At project completion, a method for detecting solar panels from aerial images via machine learning will be developed and the methodology initially developed for BE, DE and NL will be standardized for application to other EU countries. In practice, machine learning techniques are used to identify solar panels in satellite and aerial images for the province of Limburg (NL), Flanders (BE) and North Rhine-Westphalia (DE).Comment: This document provides the reader with an overview of the various datasets which will be used throughout the project. The collection of satellite and aerial images as well as auxiliary information such as the location of buildings and roofs which is required to train, test and validate the machine learning algorithm that is being develope

Analysing the vegetation of energy plants by processing UAV images

Bioenergy plants are widely used as a form of renewable energy. It is important to monitor the vegetation and accurately estimate the yield before harvest in order to maximize the profit and reduce the costs of production. The automatic tracking of plant development by traditional methods is quite difficult and labor intensive. Nowadays, the application of Unmanned Aerial Vehicles (UAV) became more and more popular in precision agriculture. Detailed, precise, three-dimensional (3D) representations of energy forestry are required as a prior condition for an accurate assessment of crop growth. Using a small UAV equipped with a multispectral camera, we collected imagery of 1051 pictures of a study area in Kompolt, Hungary, then the Pix4D software was used to create a 3D model of the forest canopy. Remotely sensed data was processed with the aid of Pix4Dmapper to create the orthophotos and the digital surface model. The calculated Normalized Difference Vegetation Index (NDVI) values were also calculated. The aim of this case study was to do the first step towards yield estimation, and segment the created orthophoto, based on tree species. This is required, since different type of trees have different characteristics, thus, their yield calculations may differ. However, the trees in the study area are versatile, there are also hybrids of the same species present. This paper presents the results of several segmentation algorithms, such as those that the widely used eCognition provides and other Matlab implementations of segmentation algorithms

The Use of Unmanned Aerial Vehicle for Geothermal Exploitation Monitoring: Khankala Field Example

The article is devoted to the use of unmanned aerial vehicle for geothermal waters exploitation monitoring. Development of a geothermal reservoir usually requires a system of wells, pipelines and pumping equipment and control of such a system is quite complicated. In this regard, use of unmanned aerial vehicle is relevant. Two test unmanned aerial vehicle based infrared surveys have been conducted at the Khankala field (Chechen Republic) with the Khankala geothermal plant operating at different regimes: during the first survey – with, and the second – without reinjection of used geothermal fluid. Unmanned aerial vehicle Geoscan 201 equipped with digital (Sony DSX-RX1) and thermal imaging (Thermoframe-MX-TTX) cameras was used. Besides different images of the geothermal plant obtained by the surveys, 13 thermal anomalies have been identified. Analysis of the shape and temperature facilitated determination of their different sources: fire, heating systems, etc., which was confirmed by a ground reconnaissance. Results of the study demonstrate a high potential of unmanned aerial vehicle based thermal imagery use for environmental and technological monitoring of geothermal fields under operation