Understanding variations in remote sensing data with illumination and sensor angle changes is important in agricultural crop monitoring. This research investigated field bidirectional reflectance factor (BRF) in crop differentiation and PROSAIL leaf area index (LAI) estimation. BRF and LAI data were collected for planophile and erectophile crops at three growth stages. In the solar principal plane, BRF differed optimally at 860 nm 60 days after planting (DAP) for canola and pea, at 860 nm 45 and 60 DAP for wheat and barley, and at 860 nm and 670 nm 45 and 60 DAP for planophiles versus erectophiles. The field BRF data helped better understand PROSAIL LAI estimation. NDVI was preferred for estimating LAI, however the MTVI2 vegetation index showed high sensitivity to view angles, particularly for erectophiles. The hotspot was important for crop differentiation and LAI. Availability of more along-track, off-nadir looking spaceborne sensors was recommended for agricultural crop monitoring. / xiii, 161 leaves : ill., map ; 29 cm

Papyrus (Cyperus papyrus .L) swamp is the most species rich habitat that play vital hydrological, ecological, and economic roles in central tropical and western African wetlands. However, the existence of papyrus vegetation is endangered due to intensification of agricultural use and human encroachment. Techniques for modelling the distribution of papyrus swamps, quantity and quality are therefore critical for the rapid assessment and proactive management of papyrus vegetation. In this regard, remote sensing techniques provide rapid, potentially cheap, and relatively accurate strategies to accomplish this task. This study advocates the development of techniques based on hyperspectral remote sensing technology to accurately map and predict biomass of papyrus vegetation in a high mixed species environment of St Lucia- South Africa which has been overlooked in scientific research. Our approach was to investigate the potential of hyperspectral remote sensing at two levels of investigation: field level and airborne platform level. First, the study provides an overview of the current use of both multispectral and hyperspectral remote sensing techniques in mapping the quantity and the quality of wetland vegetation as well as the challenges and the need for further research. Second, the study explores whether papyrus can be discriminated from each one of its coexistence species (binary class). Our results showed that, at full canopy cover, papyrus vegetation can be accurately discriminated from its entire co-existing species using a new hierarchical method based on three integrated analysis levels and field spectrometry under natural field conditions. These positive results prompted the need to test the use of canopy hyperspectral data resampled to HYMAP resolution and two machine learning algorithms in identifying key spectral bands that allowed for better discrimination among papyrus and other co-existing species (n = 3) (multi-class classification). Results showed that the random forest algorithm (RF) simplified the process by identifying the minimum number of spectral bands that provided the best overall accuracies. Narrow band NDVI and SR-based vegetation indices calculated from hyperspectral data as well as some vegetation indices published in literature were investigated to test their potential in improving the classification accuracy of wetland plant species. The study also evaluated the robustness and reliability of RF as a variables selection method and as a classification algorithm in identifying key spectral bands that allowed for the successful classification of wetland species. Third, the focus was to upscale the results of field spectroscopy analysis to airborne hyperspectral sensor (AISA eagle) to discriminate papyrus and it co-existing species. The results indicated that specific wavelengths located in the visible, red-edge, and near-infrared region of the electromagnetic spectrum have the highest potential of discriminating papyrus from the other species. Finally, the study explored the ability of narrow NDVI-based vegetation indices calculated from hyperspectral data in predicting the green above ground biomass of papyrus. The results demonstrated that papyrus biomass can be modelled with relatively low error of estimates using a non-linear RF regression algorithm. This provided a basis for the algorithm to be used in mapping wetland biomass in highly complex environments. Overall, the study has demonstrated the potential of remote sensing techniques in discriminating papyrus swamps and its co-existing species as well as in predicting biomass. Compared to previous studies, the RF model applied in this study has proved to be a robust, accurate, and simple new method for variables selection, classification, and modelling of hyperspectral data. The results are important for establishing a baseline of the species distributions in South African swamp wetlands for future monitoring and control efforts. / Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.

Local participation in environmental decision making processes is a recognized need if the goals of sustainable development are to be met. Spatial information is an important part of environmental decision making, but so far, technical barriers have prevented effective public participation in spatial data management and analysis. These barriers need to be overcome if participants are to take part in a decision making process in a manner that is both fair and competent. The study was undertaken to quantify land cover change in a particular region and, through this exercise, to determine what the practical barriers to public participation in decision making might be. The work was conducted in the Windermere Valley, British Columbia. Community questions about local environmental change were determined from a local newspaper and discussions with Environmental Non-Governmental Organizations (ENGO's). Using satellite imagery and other geospatial data, community questions about local environmental change were answered through the detection of land cover change for the period 1974--1991. The processes of acquiring the data and completing the analysis were evaluated with the criteria of fairness and competence. The products of the change detection analysis were evaluated based on how well they answered community questions. Suggestions are presented on what tools and resources ENGO's would require to complete a similar study to answer questions about the environment.

The subject of this thesis is the application of optical backscatter measurements to locate a special property of the lowest part of the atmosphere -- the mixing height. Mixing height is the altitude of the top of the layer where all the fluxes emitted at the ground become well mixed. Since Holzworth in 1967, the knowledge of this altitude is considered relevant when modeling transport of pollutants or general fluxes originating at the ground. Indirect estimations of the mixing height are possible using atmospheric models, but its accuracy is quite low. Since several institutions are attempting to estimate precise ground fluxes, networks of measurement stations are being created. The correct use of the measured fluxes, in order to estimate the evolution of the air masses, is limited by the accuracy of the localization of this layer. It can be detected in several different ways. Most are related to a direct sounding, performed with meteorological balloons. Remote sensing techniques are also attempted with acoustical or optical instruments. Both optical and acoustical methods have advantages and disadvantages. This work is focused on optical instruments like lidar and ceilometers, which are basically small cost-effective lidar systems.

Station siting, exposure, instrumentation, and time of observations influence longterm climatic records. This thesis compared and analyzed temperature data from four Kentucky Mesonet stations located in Fayette (LXGN), Franklin (LSML), Clark (WNCH), and Bullitt (CRMT) counties to two nearby Automated Surface Observation Systems (ASOS) stations in Kentucky. The ASOS stations are located at Louisville International Airport (Standiford Field - KSDF) and at Lexington Airport (Blue Grass Field - KLEX). The null hypothesis states that there is no significant difference in temperature measurements between the two types of stations. To quantify the differences in temperature measurements, geoprofiles and the following statistical procedures were used: coefficient of determination (R2), coefficient of efficiency (E), index of agreement (d), root mean square error (RMSE), and mean absolute error (MAE). Geoprofiles were developed using GIS, and take into account elevation, slope, hillshading, land use, and aspect for each site to help better understand the influence of local topography. It was found that temperature differences could be related to the advancement of weather patterns, vegetation growth and decay, and changes in the landscape at the stations. KSDF consistently recorded higher temperatures than those at CRMT. The positive bias ranged between 0.27 and 2.41 ºC during the time period of September 2009 to August 2010. KLEX was found to be warmer or cooler, with temperature differences that ranged from -1.42 to 0.22 ºC for LXGN, LSML, and WNCH. The index of agreement at KSDF for mean hourly temperatures, when compared to the Bullitt County mesonet station, ranged from 0.88 to 0.99. Meanwhile, the index of agreement at KLEX was 0.96 to 1.00 when compared to the Franklin, Fayette, and Clark mesonet stations. KLEX recorded temperatures that were higher or lower compared to the Franklin, Fayette, and Clark mesonet stations. At the seasonal scale, fall and summer showed larger differences between the Mesonet and ASOS observations. KSDF consistently recorded higher temperatures ranging up to 2.41 °C during the summer. The index of agreement at KSDF in the fall, when compared to the Bullitt County mesonet station average temperatures, ranged from 0.89 to 0.95, while in the summer it was 0.88 to 0.96. The d index indicates a good agreement between ASOS and mesonet stations in winter. KLEX indicates that the index of agreement, RMSE, and MAE are best during winter for all three stations, while in the fall and summer the agreement was not as strong when compared to the Franklin, Fayette, and Clark mesonet stations. In summary, results indicate that the Kentucky Mesonet and ASOS temperature measurements show significant differences throughout the year; therefore, the alternative hypothesis is accepted. These differences are attributed to biases associated with ASOS observations, nearby artificial sources of heating, equipment/maintenance procedures, and land use and land cover at the site.

Thesis (Ph. D.)--University of Hawaii at Manoa, 1995. / Includes bibliographical references. / Microfiche. / xx, 252 leaves, bound ill. (some col.) 29 cm

Thesis (M.A.)--University of Hawaii at Manoa, 2005. / Includes bibliographical references (leaves 80-94). / xvii, 94 leaves, bound ill. (some col.), maps (some col.) 29 cm

Thesis (Doctor of Engineering in Electrical Engineering)--Cape Peninsula University of Technology, 2017. / Remote sensing provides a way of frequently observing broad land surfaces. The availability of various earth observation data and their potential exploitation in a wide range of socioeconomic applications stimulated the rapid development of remote sensing technology. Much of the research and most of the publications dealing with remote sensing in the solar spectral domain focus on analysing and interpreting the spectral, spatial and temporal signatures of the observed areas. However, the angular signatures of the reflectance field, known as surface anisotropy, also merit attention. The current research took an exploratory approach to the land surface anisotropy described by the RPV model parameters derived from the MISR-HR processing system (denoted as MISR-HR anisotropy data or MISR-HR RPV data), over a period of 14+ years, for three typical terrestrial surfaces in the Western Cape Province of South Africa: a semi-desert area, a wheat field and a vineyard area. The objectives of this study were to explore (1) to what extent spectral and directional signatures of the MISR-HR RPV data may vary in time and space over the different targets (landscapes), and (2) whether the observed variations in anisotropy might be useful in classifying different land surfaces or as a supplementary method to the traditional land cover classification method. The objectives were achieved by exploring the statistics of the MISR-HR RPV data in each spectral band over the different land surfaces, as well as seasonality and trend in these data. The MISR-HR RPV products were affected by outliers and missing values, both of which influenced the statistics, seasonality and trend of the examined time series. This research proposes a new outlier detection method, based on the cost function derived from the RPV model inversion process. Removed outliers and missing values leave gaps in a MISR-HR RPV time series; to avoid introducing extra biases in the statistics of the anisotropy data, this research kept the gaps and relied on gap-resilient trend and seasonality detection methods, such as the Mann-Kendal trend detection and Lomb-Scargle periodogram methods. The exploration of the statistics of the anisotropy data showed that RPV parameter rho exhibited distinctive over the different study sites; NIR band parameter k exhibits prominent high values for the vineyard area; red band parameter Theta data are not that distinctive over different study sites; variance is important in describing all three RPV parameters. The explorations on trends also demonstrated interesting findings: the downward trend in green band parameter rho data for the semi-desert and vineyard areas; and the upward trend in blue band parameters k and Theta data for all the three study sites. The investigation on seasonality showed that all the RPV parameters had seasonal variations which differed over spectral bands and land covers; the results confirmed expectations in previous literature that parameter varies regularly along the observation time, and also revealed seasonal variations in the parameter rho and Theta data. The explorations on the statistics and seasonality of the MISR-HR anisotropy data show that these data are potentially useful for classifying different landscapes. Finally, the classification results demonstrated that both red band parameter rho data and NIR band parameter k data could successfully separate the three different land surfaces in this research, which fulfilled the second primary objective of this study. This research also demonstrated a classification method using multiple RPV parameters as the classification signatures to discriminate different terrestrial surfaces; significant separation results were obtained by this method.

abstract: The Dhofar Cloud Forest is one of the most diverse ecosystems on the Arabian Peninsula. As part of the South Arabian Cloud Forest that extends from southern Oman to Yemen, the cloud forest is an important center of endemism and provides valuable ecosystem services to those living in the region. There have been various claims made about the health of the cloud forest and its surrounding region, the most prominent of which are: 1) variability of the Indian Summer Monsoon threatens long-term vegetation health, and 2) human encroachment is causing deforestation and land degradation. This dissertation uses three independent studies to test these claims and bring new insight about the biodiversity of the cloud forest. Evidence is presented that shows that the vegetation dynamics of the cloud forest are resilient to most of the variability in the monsoon. Much of the biodiversity in the cloud forest is dominated by a few species with high abundance and a moderate number of species at low abundance. The characteristic tree species include Anogeissus dhofarica and Commiphora spp. These species tend to dominate the forested regions of the study area. Grasslands are dominated by species associated with overgrazing (Calotropis procera and Solanum incanum). Analysis from a land cover study conducted between 1988 and 2013 shows that deforestation has occurred to approximately 8% of the study area and decreased vegetation fractions are found throughout the region. Areas around the city of Salalah, located close to the cloud forest, show widespread degradation in the 21st century based on an NDVI time series analysis. It is concluded that humans are the primary driver of environmental change. Much of this change is tied to national policies and development priorities implemented after the Dhofar War in the 1970’s. / Dissertation/Thesis / Doctoral Dissertation Geography 2015

As wind energy plants continue to grow in size and complexity, advanced measurement technologies such as scanning Doppler LiDAR are essential for assessing site conditions and prospecting new development areas.   The RUNE project was initiated to determine best practices for the use of scanning LiDARs in resource assessments for near shore wind farms. The purpose of this thesis is to determine the optimum configuration for the plan position indicator (PPI) scan type of a scanning LiDAR. A task specific Automated Analysis Software (AAS) is created, and the sensitivity of the integrated velocity azimuth process (iVAP) reconstruction algorithm is examined using sector sizes ranging from 4 to 60 degrees. Further, a comparison to simultaneous dual Doppler measurement is presented in order to determine the necessity of deploying two LiDARs rather than one.    DTU has developed a coordinated long range coherent scanning multi-LiDAR array (the WindScanner system) based on modified Leosphere WindCube 200S devices and an application specific software framework and communication protocol. The long range WindScanner system was deployed at DTU’s test station in Høvsøre, Denmark and measurement data was collected over a period of 7 days. One WindScanner was performing 60 degree sector scans, while two others were placed in staring dual Doppler mode. All three beams were configured to converge atop a 116.5m instrumented meteorological mast.   A significant result was discovered which indicates that the accuracy of the reconstructed measurements do not differ significantly between sector sizes of 30 and 60 degrees. Using the smallest sector size which does not introduce systematic error has numerous benefits including: increasing the scan speed, measurement distance and angular resolution.   When comparing collocated dual Doppler, sector scan and in-situ met-mast instrumentation, we find very good agreement between all techniques. Dual Doppler is able to measure wind speeds within 0.1%, and 60 degree sector scan within 0.2% on average of the reference values. For retrieval of wind direction, the sector scan approach performs particularly well. This is likely attributable to lower errors introduced by the assumption of flow field homogeneity over the scanned area, in contract to wind direction which tends to be more non-uniform. For applications such as site resource assessments, where generally accurate 10 minute wind speed and direction values are required, a scanning LiDAR performing PPI scans with a sector size of between 30 and 38 degrees is recommended. The laser’s line of sight path should be directed parallel to the predominant wind direction and at the lowest elevation angle possible. / RUNE