BACKDATING OF INVARIANT PIXELS: COMPARISON OF ALGORITHMS FOR LAND USE AND LAND COVER CHANGE (LUCC) DETECTION IN THE SUBTROPICAL BRAZILIAN ATLANTIC FOREST

A challenge for the use of medium spatial resolution imagery for land use change detection consists of the reduced availability of ground reference data for previous dates. This study aims to obtain invariant training points using the backdating process for supervised classification of images that have no field data available. The study area comprises 1,353 km² in Santa Catarina, southern Brazil. We compared the accuracy performance of invariant area sets (binary change maps) generated by using three methods (IR-MAD - Iteratively Reweighted Multivariate Alteration Detection, CVA - Change Vector Analysis and SGD - Spectral Gradient Difference) for two periods (2017- 2011 and 2011-2006). The classification of the Landsat-5 TM image of 2006 was performed using as training data the sets of points indicated as invariant in the binary maps resulted from the three abovementioned methods. The accuracies for seven land-use classes were computed. The overall accuracy was greater (80,5% and 80,2%) when using training areas achieved by CVA and SGD, respectively than IR-MAD (76%). Were obtained accuracies greater than 80% for the forest class. The results stress that the combination of the IR-MAD and SGD is preferable since the CVA is more time consuming due to the subjective application of thresholds

Linking thermal variability and change to urban growth in Harare Metropolitan City using remotely sensed data.

Doctor of Philosophy in Environmental Science. University of KwaZulu-Natal. Pietermaritzburg, 2017.Urban growth, which involves Land Use and Land Cover Changes (LULCC), alters land surface thermal properties. Within the framework of rapid urban growth and global warming, land surface temperature (LST) and its elevation have potential significant socio-economic and environmental implications. Hence the main objectives of this study were to (i) map urban growth, (ii) link urban growth with indoor and outdoor thermal conditions and (iii) estimate implications of thermal trends on household energy consumption as well as predict future urban growth and temperature patterns in Harare Metropolitan, Zimbabwe. To achieve these objectives, broadband multi-spectral Landsat 5, 7 and 8, in-situ LULC observations, air temperature (Ta) and humidity data were integrated. LULC maps were obtained from multi-spectral remote sensing data and derived indices using the Support Vector Machine Algorithm, while LST were derived by applying single channel and split window algorithms. To improve remote sensing based urban growth mapping, a method of combining multi-spectral reflective data with thermal data and vegetation indices was tested. Vegetation indices were also combined with socio-demographic data to map the spatial distribution of heat vulnerability in Harare. Changes in outdoor human thermal discomfort in response to seasonal LULCC were evaluated, using the Discomfort Index (DI) derived parsimoniously from LST retrieved from Landsat 8 data. Responses of LST to long term urban growth were analysed for the period from 1984 to 2015. The implications of urban growth induced temperature changes on household air-conditioning energy demand were analysed using Landsat derived land surface temperature based Degree Days. Finally, the Cellular Automata Markov Chain (CAMC) analysis was used to predict future landscape transformation at 10-year time steps from 2015 to 2045. Results showed high overall accuracy of 89.33% and kappa index above 0.86 obtained, using Landsat 8 bands and indices. Similar results were observed when indices were used as stand-alone dataset (above 80%). Landsat 8 derived bio-physical surface properties and socio-demographic factors, showed that heat vulnerability was high in over 40% in densely built-up areas with low-income when compared to “leafy” suburbs. A strong spatial correlation (α = 0.61) between heat vulnerability and surface temperatures in the hot season was obtained, implying that LST is a good indicator of heat vulnerability in the area. LST based discomfort assessment approach retrieved DI with high accuracy as indicated by mean percentage error of less than 20% for each sub-season. Outdoor thermal discomfort was high in hot dry season (mean DI of 31oC), while the post rainy season was the most comfortable (mean DI of 19.9oC). During the hot season, thermal discomfort was very low in low density residential areas, which are characterised by forests and well maintained parks (DI ≤27oC). Long term changes results showed that high density residential areas increased by 92% between 1984 and 2016 at the expense of cooler green-spaces, which decreased by 75.5%, translating to a 1.98oC mean surface temperature increase. Due to surface alterations from urban growth between 1984 and 2015, LST increased by an average of 2.26oC and 4.10oC in the cool and hot season, respectively. This decreased potential indoor heating energy needed in the cool season by 1 degree day and increased indoor cooling energy during the hot season by 3 degree days. Spatial analysis showed that during the hot season, actual energy consumption was low in high temperature zones. This coincided with areas occupied by low income strata indicating that they do not afford as much energy and air conditioning facilities as expected. Besides quantifying and strongly relating with energy requirement, degree days provided a quantitative measure of heat vulnerability in Harare. Testing vegetation indices for predictive power showed that the Urban Index (UI) was comparatively the best predictor of future urban surface temperature (r = 0.98). The mean absolute percentage error of the UI derived temperature was 5.27% when tested against temperature derived from thermal band in October 2015. Using UI as predictor variable in CAMC analysis, we predicted that the low surface temperature class (18-28oC) will decrease in coverage, while the high temperature category (36-45oC) will increase in proportion covered from 42.5 to 58% of city, indicating further warming as the city continues to grow between 2015 and 2040. Overall, the findings of this study showed that LST, human thermal comfort and air-conditioning energy demand are strongly affected by seasonal and urban growth induced land cover changes. It can be observed that urban greenery and wetlands play a significant role of reducing LST and heat transfer between the surface and lower atmosphere and LST may continue unless effective mitigation strategies, such as effective vegetation cover spatial configuration are adopted. Limitations to the study included inadequate spatial and low temporal resolution of Landsat data, few in-situ observations of temperature and LULC classification which was area specific thus difficult for global comparison. Recommendations for future studies included data merging to improve spatial and temporal representation of remote sensing data, resource mobilization to increase urban weather station density and image classification into local climate zones which are of easy global interpretation and comparison

Anthropogenic activities contribute to changes in forest cover in the shale oil and gas region of Northeastern British Columbia

The boreal forest ecosystems have been changing due to varying levels of anthropogenic land use processes such as logging, oil and gas activities, and agriculture. However, the cumulative impacts of these processes are likely to lead to a lasting degradation of the boreal forest ecosystem; and thus, contributing to environmental change. In this study, methods from Landscape Ecology, GIS, and remote sensing were used to process Landsat images and spatial data for shale gas infrastructure. These datasets and methods were used for measuring and assessing the forest change pattern in a study area in northeastern British Columbia (BC). The results of the study show that gross loss (5.98%) of coniferous forest cover in the timber harvest land base (THLB) is higher than the rate of gross loss (3.22%) of the coniferous forest cover in the area outside the THLB. However, the rate of net loss in coniferous forest cover is smaller in the THLB than that of outside the THLB (net loss THLB=0.6%; net loss non-THLB=1.7%). These dynamics in forest cover suggest that it is more likely for forest cover to regenerate much faster in the THLB than outside the THLB. The quantity of forest cover loss (0.163%) from shale oil and gas well pads development is more than the amount of forest loss from shale oil and gas access roads (0.017%) and pipeline development (0.057%). A higher amount of forest fragmentation is associated with periods and locations that have a high amount of anthropogenic-induced land classes in the landscape. These results of the study could serve as the information for modelling land change and fragmentation in the future. The finding from this study could assist land managers in the allocation of land uses across space as well as the formulation of effective and efficient policy frameworks and management initiatives

Integrating Participatory Methods and Remote Sensing to Enhance Understanding of Ecosystem Service Dynamics Across Scales

The value of Traditional Ecological Knowledge (TEK) for informing resource management has long been recognized; however, its incorporation into ecosystem services (ES) assessments remains uncommon. Often “top-down” approaches are utilized, depending on “expert knowledge”, that are not relevant to local resource users. Here we propose an approach for combining participatory methods with remote sensing to provide a more holistic understanding of ES change. Participatory mapping in focus group discussions identified TEK regarding what ES were present, where, and their value to communities. TEK was then integrated with satellite imagery to extrapolate to the landscape-scale. We demonstrate our method for Nyangatom communities in the Lower Omo Valley, Ethiopia, showing for the first time the ES impacts of regional environmental change, including the Gibe III dam, for communities in the Omo River basin. Results confirmed the collapse of flood-retreat cultivation associated with the loss of the annual Omo flood. Communities reported declines in many other provisioning ES, and these results were supported by satellite mapping, which showed substantial reductions in land covers with high ES value (shrubland and wetland), leading to consequent ES declines. Our mixed-methods approach has potential to be applied in other regions to generate locally relevant information for evaluating ES dynamics and improving management of natural resources

Investigation into the bio-physical constraints on farmer turn-out-date decisions using remote sensing and meteorological data.

ThesisDoctoral thesisAccepted versionGrass is the most common landcover in Ireland and covers a bigger percentage (52%) of the country than any other in Europe. Grass as fodder is Ireland’s most important crop and is the foundation of its most important indigenous industry, agriculture. Yet knowledge of its distribution, performance and yield is scant. How grass is nationally, on a farm by farm, year by year basis managed is not known. In this thesis the gaps in knowledge about grassland performance across Ireland are presented along with arguments on why these knowledge gaps should be closed. As an example the need for high spatial resolution animal stocking rate data in European temperate grassland systems is shown. The effect of high stocking density on grass management is most apparent early in the growing season, and a 250m scale characterization of early spring vegetation growth from 2003-2012, based on MODIS NDVI time series products, is constructed. The average rate of growth is determined as a simple linear model for each pixel, using only the highest quality data for the period. These decadal spring growth model coefficients, start of season cover and growth rate, are regressed against log of stocking rate (r2 19 = 0.75, p<0.001). This model stocking rate is used to create a map of grassland use intensity in Ireland, which, when tested against an independent set of stocking data, is shown to be successful with an RMSE of 0.13 Livestock Unit/ha for a range of stocking densities from 0.1 to 3.3 Livestock Unit/ha. This model provides the first validated high resolution approach to mapping stocking rates in intensively managed European grassland systems. There is a demonstrated a need for a system to estimate current growing conditions. Using the spring growth model constructed for estimating stocking density a new style of grass growth progress anomaly map in the time-domain was developed. Using the developed satellite dataset 1 and 12 years of ground climate station data in Ireland, NDVI was modelled against time as a proxy for grass growth This model is the reference for estimating current seasonal progress of grass growth against a ten year average. The model is developed to estimate Seasonal Progress Anomalies in the Time domain (SPAT), giving a result in terms of “days behind” and “days ahead” of the norm. SPAT estimates for 2012 and 2013 are compared to ground based estimates from 30 climate stations and have a correlation coefficient of 0.897 and RMSE of 15days. The method can successfully map current grass growth trends compared to the average and present this information to the farmer in simple everyday language. This is understood by the author to be the first validated growth anomaly service, and the first for intensive European grasslands. The decisions on when to turn out cattle (the turn out date (TOD)) from winter housing to spring grazing is an important one on Irish dairy farms which has significant impacts on operating costs on the farm. To examine the relationship of TOD to conditions, the National Farm Survey (NFS) of Ireland database was geocoded and the data on turn out dates from 199 farms across Ireland over five years was used. A fixed effects linear panel data model was employed to explore the association between TOD and conditions, as it allows for unobserved variation between farmers to be ignored in favour of modelling the variance year on year. The environmental variables used in the analysis account for 38% of the variance in the turn out dates on farms nationwide. National seasonal conditions dominate over local variation, and for every week earlier grass grows in spring, farmers gain 3.7 days in grazing season but ignore 3.3 days of growth that could have been used. Every 100mm extra rain in spring means TOD is a day later and every dry day leads to turn out being half a day earlier. A well-drained soil makes TOD 2.5 days earlier compared to a poorly drained soil and TOD gets a day later for every 16km north form the south coast. This work demonstrates that precision agriculture 1 driven by optical and radar satellite data is closer to being a reality in Europe driven by enormous amounts of free imagery from NASA and the ESA Sentinel programs coupled with open source meteorological data and models and new developments in data analytics

Soil erosion in the Alps : causes and risk assessment

The issue of soil erosion in the Alps has long been neglected due to the low economic value of the agricultural land. However, soil stability is a key parameter which affects ecosystem services like slope stability, water budgets (drinking water reservoirs as well as flood prevention), vegetation productivity, ecosystem biodiversity and nutrient production. In alpine regions, spatial estimates on soil erosion are difficult to derive because the highly heterogeneous biogeophysical structure impedes measurement of soil erosion and the applicability of soil erosion models. However, remote sensing and geographic information system (GIS) methods allow for spatial estimation of soil erosion by direct detection of erosion features and supply of input data for soil erosion models. Thus, the main objective of this work is to address the problem of soil erosion risk assessment in the Alps on catchment scale with remote sensing and GIS tools. Regarding soil erosion processes the focus is on soil erosion by water (here sheet erosion) and gravity (here landslides). For these two processes we address i) the monitoring and mapping of the erosion features and related causal factors ii) soil erosion risk assessment with special emphasis on iii) the validation of existing models for alpine areas. All investigations were accomplished in the Urseren Valley (Central Swiss Alps) where the valley slopes are dramatically affected by sheet erosion and landslides. For landslides, a natural susceptibility of the catchment has been indicated by bivariate and multivariate statistical analysis. Geology, slope and stream density are the most significant static landslide causal factors. Static factors are here defined as factors that do not change their attributes during the considered time span of the study (45 years), e.g. geology, stream network. The occurrence of landslides might be significantly increased by the combined effects of global climate and land use change. Thus, our hypothesis is that more recent changes in land use and climate affected the spatial and temporal occurrence of landslides. The increase of the landslide area of 92% within 45 years in the study site confirmed our hypothesis. In order to identify the cause for the trend in landslide occurrence time-series of landslide causal factors were analysed. The analysis revealed increasing trends in the frequency and intensity of extreme rainfall events and stocking of pasture animals. These developments presumably enhanced landslide hazard. Moreover, changes in land-cover and land use were shown to have affected landslide occurrence. For instance, abandoned areas and areas with recently emerging shrub vegetation show very low landslide densities. Detailed spatial analysis of the land use with GIS and interviews with farmers confirmed the strong influence of the land use management practises on slope stability. The definite identification and quantification of the impact of these non-stationary landslide causal factors (dynamic factors) on the landslide trend was not possible due to the simultaneous change of several factors. The consideration of dynamic factors in statistical landslide susceptibility assessments is still unsolved. The latter may lead to erroneous model predictions, especially in times of dramatic environmental change. Thus, we evaluated the effect of dynamic landslide causal factors on the validity of landslide susceptibility maps for spatial and temporal predictions. For this purpose, a logistic regression model based on data of the year 2000 was set up. The resulting landslide susceptibility map was valid for spatial predictions. However, the model failed to predict the landslides that occurred in a subsequent event. In order to handle this weakness of statistic landslide modelling a multitemporal approach was developed. It is based on establishing logistic regression models for two points in time (here 1959 and 2000). Both models could correctly classify >70% of the independent spatial validation dataset. By subtracting the 1959 susceptibility map from the 2000 susceptibility map a deviation susceptibility map was obtained. Our interpretation was that these susceptibility deviations indicate the effect of dynamic causal factors on the landslide probability. The deviation map explained 85% of new independent landslides occurring after 2000. Thus, we believe it to be a suitable tool to add a time element to a susceptibility map pointing to areas with changing susceptibility due to recently changing environmental conditions or human interactions. In contrast to landslides that are a direct threat to buildings and infrastructure, sheet erosion attracts less attention because it is often an unseen process. Nonetheless, sheet erosion may account for a major proportion of soil loss. Soil loss by sheet erosion is related to high spatial variability, however, in contrast to arable fields for alpine grasslands erosion damages are long lasting and visible over longer time periods. A crucial erosion triggering parameter that can be derived from satellite imagery is fractional vegetation cover (FVC). Measurements of the radiogenic isotope Cs-137, which is a common tracer for soil erosion, confirm the importance of FVC for soil erosion yield in alpine areas. Linear spectral unmixing (LSU), mixture tuned matched filtering (MTMF) and the spectral index NDVI are applied for estimating fractional abundance of vegetation and bare soil. To account for the small scale heterogeneity of the alpine landscape very high resolved multispectral QuickBird imagery is used. The performance of LSU and MTMF for estimating percent vegetation cover is good (r²=0.85, r²=0.71 respectively). A poorer performance is achieved for bare soil (r²=0.28, r²=0.39 respectively) because compared to vegetation, bare soil has a less characteristic spectral signature in the wavelength domain detected by the QuickBird sensor. Apart from monitoring erosion controlling factors, quantification of soil erosion by applying soil erosion risk models is done. The performance of the two established models Universal Soil Loss Equation (USLE) and Pan-European Soil Erosion Risk Assessment (PESERA) for their suitability to model erosion for mountain environments is tested. Cs-137 is used to verify the resulting erosion rates from USLE and PESERA. PESERA yields no correlation to measured Cs-137 long term erosion rates and shows lower sensitivity to FVC. Thus, USLE is used to model the entire study site. The LSU-derived FVC map is used to adapt the C factor of the USLE. Compared to the low erosion rates computed with the former available low resolution dataset (1:25000) the satellite supported USLE map shows “hotspots” of soil erosion of up to 16 t ha-1 a-1. In general, Cs-137 in combination with the USLE is a very suitable method to assess soil erosion for larger areas, as both give estimates on long-term soil erosion. Especially for inaccessible alpine areas, GIS and remote sensing proved to be powerful tools that can be used for repetitive measurements of erosion features and causal factors. In times of global change it is of crucial importance to account for temporal developments. However, the evaluation of the applied soil erosion risk models revealed that the implementation of temporal aspects, such as varying climate, land use and vegetation cover is still insufficient. Thus, the proposed validation strategies (spatial, temporal and via Cs-137) are essential. Further case studies in alpine regions are needed to test the methods elaborated for the Urseren Valley. However, the presented approaches are promising with respect to improve the monitoring and identification of soil erosion risk areas in alpine regions

Application of ERTS-1 imagery to state wide land information system in Minnesota

There are no author-identified significant results in this report

EXAMINING ASPEN EXPANSION FROM BEFORE AND AFTER PRESCRIBED BURNING IN A NATIVE FESCUE GRASSLAND THROUGH GEOSPATIAL TECHNIQUES

Native fescue (Fescue spp.) grasslands of the Intermountain West have become increasingly scarce due to the advent of modern agriculture, the loss of Indigenous people’s land management practices, modern wildfire management and the extirpation of bison (Bison bison bison). Native grassland is a biodiversity hot-spot, is significant for carbon sequestration, and essential to many species of flora and fauna that occur in the ecosystem. Our study site, on the Rocky Mountain Front in Waterton Lakes National Park, Alberta Canada, consists of 30 discrete aspen stands (Populous tremuloides) which are encroaching on this declining shortgrass fescue grassland. Parks Canada is attempting to suppress aspen expansion and improve fescue prairie through ecological restoration by instituting prescribed burns and elk (Cervus elaphus) browse. Prescribed burns will decrease woody vegetation through adult aspen stem mortality while stimulating regeneration, which is subsequently browsed by elk. The park has a wolf pack (Canis lupus) that preys primarily on the elk, thereby affecting aspen stem recruitment spatially. These dynamics create a natural laboratory for examining the interaction of fire, elk and wolves that impact the aspen/grassland dynamics. We measured the aspen stand structure before and after a prescribed burn set in spring of 2017 to determine the change in aspen stand area from before to after the burn. We measured aspen stands before the prescribed burn during the summer of 2016 via GNSS handheld mapping units. We collected post-burn measurements in summer 2017 via unmanned aerial system (UAS). We also conducted ground measurements for a subset of aspen stands in 2017 to ground-truth the aerial photography data. We used knowledge Engineer (KE) in Erdas Imagine for classifying the UAS imagery and then created polygons in ArcGIS to analyze the data from before and after prescribed burning. We also digitized all aspen stand layers from the UAS imagery through the heads-up digitization technique and used these data to compare the aspen stands from before to after prescribed burning. Aspen stand area did not decline at a statistically significant level for any layers we measured: canopy, regeneration, and shrub expansion before and after prescribed burning. We did see an observational decline in the total aspen canopy area

Urban Expansion, Land Use Land Cover Change and Human Impacts: A Case Study of Rawalpindi

Urbanization in Pakistan has increased rapidly from 25% in 1972 to 42% in 2012. Peripheral zones are being pushed by urbanization much beyond their previous extents. Moreover, dispersed developments along the highways/motorways and unplanned expansion of existing urban centres is instigating a substantial loss of vegetation and open spaces. This research is an effort to analyse the relationship between urban expansion and land use/cover change using a combination of remote sensing, census and field data. Rawalpindi has been chosen as a study area because of its rapidly changing population density and land cover over the last few decades, and availability of satellite and census data. Landsat MSS and TM images of 1972, 1979, 1998 and 2010 which are compatible with the 1972, 1981, 1998 and 2012 Census of Pakistan dates were classified using the Maximum Likelihood classifier. The results of the assessment of classification accuracy yielded an overall accuracy of 75.16%, 72.5%, for Landsat MSS 1972, 1979 images and 84.5% and 87.1% for Landsat TM 1998 and 2010 images. Results reveal that the built up area of the study area has been increased from 7,017 hectares to 36,220 hectares during the 1972 -2012 period. This expansion has been accompanied by the loss of agricultural and forest land. There has been a decrease of approximately 10,000 hectares in cropped area and 2,000 hectares in forest land of the study area during the 1998-2012 inter-censal period. Corroboration of official census data, remote sensing results and field based qualitative data supports the view that high population growth rate, industrialization, better educational and transportation facilities and proximity of the study area to the capital (Islamabad) are the major factors of urban expansion and resulting land cover changes The present research is expected to have significant implications for other rapidly urbanizing areas of Pakistan in particular, and the Global South in general, in delivering baseline information about long term land use/cover changes

Managing hybrid methods for integration and combination of data

This chapter looks at how monitoring can combine data from multiple sources, including basic observations coupled with auxiliary information and the use of reference data for classification and modelling. A vital component of monitoring research is to be able to combine and synthesize data in a systematic, transparent way that can integrate social and environmental factors and show how these reflect, overlap with, correlate to, and influence each other. Data types and relevant analytical methods are briefly discussed, as well as aspects of classification and semantics, showing best practice in analysis and some suitable methods for describing data properties such as data quality. Typical problems of incompleteness, lack of fit to semantic classes, thematic and geometric inaccuracy, and data redundancy are discussed, with a range of examples showing how these challenges can be met by identifying and filling gaps in datasets