Preprocessing: Geocoding of AVIRIS data using navigation, engineering, DEM, and radar tracking system data

Remotely sensed data have geometric characteristics and representation which depend on the type of the acquisition system used. To correlate such data over large regions with other real world representation tools like conventional maps or Geographic Information System (GIS) for verification purposes, or for further treatment within different data sets, a coregistration has to be performed. In addition to the geometric characteristics of the sensor there are two other dominating factors which affect the geometry: the stability of the platform and the topography. There are two basic approaches for a geometric correction on a pixel-by-pixel basis: (1) A parametric approach using the location of the airplane and inertial navigation system data to simulate the observation geometry; and (2) a non-parametric approach using tie points or ground control points. It is well known that the non-parametric approach is not reliable enough for the unstable flight conditions of airborne systems, and is not satisfying in areas with significant topography, e.g. mountains and hills. The present work describes a parametric preprocessing procedure which corrects effects of flight line and attitude variation as well as topographic influences and is described in more detail by Meyer

SPECIFIC ALPINE ENVIRONMENT LAND COVER CLASSIFICATION METHODOLOGY: GOOGLE EARTH ENGINE PROCESSING FOR SENTINEL-2 DATA

Abstract. Land Cover (LC) plays a key role in many disciplines and its classification from optical imagery is one of the prevalent applications of remote sensing. Besides years of researches and innovation on LC, the classification of some areas of the World is still challenging due to environmental and climatic constraints, such as the one of the mountainous chains. In this contribution, we propose a specific methodology for the classification of the Land Cover in mountainous areas using Sentinel 2, 1C-level imagery. The classification considers some specific high-altitude mountainous classes: clustered bare soils that are particularly prone to erosion, glaciers, and solid-rocky areas. It consists of a pixel-based multi-epochs classification using random forest algorithm performed in Google Earth Engine (GEE). The study area is located in the western Alps between Italy and France and the analyzed dataset refers to 2017–2019 imagery captured in the summertime only. The dataset was pre-processed, enriched of derivative features (radiometric, histogram-based and textural). A workflow for the reduction of the computational effort for the classification, which includes correlation and importance analysis of input features, was developed. Each image of the dataset was separately classified using random forest classification algorithm and then aggregated each other by the most frequent pixel value. The results show the high impact of textural features in the separation of the mountainous-specific classes the overall accuracy of the final classification achieves 0.945

Specific alpine environment land cover classification methodology: Google Earth Engine processing for Sentinel-2 data

Land Cover (LC) plays a key role in many disciplines and its classification from optical imagery is one of the prevalent applications of remote sensing. Besides years of researches and innovation on LC, the classification of some areas of the World is still challenging due to environmental and climatic constraints, such as the one of the mountainous chains. In this contribution, we propose a specific methodology for the classification of the Land Cover in mountainous areas using Sentinel 2, 1C-level imagery. The classification considers some specific high-altitude mountainous classes: clustered bare soils that are particularly prone to erosion, glaciers, and solid-rocky areas. It consists of a pixel-based multi-epochs classification using random forest algorithm performed in Google Earth Engine (GEE). The study area is located in the western Alps between Italy and France and the analyzed dataset refers to 2017–2019 imagery captured in the summertime only. The dataset was pre-processed, enriched of derivative features (radiometric, histogram-based and textural). A workflow for the reduction of the computational effort for the classification, which includes correlation and importance analysis of input features, was developed. Each image of the dataset was separately classified using random forest classification algorithm and then aggregated each other by the most frequent pixel value. The results show the high impact of textural features in the separation of the mountainous-specific classes the overall accuracy of the final classification achieves 0.945

Land cover change on the Seward Peninsula: the use of remote sensing to evaluate the potential influences of climate change on historical vegetation dynamics

Thesis (M.S.) University of Alaska Fairbanks, 2000Vegetation on the Seward Peninsula, Alaska, which is characterized by transitions from tundra to boreal forest, may be sensitive to the influences of climate change on disturbance and species composition. To determine the ability to detect decadal-scale structural changes in vegetation, Change Vector Analysis (CVA) techniques were evaluated for Landsat TM imagery of the Seward Peninsula. Scenes were geographically corrected to sub-pixel accuracy and then radiometrically rectified. The CVA results suggest that shrubbiness is increasing on the Seward Peninsula. The CVA detected vegetation change on more than 50% of the burned region on TM imagery for up to nine years following fire. The use of both CVA and unsupervised classification together provided a more powerful interpretation of change than either method alone. This study indicates that CVA may be a valuable tool for the detection of land-cover change in transitional regions between tundra and boreal forest.Abstract -- List of figures -- List of tables -- Acknowledgements -- Introduction -- Methods -- Results -- Radiometric rectification -- Fire disturbance -- Land cover change on the Seward Peninsula -- Potential false change -- Discussion -- CVA vs. unsupervised classification -- Fire disturbance -- Land cover change on the Seward Peninsula -- Challenges and limitations -- Improvements and future directions -- Literature cited

A semiautomatic methodology to detect fire scars in shrubs and evergreen forests with Landsat MSS time series.

This paper presents a semi-automatic methodology for fire scars mapping from a long time series of remote sensing data. Approximately, a hundred MSS images from different Landsat satellites were employed over an area of 32 100 km2 in the north-east of the Iberian Peninsula. The analysed period was from 1975 to 1993. Results are a map series of fire history and frequencies. Omission errors are 23% for burned areas greater than 200 ha while commission errors are 8% for areas greater than 50 ha. Subsequent work based on the resultant fire scars will also help in describing fire regime and in monitoring post-fire regeneration dynamics.Peer Reviewe

Multitemporal Analysis in Mediterranean Forestland with Remote Sensing

The study employs a Fourier transform analysis approach to assess the land-cover changes in a mountainous Mediterranean protected area using multi-temporal satellite images. Harmonic analysis was applied to a time series of Landsat satellite images acquired from 1984 to 2008 to extract information about land cover status with a vegetation spectral index, the Normalized Difference Vegetation Index (NDVI). Ancillary cartographic information depicting land cover classes and the enlargement of the protected area over time (i.e., maps showing the original delineation in 1995 and subsequent enlargement in 2007) were employed as additional factors to understand vegetation-cover changes. Significant differences in the NDVI and harmonic components values were observed with respect to both factors. The application of the Fourier transform was particularly successful to extract subtle information. The harmonic analysis of the NDVI time series revealed valuable information about the evolution of the landscape. The initially protected area (northern sector) seems more affected by human activities than the southern sector (enlarged area in 2007) as revealed by the analysis of the first harmonic component that was closely related with vegetation coverage. Rural abandonment is a major driver of land-cover changes in the study area

Canopy reflectance modeling of forest stand volume

xiii, 143 leaves : ill. (some col.) ; 29 cm.Three-dimensional canopy relectance models provide a physical-structural basis to satellite image analysis, representing a potentially more robust, objective and accurate approach for obtaining forest cover type and structural information with minimal ground truth data. The Geometric Optical Mutual Shadowing (GOMS) canopy relectance model was run in multiple-forward-mode (MFM) using digital multispectral IKONOS satellite imagery to estimate tree height and stand volume over 100m2 homogeneous forest plots in mountainous terrain, Kananaskis, Alberta. Height was computed within 2.7m for trembling aspen and 1.8m fr lodgepole pine, with basal area estimated within 0.05m2. Stand volume, estimated as the product of mean tree height and basal area, had an absolute mean difference from field measurements of 0.85m3/100m2 and 0.61m3/100m2 for aspen and pine, respectively

Geospatial monitoring and evaluation of UNESCO world heritage forest areas in the Tropics

This Study aimed at providing a better understanding for monitoring the status, change and threats to UNESCO world heritage areas that are present in the tropical forest. Three change detection techniques were tested using Landsat images for detecting area of changes in the region of the Rio Platano biosphere reserve, a tropical rain forest in Honduras. The change detection techniques considered were image differencing, post classification analysis using supervised classification and vegetation index differencing (NDVI differencing). Two Landsat scenes recorded on 28th January 1986 and 18th December 2002 were downloaded from USGS. Images were geometrically and radiometrically corrected and the three change detection techniques were tested. Change maps obtained from each technique were visually interpreted. In order to determine the accuracy of each change maps random points were generated using systematic sampling. For each random point, change/no change were separately evaluated by using high resolution data (Google earth data) through a confusion matrix method. Image differencing for band 2 was found to be the most accurate followed by supervised classification and NDVI. Image differencing using band 3 was found to be less accurate than supervised and NDVI differencing. Supervised classification was selected for calculating area statistics inside and outside the UNESCO protected boundary because of the advantage of indicating the nature of changes. The study revealed two important changes which are clear-cut and some changes (regrowth). Clear-cut have been found to be much higher outside than the inside the protected boundary of UNESCO world heritage forested site

Investigating the impact of Tourism on forest cover in the Annapurna conservation area through Remote Sensing and Statistical Analysis

Tourism is Nepal’s largest industry giving people in rural areas an alternative to subsistence farming. Tourism can have an impact on the forest cover of a region as trees provide firewood for cooking and heating and timber for building accommodation. In 1986 the Annapurna conservation area project was started to ensure that tourism was managed sustainably, which includes minimising the impacts on the forest cover. This study assesses the impacts of tourism on the forest cover in the Annapurna region by comparing Landsat images from 1999 and 2011. This was achieved through spectral classification of different landcover and assessing the change in forest cover in relation to increasing distances from tourism villages. A major problem with remote sensing in mountainous regions such as Nepal is shadow caused by the relief. This issue was addressed by only assessing areas which were free from shadow, which in effect meant a sample was used rather than the whole study region. The results indicate that there has been an 8 per cent reduction in overall forest extent, but this change varies by region. In the northern drier regions there has been a net increase in forest cover, while in the southern regions there has been a net reduction in forests. The influence of tourism facilities on forest is also variable. Around each of the sample tourism villages there was a general trend of decreasing removal of forest at greater distances from each village, which indicates tourism does have a negative impact on forests. However, there was an opposite trend in the northern villages that were well inside the conservation area

INVESTIGATION OF DEFORESTATION USING MULTI-SENSOR SATELLITE TIME SERIES DATA IN NORTH KOREA

Department of Urban and Environmental Engineering(Environmental Science and Engineering)North Korea is very vulnerable to natural disasters such as floods and landslides due to institutional, technological, and other various reasons. Recently, the damage has been more severe and vulnerability is also increased because of continued deforestation. However, due to political constraints, such disasters and forest degradation have not been properly monitored. Therefore, using remote sensing based satellite imagery for forest related research of North Korea is regarded as currently the only and most effective method. Especially, machine learning has been widely used in various classification studies as a useful technique for classification and analysis using satellite images. The aim of this study was to improve the accuracy of forest cover classification in the North Korea, which cannot be accessed by using random forest model. Indeed, another goal of this study was to analyze the change pattern of denuded forest land in various ways. The study area is Musan-gun, which is known to have abundant forests in North Korea, with mountainous areas accounting for more than 90%. However, the area has experienced serious environmental problems due to the recent rapid deforestation. For example, experts say that the damage caused by floods in September 2016 has become more serious because denuded forest land has increased sharply in there and such pattern appeared even in the high altitude areas. And this led the mountain could not function properly in the flood event. This study was carried out by selecting two study periods, the base year and the test year. To understand the pattern of change in the denuded forest land, the time difference between the two periods was set at about 10 years. For the base year, Landsat 5 imageries were applied, and Landsat 8 and RapidEye imageries were applied in the test year. Then the random forest machine learning was carried out using randomly extracted sample points from the study area and various input variables derived from the used satellite imageries. Finally, the land cover classification map for each period was generated through this random forest model. In addition, the distribution of forest changing area to cropland, grassland, and bare-soil were estimated to the denuded forest land. According to the study results, this method showed high accuracy in forest classification, also the method has been effective in analyzing the change detection of denuded forest land in North Korea for about 10 years.ope