Whether a species is rare and requires protection or is overabundant and needs control, an accurate estimate of population size is essential for the development of conservation plans and management goals. Wildlife censuses in remote locations or over extensive areas are logistically difficult, frequently biased, and time consuming. My dissertation examined various techniques to determine the probability of detecting animals using remotely sensed imagery. We investigated four procedures that integrated unsupervised classification, texture characteristics, spectral enhancements, and image differencing to identify and count animals in remotely sensed imagery. The semi-automated processes had relatively high errors of over-counting (i.e., greater than 60%) in contrast to low (i.e. less than 19%) under-counting errors. The single-day image differencing had over-counting errors of 53% while the manual interpretation had over-counting errors of 19%. The probability of detection indicates the ability of a process or analyst to detect animals in an image or during an aerial wildlife survey and can adjust total counts to estimate the size of a population. The probabilities of detecting an animal in remotely sensed imagery with semi-automated techniques, single-day image differencing, or manual interpretation were high (e.g. ≥ 80%). Single-day image differencing resulted in the highest probability of detection suggesting this method could provide a new technique for managers to estimate animal populations, especially in open, grassland habitats. Remotely sensed imagery can be successfully used to identify and count animals in isolated or remote areas and improve management decisions. Sightability models, used to estimate population abundances, are derived from count data and the probability of detecting an animal during a census. Global positioning systems (GPS) radio-collared bison in the Henry Mountains of south-central Utah provided a unique opportunity to examine remotely sensed physiographic and survey characteristics for known occurrences of double-counted and missed animals. Bison status (detected, missed, or double-counted) was determined by intersecting helicopter survey paths with bison travel paths during annual helicopter surveys. The probability of detecting GPS-collared bison during the survey ranged from 91% in 2011 to 88% in 2012.

Karlstads vision är att bli en kommun med 100 000 invånare till år 2031. För att kommunen ska nå målet innebär det att fler bostäder behöver byggas. Det innebär att det kommer bli markanvändningsförändringar inom Karlstadskommun. Den urbana miljön bör påverkats när antalet invånare i Karlstad ökar. Fjärranalys är ett sätt att kunna inventera jordytans biofysiska egenskaper och antropogena förändringar. Fjärranalysen har även använts för att kvantifiera och kartlägga ekosystem-egenskaper. Ekosystemtjänster har positiv påverkan på människor, eftersom de hjälper till med att minska stress, ångest, och har positiv inverkan på återhämtning. Medvetenheten av ekosystemtjänster har ökat med åren och det medför att besluttagares och allmänheten är mer medvetna på värdet av de varor och tjänster som ekosystemtjänsterna bidrar till. Att uttrycka värdena av ekosystemtjänster i pengarvärde är ett viktigt verktyg för att ytterligare öka medvetenheten och betydelsen av ekosystem och mångfald till beslutsfattare. Med hjälp av fjärranalys går det att upptäcka att det har skett en del marktäckes- och markanvändningsförändringar runt om i Karlstads tätort. Tydliga förändringar upptäcktes runt Välsviken och vid Bergviks köpcenter, har det uppkommit nya byggnader. Vid den gamla flygplatsen har ett nytt bostadskvarter byggts samt vid områdena Stockfallet och Campus har expanderats med fler bostäder och andra byggnader.Studien har visat att nybyggnationer sker på bekostnad av främst skog. Detta medför att en del av de naturliga ekosystemtjänsterna försvinner. Totalt har 1 016 ha skog och 154 ha vatten försvunnit, det har tillkommit 196 ha öppen mark och bebyggelse har ökat med 975 ha i Karlstads tätort.Ett ekosystemvärde på 58 986 Int$/ha/år för skog har försvunnit från Karlstads tätortsområde, det motsvarar en förminskning på 30 % på 13 år. Medans öppen mark har ett ekosystemvärde som motsvarar 6 272 Int$/ha/år som har tillkommit i området. Det är en ökning på 0,7 %. Vattnet har ett värde på 2 310 Int$/ha/år, som motsvarar en förminskning på 0,6 %. Ett ekosystemtjänstvärde av totalt 61 238 Int$/ha/år har försvunnit från Karlstad. Invånare som bor i centrum kommer få längre till ekosystemtjänsterna som finns i skogen, men även mycket av de natursköna vyerna kommer försvinna från staden. Eftersom växlighet ingår i infrastrukturen kommer invånarna fortfarande ha tillgång till en del ekosystemtjänster i staden. / The municipality of Karlstad has a vision to reach 100,000 inhabitants by year 2031, which leads to that more housing is needed. In conclusion, more housing leads to land cover changes in the municipality. Remote sensing is one way to discoverer soil biophysical properties and anthropogenic changes. It has even been used to quantify and map ecosystem properties. Ecosystem services have a positive effect on people, because they help to reduce stress, depression and have a good impact on recovery. The awareness of ecosystem services has increased, it means that decision makers and the public are now more aware of the significant value of ecosystem services. Through remote sensing, land use and land cover changes can be observed in Karlstad’s urban area. As prominent changes, in the areas around Välsviken and Bergviks shopping mall, new buildings were created. At the old airport, a new residential area was constructed. Further changes could be observed in areas around Stockfallet and Campus where more residential buildings were constructed. When forests are converted to new residential areas, some natural ecosystem services disappear. The municipality of Karlstad has experienced losses that amount to a total of 1017 ha forest and 154 ha water in 13 years. It has an ecosystem service value of 61 238 Int$/ha/year and that has disappeared from Karlstad. It corresponds a loss of 30 percent of the ecosystem services. While open fields have increased with 6272 ha and has a value of 6272 Int$/ha/year. Citizens that are living in the centre is going to have a longer distance to ecosystem services in the forest. Much of the scenic views will disappeared from Karlstad. Because vegetation is included in infrastructures will residents still have access to ecosystem services in the city.

Den 8:e januari 2005 inträffade en av de mest förödande stormarna i Sveriges historia då hundratusentals blev strömlösa och sju personer miste livet. Stormen Gudrun drabbade centrala Götaland värst och uppemot nio årsavverkningar skog beräknas ha fällts i vissa områden. Tidigare studier av stormen har genomförts på uppdrag av Skogsstyrelsen där resultaten visar att andel stormfälld skogsmarksareal var 11 % i värst drabbade Ljungby kommun, och ca 80 % av all den stormfällda skogen var gran, 18 % var tall och 2 % var löv. Syftet med arbetet är att undersöka mängden stormfälld skog efter stormen Gudrun genom analys av satellitburen fjärranalysdata. Även andelen stormfälld barr- och lövskog beräknades och resultaten jämfördes med de rapporter skrivna för Skogsstyrelsen. Även andelen stormfälld skog som är återbeskogad år 2016 beräknades. En förändringsanalys med satellitbilder från Landsat 5, tagna åren 2004 och 2005, genomfördes vilken inkluderade en skogsmask som skapades genom övervakad MLC-klassificering. Skogsmasken användes för att utesluta ointressanta områden i analyserna. Resultatet användes sedan för analys av andelen stormfälld barr- och lövskog samt för analys av återbeskogade områden år 2016. I den sistnämnda skapades en skogsmask med en satellitbild från Landsat 8 och som sedan användes i analysen. Resultaten från analyserna visar att ca 15,8 % av skogen stormfälldes, varav 78 % var barrskog och 13 % var lövskog. År 2016 hade ca 25 % av de stormfällda områdena återbeskogats. Noggrannheten på resultaten är generellt höga men skiljer sig trots detta väsentligt från resultaten i studierna som gjorts för Skogsstyrelsen. Anledningen till att resultaten skiljer sig åt kan bero på vilka satellitbilder och program som använts i analyserna, samt felkällor som uppkommit i samband med analyserna i denna studie. / On January 8th, 2005 one of the most devastating storms in Sweden’s history occurred, where hundreds of thousands became powerless and seven people lost their lives. The storm Gudrun hit central Götaland worst and nearly nine years’ professional felling of forests was estimated to have fallen in some areas. Previous studies of the storm were carried out on behalf of the Swedish Forest Agency, where the results show that the proportion of windthrown forest area was 11 % in the worst affected municipality of Ljungby. About 80 % of all damaged forests were spruce, 18 % were pine and 2 % were deciduous.                        The aim of this thesis is to investigate the amount of windthrown forest after the storm Gudrun through analysis of satellite remote sensing data. The proportion of windthrown coniferous and deciduous forest was calculated and the results were compared to the reports written on behalf of the Swedish Forest Agency. Furthermore, the proportion of reforested areas in 2016 was calculated. A change analysis based on satellite data from Landsat 5 from 2004 and 2005 was performed which included a forest mask created by supervised MLC classification. The forest mask was used to exclude uninteresting areas in the analyses. The result was then used for the analysis of the proportion of windthrown coniferous and deciduous forest and for the analysis of reforested areas in 2016. In the latter, a forest mask based on Landsat 8 data was used. The results from the analyses show that about 15.8 % of the forest was windthrown, of which 78 % were coniferous and 13 % were deciduous forest. By 2016, 25% of the windthrown areas had been reforested. The accuracy of the results is generally high, but despite this, it substantially differs from the results of earlier studies. The reason for this could be differences in satellite images and programs and additional error sources in conjunction with the analyses.

The objective of this research is to detect change in urban areas using two satellite images (from 2001 and 2010) covering the city of Shanghai, China. These satellite images were acquired by Landsat-7 and HJ-1B, two satellites with different sensors. Two change detection algorithms were tested: image differencing and post-classification comparison. For image differencing the difference image was classified using unsupervised k-means classification, the classes were then aggregated into change and no change by visual inspection. For post-classification comparison the images were classified using supervised maximum likelihood classification and then the difference image of the two classifications were classified into change and no change also by visual inspection. Image differencing produced result with poor overall accuracy (band 2: 24.07%, band 3: 25.96%, band 4: 46.93%), while post-classification comparison produced result with better overall accuracy (90.96%). Post-classification comparison works well with images from different sensors, but it relies heavily on the accuracy of the classification. The major downside of the methodology of both algorithms was the large amount of visual inspection.

The main purpose of thesis is creation and evaluation of models for change detection of arable land to grassland by Hybrid-based Change Detection method, which combined approaches based on the Vegetation Indices, Image Differencing and Principal Component Analysis. Six locations with different seasonal configuration of images with high resolution and one locality covered by image with very high resolution were used. The areas were spread across the foothill areas of the Czech Republic. The selection of predictors and the most suitable model was supported by statistical calculation. Application selected models were carried out using a multi-temporal object classification and their accuracy were verified using reference data. The benefit of this thesis is finding generally applicable model useful to investigate the land cover change and evaluation of the potentially most appropriate seasonal configuration of images. Valuable is also methodology in this thesis which focus on selection of predictors and calculation the order of the most appropriate models, which is unique in the available literature. The thesis provides useful findings fitting to insufficiently explored issue of Change Detection arable land to grassland. Powered by TCPDF (www.tcpdf.org)

Many image processing algorithms exist that can accurately detect humans and other objects such as vehicles and animals. Many of these algorithms require large amounts of processing often requiring hardware acceleration with powerful central processing units (CPUs), graphics processing units (GPUs), field programmable gate arrays (FPGAs), etc. Implementing an algorithm that can detect objects such as humans at longer ranges makes these hardware requirements even more strenuous as the numbers of pixels necessary to detect objects at both close ranges and long ranges is greatly increased. Comparing the performance of different low-power implementations can be used to determine a trade-off between performance and power. An image differencing algorithm is proposed along with selected low-power hardware that is capable of detected humans at ranges of 500 m. Multiple versions of the detection algorithm are implemented on the selected hardware and compared for run-time performance on a low-power system.

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There has been rapid change in the landcover/landuse in the Mekong delta, Viet Nam. The landcover/landuse has changed very fast due to intense population pressure, agriculture/aquaculture farming and timber collection in the coastal areas of the delta. The changing landuse pattern in the coastal areas of the delta is threatened to be flooded by sea level rise; sea level is expected to rise 33 cm until 2050; 45 cm until 2070 and 1 m until 2100. The coastline along the eastern Mekong delta has never been static, but the loss of mangrove forests along the coast has intensified coastline change. The objective of the present study is to map the changes in landcover/landuse along the eastern coast of the Mekong delta; and to detect the changes in position of the eastern coastline over the time period from 1989 to 2002.To detect changes in landuse, two satellite images of the same season, acquired by the TM sensor of Landsat 5 and the ETM+ sensor of Landsat 7 were used. The TM image was acquired on January 16, 1989 and ETM+ image was acquired on February 13, 2002. The landcover/landuse classes selected for the study are water, forest, open vegetation, soil and shrimp farms. Image differencing and post classification comparison are used to detect the changes between two time periods. Image to image correction technique is used to align satellite images. Maximum likelihood supervised classification technique is used to classify images. The result of the classification consists of five classes for 1989 and 2002, respectively. Overall accuracies of 87.5% and 86.8%, with kappa values of 0.85 and 0.84 are obtained for landuse 1989 and landuse 2002, respectively. The overall accuracy for the change map is 82% with kappa value 0.80. Post classification comparison is carried out in this study based on the supervised classification results. According to the results obtained from the post classification comparison, a significant decrease of 48% in forest and a significant increase of 74% in open vegetation and 21% in shrimp farms area observed over the entire study area. The coastline obtained by the combination of histogram thresholding and band ratio showed an overall advancement towards the South China Sea. The results showed that new land patches emerged along the eastern coast. The amount of new land patches appeared along the coast of the Mekong delta is approximately 2% of the entire study area.