To successfully protect and manage wetlands, efficient and accurate tools are needed to identify where wetlands are located, the wetland type, what condition they are in, what are the stressors present, and the trend in their condition. Wetland mapping and monitoring are useful to accomplish these tasks. Wetland mapping and monitoring with optical remote sensing data has mainly focused on using a single image or using image acquired over two seasons within the same year. Now that Landsat data are available freely, a multi-temporal approach utilizing images that span multiple seasons and multiple years can potentially be used to characterize wetland dynamics in more detail. In addition, newer remote sensing techniques such as Light Detection and Ranging (lidar) can provide highly detailed and accurate topographic information, which can improve our ability to discriminate wetlands. Thus, the overall objective of this study was to investigate the utility of lidar and multi-temporal Landsat data for mapping and monitoring of wetlands. My research is presented as three independent studies related to wetland mapping and monitoring. In the first study, inter-annual time series of Landsat data from 1985 to 2009 was used to map changes in wetland ecosystems in northern Virginia. Z-scores calculated on tasseled cap images were used to develop temporal profile for wetlands delineated by the National Wetland Inventory. A change threshold was derived based on the Chi-square distribution of the Z-scores. The accuracy of a change/no change map produced was 89% with a kappa value of 0.79. Assessment of the change map showed that the method used was able to detect complete wetland loss together with other subtle changes resulting from development, harvesting, thinning and farming practices. The objective of the second study was to characterize differences in spectro-temporal profile of forested upland and wetland using intra and inter annual time series of Landsat data (1999-2012). The results show that the spector-temporal metrics derived from Landsat can accurately discriminate between forested upland and wetland (accuracy of 88.5%). The objective of the third study was to investigate the ability of topographic variables derived from lidar to map wetlands. Different topographic variables were derived from a high resolution lidar digital elevation model. Random forest model was used to assess the ability of these variables in mapping wetlands and uplands area. The result shows that lidar data can discriminate between wetlands and uplands with an accuracy of 72%. In summary, because of its spatial, spectral, temporal resolution, availability and cost Landsat data will be a primary data source for mapping and monitoring wetlands. The multi-temporal approach presented in this study has great potential for significantly improving our ability to detect and monitor wetlands. In addition, synergistic use of multi-temporal analysis of Landsat data combined with lidar data may be superior to using either data alone for future wetland mapping and monitoring approaches. / Ph. D.

Příloha k disertační práci: Abstrakt v AJ (Mgr. Josef Laštovička) Abstract This dissertation thesis deals with the study of forest ecosystems in the central Europe with the time series of multispectral optical satellite data. These forest ecosystems have been influenced by biotic and abiotic disturbances for the last decade. The time series of the satellite data with high spatial resolution allow the detection and analysis of forest disturbances. This thesis is mainly focused primally on free available Landsat and Sentinel-2 data, these two data types were compared. From methods, the difference time series analyses / algorithms were used. The whole thesis can be divided into two main parts. The first one analyses usability of classifiers for detection of forest ecosystems with per-pixel and sub-pixel methods. Specifically, the Neural Network, the Support Vector Machine and the Maximum Likelihood per-pixel classifiers were used and compared for different types of data (for data with high spatial resolution - Landsat or Sentinel-2; very high spatial resolution - WorldView-2) and for classification of protected forest areas. The Support Vector Machine were selected as the most suitable method for forest classifications (with most accurate outputs) from the list of selected per-pixel classifiers. Also, Spectral...

La dégradation de la forêt est un changement de sa structure et de la composition floristique et faunistique, ce qui conduit à une perte de biodiversité, de production de biens et de services et à un accroissement de la vulnérabilité aux aléas climatiques et aux incendies. Elle concerne de vastes espaces en zone tropicale particulièrement dans les régions de fronts pionniers plus ou moins consolidés où la forêt primaire est soumise à l’extraction de bois, aux incendies et à la fragmentation. Pour évaluer son ampleur et son intensité, il est nécessaire de recourir à la télédétection. Mais les méthodologies disponibles restent encore insuffisantes.L’enjeu scientifique est de développer des méthodes adaptées à de grandes surfaces afin d’analyser l’effet de différentes perturbations sur les trajectoires suivies par le couvert forestier. Il s’agit également de distinguer différentes intensités de dégradation suite à l’accumulation de perturbations. C’est un préalable indispensable pour définir et mettre en œuvre des plans de gestion adaptés. Le premier axe de ce travail a pour objectif de cartographier annuellement l’ampleur des perturbations, d’identifier les principaux types de perturbations et de caractériser la trajectoire de restauration de l’activité photosynthétique. Il est réalisé à partir de séries temporelles d’images Landsat traitées au moyen du progiciel CLASlite. L’agrégation des couvertures annuelles résultant des traitements avec CLASlite a également permis de constituer un indicateur de dégradation résultant du cumul de processus de perturbations sur plusieurs années. / The forest degradation is a change of the structure and the composition of flora and fauna, which leads to a loss of biodiversity, of production of goods and services and an increased vulnerability to weather hazards and fires. This process concerns large areas in the tropics, particularly in agricultural frontier where primary forest is subject to timber extraction, fire and fragmentation. Remote sensing is used to assess the magnitude and the extent of forest degradation. However, the methodologies available are still insufficient. The scientific challenge is to develop methods adapted to large areas to analyze the effect of different disturbances on the trajectories followed by the forest cover. It is also to identify different intensities of degradation following disturbances events. This is a prerequisite for defining and implementing appropriate management plans. The first axis of this work aims to map annually the extent of the disturbances, to identify the main types of disturbances and to characterize the restoration trajectory of the photosynthetic activity. This work is based on time series of Landsat images processed using CLASlite software. The aggregation of the annual coverages resulting from treatments with CLASlite also made it possible to constitute an indicator of degradation resulting from the accumulation of disturbance processes over several years. The second axis aims to evaluate the evolution of the forest sensitivity to drought as a function of its degradation and to build a degradation indicator. The approach uses MODIS images and TRMM precipitation data. This work is implemented in the municipality of Paragominas (state of Pará, Brazil).