The population, employment and housing units along the Gulf Coast of Mississippi have been increasing since the 1970s through the 2000s. In this study, an overall increasing trend in land cover was found in developed land area near interstates and highways along all three coastal counties. A strong positive correlation was observed in Hancock County between developed land and population and developed land and housing units. A strong negative correlation was observed between vegetation and housing units. Weak positive correlations were found in Harrison County between developed land and population, marsh and population, and marsh and housing units. A weak positive correlation was found in Jackson County between bare soil and population. Several study limitations such as unsupervised classification and misclassification are discussed to explain why a strong correlation was not found in Harrison and Jackson Counties.

Image classification based on remotely sensed data is the primary domain of automatic mapping research. With the increasing of urban development, keeping geographic database updating is imminently needed. Automatic mapping of land cover types in urban area is one of the most challenging problems in remote sensing. Traditional database updating is time consuming and costly. It has usually been performed by manual observation and visual interpretation, In order to improve the efficiency as well as the accuracy, new technique in the data collection and extraction becomes increasingly necessary. This paper studied an object-based decision tree classification based on orthophoto and lidar data, both alone and integrated. Four land cover types i.e. Forest, Water, Openland as well as Building were successfully extracted. Promising results were obtained with the 89.2% accuracy of orthophoto based classification and 88.6% accuracy of lidar data based classification. Both lidar data and orthophoto showed enough capacity to classify general land cover types alone. Meanwhile, the combination of orthophoto and lidar data demonstrated a prominent classification results with 95.2% accuracy. The results of integrated data revealed a very high agreement. Comparing the process of using orthophoto or lidar data alone, it reduced the complexity of land cover type discrimination. In addition, another classification algorithm, support vector machines (SVM) classification was preformed. Comparing to the decision tree classification, it obtained the same accuracy level as decision tree classification in orthophoto dataset (89.2%) and integration dataset (97.3%). However, the SVM results of lidar dataset was not satisfactory. Its overall accuracy only reached 77.1%. In brief, object-based land cover classification demonstrated its effectiveness in land cover map generation. It could exploit spectral and spatial features from input data efficiently and classifying image with high accuracy.

Apparent long-term declines of white-winged and surf scoter (<i>Melanitta fusca </i> and <i>M. perspicillata</i>) populations in the northern boreal forest have raised concern for these sea duck species. Reasons for population declines are not well understood but some evidence suggests that factors associated with events on the breeding grounds may be responsible. Breeding ground changes could adversely affect abiotic or biotic characteristics of upland or wetland habitats or key food sources for breeding females or ducklings, which in turn may lower productivity or recruitment. Like most boreal-nesting ducks, virtually nothing is known about wetland habitat preferences of scoters. Determining habitat features that scoters need to breed successfully, and how habitat changes in the boreal forest affect scoters, is an important step in understanding their ecology and developing conservation initiatives. Thus, my overall goal was to look for evidence of habitat selection in scoters at two spatial scales by characterizing biotic and abiotic features of areas used by scoter pairs and broods, and comparing these features with those of areas not used by scoters. Habitat characteristics and scoter use of wetlands in recently burned forest was also contrasted with unburned forest to determine whether habitat change caused by fire could affect patterns of habitat use by scoters.<p> I used remote sensing data as a tool to delineate coarse-scale patterns of habitat use by scoter pairs and broods. Results indicate that although scoters may not settle on wetlands in areas dominated by burned vegetation two years following the fire, three years after the fire I found no difference in scoter pair or brood use between wetlands in burned and unburned upland. I found that surf and white-winged scoter pairs often co-occurred on wetlands. I was unable to find any evidence to support the prediction that scoters prefer wetlands with irregular shorelines that might enhance pair isolation and offer greater protection to ducklings from severe winds and wave action. <p> Based on fine-scale wetland habitat characteristics, scoter pairs and broods used wetlands with more abundant food, a finding that is consistent with many other waterfowl studies. However, unlike some previous waterfowl studies, I did not find a consistent correlation between total phosphorus levels and amphipod abundance or wetland use by scoters. Very high total nitrogen to total phosphorus ratios in sampled wetlands lead me to speculate that wetlands in my study area may be phosphorus limited. I did not detect a difference in fine-scale features of wetlands surrounded by burned versus unburned vegetation. This study of scoters in the northern boreal forest was among the first to determine why scoters use specific wetlands or areas and not others.

Apparent long-term declines of white-winged and surf scoter (<i>Melanitta fusca </i> and <i>M. perspicillata</i>) populations in the northern boreal forest have raised concern for these sea duck species. Reasons for population declines are not well understood but some evidence suggests that factors associated with events on the breeding grounds may be responsible. Breeding ground changes could adversely affect abiotic or biotic characteristics of upland or wetland habitats or key food sources for breeding females or ducklings, which in turn may lower productivity or recruitment. Like most boreal-nesting ducks, virtually nothing is known about wetland habitat preferences of scoters. Determining habitat features that scoters need to breed successfully, and how habitat changes in the boreal forest affect scoters, is an important step in understanding their ecology and developing conservation initiatives. Thus, my overall goal was to look for evidence of habitat selection in scoters at two spatial scales by characterizing biotic and abiotic features of areas used by scoter pairs and broods, and comparing these features with those of areas not used by scoters. Habitat characteristics and scoter use of wetlands in recently burned forest was also contrasted with unburned forest to determine whether habitat change caused by fire could affect patterns of habitat use by scoters.<p> I used remote sensing data as a tool to delineate coarse-scale patterns of habitat use by scoter pairs and broods. Results indicate that although scoters may not settle on wetlands in areas dominated by burned vegetation two years following the fire, three years after the fire I found no difference in scoter pair or brood use between wetlands in burned and unburned upland. I found that surf and white-winged scoter pairs often co-occurred on wetlands. I was unable to find any evidence to support the prediction that scoters prefer wetlands with irregular shorelines that might enhance pair isolation and offer greater protection to ducklings from severe winds and wave action. <p> Based on fine-scale wetland habitat characteristics, scoter pairs and broods used wetlands with more abundant food, a finding that is consistent with many other waterfowl studies. However, unlike some previous waterfowl studies, I did not find a consistent correlation between total phosphorus levels and amphipod abundance or wetland use by scoters. Very high total nitrogen to total phosphorus ratios in sampled wetlands lead me to speculate that wetlands in my study area may be phosphorus limited. I did not detect a difference in fine-scale features of wetlands surrounded by burned versus unburned vegetation. This study of scoters in the northern boreal forest was among the first to determine why scoters use specific wetlands or areas and not others.

Thesis (MA)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: Image classification has long been used in earth observation and is driven by the need for accurate maps to develop conceptual and predictive models of Earth system processes. Synthetic aperture radar (SAR) imagery is used ever more frequently in land cover classification due to its complementary nature with optical data. There is therefore a growing need for reliable, accurate methods for using SAR and optical data together in land use and land cover classifications. However, combining data sets inevitably increases data dimensionality and these large, complex data sets are difficult to handle. It is therefore important to assess the benefits and limitations of using multi-temporal, dual-sensor data for applications such as land cover classification. This thesis undertakes this assessment through four main experiments based on combined RADARSAT-2 and SPOT-5 imagery of the southern part of Reunion Island. In Experiment 1, the use of feature selection for dimensionality reduction was considered. The rankings of important features for both single-sensor and dual-sensor data were assessed for four dates spanning a 6-month period, which coincided with both the wet and dry season. The mean textural features produced from the optical bands were consistently ranked highly across all dates. In the two later dates (29 May and 9 August 2014), the SAR features were more prevalent, showing that SAR and optical data have complementary natures. SAR data can be used to separate classes when optical imagery is insufficient. Experiment 2 compared the accuracy of six supervised and machine learning classification algorithms to determine which performed best with this complex data set. The Random Forest classification algorithm produced the highest accuracies and was therefore used in Experiments 3 and 4. Experiment 3 assessed the benefits of using combined SAR-optical imagery over single-sensor imagery for land cover classifications on four separate dates. The fused imagery produced consistently higher overall accuracies. The 29 May 2014 fused data produced the best accuracy of 69.8%. The fused classifications had more consistent results over the four dates than the single-sensor imagery, which suffered lower accuracies, especially for imagery acquired later in the season. In Experiment 4, the use of multi-temporal, dual-sensor data for classification was evaluated. Feature selection was used to reduce the data set from 638 potential training features to 50, which produced the best accuracy of 74.1% in comparison to 71.9% using all of the features. This result validated the use of multi-temporal data over single-date data for land cover classifications. It also validated the use of feature selection to successfully inform data reduction without compromising the accuracy of the final product. Multi-temporal and dual-sensor data shows potential for mapping land cover in a tropical, mountainous region that would otherwise be challenging to map using single-sensor data. However, accuracies Stellenbosch University https://scholar.sun.ac.za iv generally remained lower than would allow for transferability and replication of the current methodology. Classification algorithm optimisation, supervised segmentation and improved training data should be considered to improve these results. / AFRIKAANSE OPSOMMING: Beeld-klassifikasie word al ‘n geruime tyd in aardwaarneming gebruik en word gedryf deur die behoefte aan akkurate kaarte om konseptuele en voorspellende modelle van aard-stelsel prosesse te ontwikkel. Sintetiese apertuur radar (SAR) beelde word ook meer dikwels in landdekking klassifikasie gebruik as gevolg van die aanvullende waarde daarvan met optiese data. Daar is dus 'n groeiende behoefte aan betroubare, akkurate metodes vir die gesamentlike gebruik van SAR en optiese data in landdekking klassifikasies. Die kombinasie van datastelle bring egter ‘n onvermydelike verhoging in data dimensionaliteit mee, en hierdie groot, komplekse datastelle is moeilik om te hanteer. Dus is dit belangrik om die voordele en beperkings van die gebruik van multi-temporale, dubbel-sensor data vir toepassings soos landdekking-klassifikasie te evalueer. Die waarde van gekombineerde (versmelte) RADARSAT-2 en SPOT-5 beelde word in hierdie tesis deur middel van vier eksperimente geevalueer. In Eksperiment 1 is die gebruik van kenmerk seleksie vir dimensionaliteit-vermindering toegepas. Die ranglys van belangrike kenmerke vir beide enkel-sensor en 'n dubbel-sensor data is beoordeel vir vier datums wat oor 'n tydperk van 6 maande strek. Die gemiddelde tekstuur kenmerke uit die optiese lae is konsekwent hoog oor alle datums geplaas. In die twee later datums (29 Mei en 9 Augustus 2014) was die SAR kenmerke meer algemeen, wat dui op die aanvullende aard van SAR en optiese data. SAR data dus gebruik kan word om klasse te onderskei wanneer optiese beelde onvoldoende daarvoor is. Eksperiment 2 het die akkuraatheid van ses gerigte en masjien-leer klassifikasie algoritmes vergelyk om te bepaal watter die beste met hierdie komplekse datastel presteer. Die random gorest klassifikasie algoritme het die hoogste akkuraatheid bereik en is dus in Eksperimente 3 en 4 gebruik. Eksperiment 3 het die voordele van gekombineerde SAR-optiese beelde oor enkel-sensor beelde vir landdekking klassifikasies op vier afsonderlike datums beoordeel. Die versmelte beelde het konsekwent hoër algehele akkuraathede as enkel-sensor beelde gelewer. Die 29 Mei 2014 data het die hoogste akkuraatheid van 69,8% bereik. Die versmelte klassifikasies het ook meer konsekwente resultate oor die vier datums gelewer en die enkel-sensor beelde het tot laer akkuraathede gelei, veral vir die later datums. In Eksperiment 4 is die gebruik van multi-temporale, dubbel-sensor data vir klassifikasie ge-evalueer. Kenmerkseleksie is gebruik om die data stel van 638 potensiële kenmerke na 50 te verminder, wat die beste akkuraatheid van 74,1% gelewer het. Hierdie resultaat bevestig die belangrikheid van multi-temporale data vir grond dekking klassifikasies. Dit bekragtig ook die gebruik van kenmerkseleksie om data vermindering suksesvol te rig sonder om die akkuraatheid van die finale produk te belemmer. Stellenbosch University https://scholar.sun.ac.za vi Multi-temporale en dubbel-sensor data toon potensiaal vir die kartering van landdekking in 'n tropiese, bergagtige streek wat andersins uitdagend sou wees om te karteer met behulp van enkel-sensor data. Oor die algemeen het akkuraathede egter te laag gebly om vir oordraagbaarheid en herhaling van die huidige metode toe te laat. Klassifikasie algoritme optimalisering, gerigte segmentering en verbeterde opleiding data moet oorweeg word om hierdie resultate te verbeter.

The predicted increase in atmospheric temperatures is expected to affect the turnover of soil organic carbon in permafrost soils through modifications of the soil thermal regime. However, the tundra biome is formed of a mosaic of diverse landscape types with differing patterns of soil organic carbon storage and partitioning. Among these, differences in e.g. vegetation diversity and soil movements due to freeze-thaw processes are of main importance for assessing potential C remobilization under a changing climate. In this study, we described the storage of soil organic carbon (SOC) and the aboveground phytomass carbon in relation to geomorphology and periglacial features for two areas on Taymir Peninsula (Arctic Russia). An average of 29.5 kg C m-2, calculated by upscaling with a land cover classification, is stored in the upper soil meter at these two study sites. The mean C phytomass storage amounts to ca.0.406 Kg C m-2, or only 1.38% of the total SOC storage. The topography, at different scales, plays an important role in the carbon partitioning. High amounts of soil organic carbon are found in highland areas and within the patterned ground features found in peatlands. The highest amounts of aboveground phytomass carbon are found in deciduous shrubs and moss layers. The large variability in carbon distribution within land cover types among the sites reveals the challenge of upscaling the carbon storage values over the Arctic and thus highlight the necessity to carry out detailed field inventories in this region.

Land cover classification using remotely sensed data is a critical first step in large-scale environmental monitoring, resource management and regional planning. The classification task is made difficult by severe atmospheric scattering and absorption, seasonal variation, spatial dependence, complex surface dynamics and geometries, and large intra-class variability. Most of the recent research effort in land cover classification has gone into the development of increasingly robust and accurate (and also increasingly complex) classifiers by constructing–often in an ad hoc manner–multispectral, multitemporal, multisource classifiers using modern machine learning techniques such as artificial neural networks, fuzzy-sets, and expert systems. However, the focus has always been (almost exclusively) on increasing the classification accuracy of newly developed classifiers. We would of course like to perform land cover classification (i) as accurately as possible, but also (ii) as quickly as possible. Unfortunately there exists a tradeoff between these two requirements, since the faster we must make a decision, the lower we expect our classification accuracy to be, and conversely, a higher classification accuracy typically requires that we observe more samples (i.e., we must wait longer for a decision). Sequential analysis provides an attractive (indeed an optimal) solution to handling this tradeoff between the classification accuracy and the detection delay–and it is the aim of this study to apply sequential analysis to the land cover classification task. Furthermore, this study deals exclusively with the binary classification of coarse resolution MODIS time series data in the Gauteng region in South Africa, and more specifically, the task of discriminating between residential areas and vegetation is considered. / Dissertation (MEng)--University of Pretoria, 2011. / Electrical, Electronic and Computer Engineering / unrestricted

No description available.

Healthy forests and soils are crucial for the very existence of mankind as they provide food, clean water and air, shade and protection against floods and storms. With their photosynthetic carbon storage ability, they mitigate climate change and fertilise and stabilise soils. Unfortunately, deforestation and the loss of fertile soils are the bleak reality and among the world’s most pressing challenges. Over the past decades Kenya has faced severe deforestation, but efforts are being undertaken to reverse deforestation, revegetate degraded land and combat erosion. Satellite remote sensing technology becomes increasingly useful for vegetation monitoring as the data quality improves and the costs decrease. This thesis explores the potential of free open access Sentinel-2 data for vegetation monitoring through Random Forest land cover classification and post-classification change detection on Rusinga Island, Kenya. Different single-date and multi-temporal predictor datasets differentiating respectively between five and four classes were examined to develop the most suitable model. The classification achieved acceptable results when assessed on an independent test dataset (overall accuracy of 90.06% with five classes and 96.89% with four classes), which should however be confirmed on the ground and could potentially be improved with better reference data. In this study, change detection could only be analysed over a time frame of two years, which is too short to produce meaningful results. Nevertheless, the method was proven conceptually and could be applied in the future to monitor land cover changes on Rusinga Island.

Land cover information is a key input to many earth system models, and thus accurate and consistent land cover maps are critically important to global change science. However, existing global land cover products show unrealistically high levels of year-to-year change. This thesis explores methods to improve accuracies for global land cover classifications, with a focus on reducing spurious year-to-year variation in results derived from MODIS data. In the first part of this thesis I use clustering to identify spectrally distinct sub-groupings within defined land cover classes, and assess the spectral separability of the resulting sub-classes. Many of the sub-classes are difficult to separate due to a high degree of overlap in spectral space. In the second part of this thesis, I examine two methods to reduce year-to-year variation in classification labels. First, I evaluate a technique to construct training data for a per-pixel supervised classification algorithm by combining multiple years of spectral measurements. The resulting classifier achieves higher accuracy and lower levels of year-to-year change than a reference classifier trained using a single year of data. Second, I use a spatio-temporal Markov Random Field (MRF) model to post-process the predictions of a per-pixel classifier. The MRF framework reduces spurious label change to a level comparable to that achieved by a post-hoc heuristic stabilization technique. The timing of label change in the MRF processed maps better matched disturbance events in a reference data, whereas the heuristic stabilization results in label changes that lag several years behind disturbance events.