Finding suitable transect spacing and sampling designs for accurate soil ECa mapping from EM38-MK2.

Finding an ideal sampling design is a crucial stage in detailed soil mapping to assure reasonable accuracy of resulting soil property maps. This study aimed to evaluate the influence of sampling designs and sample sizes on the quality of soil apparent electrical conductivity (ECa) maps from an electromagnetic sensor survey. Twenty-six (26) parallel transects were gathered in a 72-ha plot in Southeastern Brazil. Soil ECa measurements using an on-the-go electromagnetic induction sensor were taken every second using sensor vertical orientation. Two approaches were used to reduce the sample size and simulate kriging interpolations of soil ECa. Firstly, the number of transect lines was reduced by increasing the distance between them; thus, 26 transects with 40 m spacing; 13 with 80 m; 7 with 150 m; and 4 with 300 m. Secondly, random point selection and Douglas-Peucker algorithms were used to derive four reduced datasets by removing 25, 50, 75, and 95% of the points from the ECa survey dataset. Soil ECa was interpolated at 5 m output spatial resolution using ordinary kriging and the four datasets from each simulation (a total of twelve datasets). Map uncertainty was assessed by root mean square error and mean error metrics from 400 random samples previously selected for external map validation. Maps were evaluated on their uncertainty and spatial structure of variation. The transect elimination approach showed that maps produced with transect spacing up to 150 m could preserve the spatial structure of ECa variations. Douglas-Peucker results showed lower nugget values than random point simulations for all selected sample densities, except for a 95% point reduction. The soil ECa maps derived from the 75% reduced dataset (by random sampling or Douglas-Peucker) or from 13 transect lines (80 m spacing) showed reasonable accuracy (RMSE of validation circa 0.7) relative to the map interpolated from all survey points (RMSE of 0.5), suggesting that transect spacing of 80 m and reading intervals greater than one second can be used for improving the efficiency of on-the-go soil ECa surveys

Applications of Airborne and Portable LiDAR in the Structural Determination, Management, and Conservation of Southeastern U.S. Pine Forests

Active remote sensing techniques, such as Light Detection and Ranging (LiDAR), have transformed the field of forestry and natural resource management in the last decade. Intensive assessments of forest resources and detailed structural assessments can now be accomplished faster and at multiple landscape scales. The ecological applications of having this valuable information at-hand are still only being developed. This work explores the use of two active remote sensing techniques, airborne and portable LiDAR for forestry applications in a rapidly changing landscape, Southeastern Coastal Pine woodlands. Understanding the strengths and weaknesses of airborne and portable LiDAR, the tools used to extract structural information, and how to apply these to managing fire regimes are key to conserving unique upland pine ecosystems. Measuring habitat structure remotely and predicting habitat suitability through modeling will allow for the management of specific species of interest, such as threatened and endangered species. Chapter one focuses on the estimation of canopy cover and height measures across a variety of conditions of secondary upland pine and hardwood forests at Tall Timbers Research Station, FL. This study is unique since it uses two independent high resolution small-footprint LiDAR datasets (years 2002 and 2008) and extensive field plot and transect sampling for validation. Chapter One explores different tools available for metric derivation and tree extraction from discrete return airborne LiDAR data, highlighting strengths and weaknesses of each. Field and LiDAR datasets yielded better correlations for stand level comparisons, especially in canopy cover and mean height data extracted. Individual tree crown extraction from airborne LiDAR data significantly under-reported the total number of trees reported in the field datasets using either Fusion/LVD and LiDAR Analyst (Overwatch). Chapter two evaluates stand structure at the site of one of the longest running fire ecology studies in the US, located at Tall Timbers Research Station (TTRS) in the southeastern U.S. Small footprint high resolution discrete return LiDAR was used to provide an understanding of the impact of multiple disturbance regimes on forest structure, especially on the 3-dimensional spatial arrangement of multiple structural elements and structural diversity indices. LiDAR data provided sensitive detection of structural metrics, diversity, and fine-scale vertical changes in the understory and mid-canopy structure. Canopy cover and diversity indices were shown to be statistically higher in fire suppressed and less frequently burned plots than in 1- and 2-year fire interval treated plots, which is in general agreement with the increase from 2- to 3-year fire return interval being considered an ecological threshold for these systems (Masters et al. 2005). The results from this study highlight the value of the use of LiDAR in evaluating disturbance impacts on the three-dimensional structure of pine forest systems, particularly over large landscapes. Chapter three uses an affordable portable LiDAR system, first presented by Parker et al. (2004) and further modified for extra portability, to provide an understanding of structural differences between old-growth and secondary-growth forests in the Red Hills area of southwestern Georgia and North Florida. It also provides insight into the strengths and weaknesses in structural determination of ground-based portable systems in contrast to airborne LiDAR systems. Structural plot metrics obtained from airborne and portable LiDAR systems presented some similarities (i.e. canopy cover), but distinct differences appeared when measuring canopy heights (maximum and mean heights) using these different methods. Both the airborne and portable systems were able to provide gap detection and canopy cover estimation at the plot level. The portable system, when compared to the airborne LiDAR sensor, provides an underestimation of canopy cover in open forest systems ([less than]50% canopy cover), but is more sensitive in detection of cover in hardwood woodland plots ([greater than]60% canopy cover). The strength of the portable LiDAR system lies in the detection of 3-dimensional fine structural changes (i.e. recruitment, encroachment) and with higher sensitivity in detecting lower canopy levels, often missed by airborne systems. Chapter four addresses a very promising application for fine-scale airborne LiDAR data, the creation of habitat suitability models for species of management and conservation concerns. This Chapter uses fine scale LiDAR metrics, such as canopy cover at various height strata, canopy height information, and a measure of horizontal vegetation distribution (clumped versus dispersed) to model the preferences of 10 songbirds of interest in southeast US woodlands. The results from this study highlight the rapidly changing nature of habitat conditions and how these impact songbird occurrence. Furthermore, Chapter four provides insight into the use of airborne LiDAR to provide specific management guidance to enhance the suitable habitat for 10 songbird species. The collection of studies presented here provides applied tools for the use of airborne and portable LiDAR for rapid assessment and responsive management in southeastern pine woodlands. The advantages of detecting small changes in three-dimensional vegetation structure and how these can impact habitat functionality and suitability for species of interest are explored throughout the next four chapters. The research presented here provides an original and important contribution in the application of airborne and portable LiDAR datasets in forest management and ecological studies

Benthic mapping of the Bluefields Bay fish sanctuary, Jamaica

Small island states, such as those in the Caribbean, are dependent on the nearshore marine ecosystem complex and its resources; the goods and services provided by seagrass and coral reef for example, are particularly indispensable to the tourism and fishing industries. In recognition of their valuable contributions and in an effort to promote sustainable use of marine resources, some nearshore areas have been designated as fish sanctuaries, as well as marine parks and protected areas. In order to effectively manage these coastal zones, a spatial basis is vital to understanding the ecological dynamics and ultimately inform management practices. However, the current extent of habitats within designated sanctuaries across Jamaica are currently unknown and owing to this, the Government of Jamaica is desirous of mapping the benthic features in these areas. Given the several habitat mapping methodologies that exist, it was deemed necessary to test the practicality of applying two remote sensing methods - optical and acoustic - at a pilot site in western Jamaica, the Bluefields Bay fish sanctuary. The optical remote sensing method involved a pixel-based supervised classification of two available multispectral images (WorldView-2 and GeoEye-1), whilst the acoustic method comprised a sonar survey using a BioSonics DT-X Portable Echosounder and subsequent indicator kriging interpolation in order to create continuous benthic surfaces. Image classification resulted in the mapping of three benthic classes, namely submerged vegetation, bare substrate and coral reef, with an overall map accuracy of 89.9% for WorldView-2 and 86.8% for GeoEye-1 imagery. These accuracies surpassed those of the acoustic classification method, which attained 76.6% accuracy for vegetation presence, and 53.5% for bottom substrate (silt, sand and coral reef/ hard bottom). Both approaches confirmed that the Bluefields Bay is dominated by submerged aquatic vegetation, with contrastingly smaller areas of bare sediment and coral reef patches. Additionally, the sonar revealed that silty substrate exists along the shoreline, whilst sand is found further offshore. Ultimately, the methods employed in this study were compared and although it was found that satellite image classification was perhaps the most cost-effective and well-suited for Jamaica given current available equipment and expertise, it is acknowledged that acoustic technology offers greater thematic detail required by a number of stakeholders and is capable of operating in turbid waters and cloud covered environments ill-suited for image classification. On the contrary, a major consideration for the acoustic classification process is the interpolation of processed data; this step gives rise to a number of potential limitations, such as those associated with the choice of interpolation algorithm, available software and expertise. The choice in mapping approach, as well as the survey design and processing steps is not an easy task; however the results of this study highlight the various benefits and shortcomings of implementing optical and acoustic classification approaches in Jamaica.Persons automatically associate tropical waters with spectacular views of coral reefs and colourful fish; however many are perhaps not aware that these coral reefs, as well as other living organisms inhabiting the seabed are in fact extremely valuable to our existence. Healthy coral reefs and seagrass assist in maintaining the sand on our beaches and fish populations and are thereby crucial to the tourism and fishing industries in the Caribbean. For this reason, a number of areas are protected by law and have been designated fish sanctuaries or marine protected areas. In order to understand the functioning of theses areas and effectively inform management strategy, the configuration of what exists on the seafloor is crucial. In the same vein that a motorist needs a road map to navigate unknown areas, coastal stakeholders require maps of the seafloor in order to understand what is happening beneath the water’s surface. The location of seafloor habitats within fish sanctuaries in Jamaica are currently unknown and the Government is interested in mapping them. However a myriad of methods exist that could be employed to achieve this goal. Remote sensing is a broad grouping of methods that involve collecting information about an object without being in direct physical contact with it. Many researchers have successfully mapped marine areas using these techniques and it was believed crucial to test the practicality of two such methods, specifically optical and acoustic remote sensing. The main question to be answered from this study was therefore: Which mapping approach is better for benthic habitat mapping in Jamaica and possibly the wider Caribbean? Optical remote sensing relates to the interaction of energy with the Earth’s surface. A digital photograph is taken from a satellite and subsequently interpreted. Acoustic/ sonar technology involves the recording of waveforms reflected from the seabed. Both methods were employed at a pilot site, the Bluefields Bay fish sanctuary, situated in western Jamaica. The optical remote sensing method involved the classification of two satellite images (named WorldView-2 and GeoEye-1) and this process was informed using known positions of seafloor features, this being known as supervised image classification. With regard to the acoustic method, a field survey utilising sonar equipment (BioSonics DT-X Portable Echosounder) was undertaken in order to collect the necessary sonar data. The processed field data was modelled in order to convert lines of field point data to one continuous map of the sanctuary, a process known as interpolation. The accuracy of each method was then tested using field knowledge of what exists in the sanctuary. The map resulting from the image classification revealed three seafloor types, namely submerged vegetation, coral reef and bare seafloor. The overall map accuracy was 89.9% for the WorldView-2 image and 86.8% for GeoEye-1 imagery. These accuracies surpassed those attained from the acoustic classification method (76.6% for vegetation presence and 53.5% for bottom type - silt, sand and coral reef/ hard bottom). Similar to previous studies undertaken, it was shown that the seabed of Bluefields Bay is primarily inhabited by submerged aquatic vegetation (including seagrass and algae), with contrastingly smaller areas of bare sediment and coral reef. Ultimately, the methods employed in this study were compared and the pros and cons of each were weighed in order to deem one method more suitable in Jamaica. Often, the presence of cloud and suspended matter in the water block the view of the seafloor making image classification difficult. On the contrary, acoustic surveys are capable of operating throughout cloudy conditions and attaining more detailed information of the ocean floor, otherwise not possible with optical remote sensing. A major step in the acoustic classification process however, was the interpolation of processed data, which may introduce additional limitations if careful consideration is not given to the intricacies of the process. Lastly, the acoustic survey certainly required greater financial resources than satellite image classification. In answer to the main question of this study, the most cost effective and feasible mapping method for Jamaica is satellite image classification (based on the results attained). It must be stressed however that the effective implementation of any method will depend on a number of factors, such as available software, equipment, expertise and user needs, that must be weighed in order to select the most feasible mapping method for a particular site

Online measurement of soil organic carbon as correlated with wheat normalised difference vegetation index in a vertisol field

This study explores the potential of visible and near infrared (vis-NIR) spectroscopy for onlinemeasurement of soil organic carbon (SOC). It also attempts to explore correlations and similarities between the spatial distribution of SOC and normalized differential vegetation index (NDVI) of a wheat crop. The online measurement was carried out in a clay vertisol field covering 10 ha of area in Karacabey, Bursa, Turkey. Kappa statistics were carried out between different SOC and NDVI data to investigate potential similarities. Calibration model of SOC in full cross-validationresulted in a good accuracy ( 2 = 0.75, root mean squares error of prediction (RMSEP) = 0.17%, and ratio of prediction deviation (RPD) = 1.81). The validation of the calibration model using laboratory spectra provided comparatively better prediction accuracy ( 2 = 0.70, RMSEP = 0.15%, and RPD = 1.78), as compared to the online measured spectra ( 2 = 0.60, RMSEP = 0.20%, and RPD = 1.41). Although visual similarity was clear, low similarity indicated by a lowKappa value of 0.259 was observed between the online vis-NIR predicted full-point (based on all pointsmeasured in the field, e.g., 6486 points)map of SOC and NDVI map

Assessing the Potential of LAPAN-A3 Data for Landuse/landcover Mapping

LAPAN-A3 / LAPAN-IPB is the third generation of micro-satellite developed by Indonesian National Institute of Aeronautics and Space (LAPAN). The satellite carries a multispectral push-broom sensor that can record the earth's surface at the visible and near-infrared spectrum. Being launched in June 2016, there has no been many publications related to the use of LAPAN-A3 multispectral data for landuse/landcover (LULC) mapping. This paper aims to provide information regarding the use of LAPAN-A3 data for the LULC extraction maximum likelihood algorithm as well as neural network and then evaluate the results. The LAPAN-A3 image was geometrically corrected by using Landsat-8 OLI image as reference data. Three test areas with a size of 1200x945 pixels are then selected for pixel-based classification with the two aforementioned algorithms. For comparison, both LAPAN-A3 and Landsat-8 data were classified for 3 test areas. Accuracy assessment was performed on both datasets using manually interpreted SPOT-6 Pansharpened image as reference data. Preliminary results showed that LAPAN-A3 were able to extract  10 different LULC classes, comprises of built-up area, forest, rivers, fishponds, shrubs, wetland forests, rice fields, sea, agricultural land, and bare soil. The overall accuracy of LAPAN-A3 data is generally lower than Landsat-8, which ranges from 49.76% to 71.74%. These results illustrate the potential of LAPAN-A3 data to derive LULC information. The lack of necessary parameters to perform radiometric correction and blurring effect are several issues that need to be solved to improve the accuracy LULC.

Spatio-temporal analysis of coastal sediment erosion in Cape Town through remote sensing and geoinformation science

Coastal erosion can be described as the landward or seaward propagation of coastlines. Coastal processes occur over various space and time scales, limiting in-situ approaches of monitoring change. As such it is imperative to take advantage of multisensory, multi-scale and multi-temporal modern spatial technologies for multi-dimensional coastline change monitoring. The research presented here intends to showcase the synergy amongst remote sensing techniques by showcasing the use of coastal indicators towards shoreline assessment over the Kommetjie and Milnerton areas along the Cape Town coastline. There has been little progress in coastal studies in the Western Cape that encompass the diverse and dynamic aspects of coastal environments and in particular, sediment movement. Cape Town, in particular; is socioeconomically diverse and spatially segregated, with heavy dependence on its 240km of coastline. It faces sea level rise intensified by real-estate development close to the high-water mark and on reclaimed land. Spectral indices and classification techniques are explored to accommodate the complex bio-optical properties of coastal zones. This allows for the segmentation of land and ocean components to extract shorelines from multispectral Landsat imagery for a long term (1991-2021) shoreline assessment. The DSAS tool used these extracted shorelines to quantify shoreline change and was able to determine an overall averaged erosional rate of 2.56m/yr. for Kommetjie and 2.35m/yr. for Milnerton. Beach elevation modelling was also included to evaluate short term (2016-2021) sediment volumetric changes by applying Differential Interferometry to Sentinel-1 SLC data and the Waterline method through a combination of Sentinel -1 GRD and tide gauge data. The accuracy, validation and correction of these elevation models was conducted at the pixel level by comparison to an in-field RTK GPS survey used to capture the current state of the beaches. The results depict a sediment deficit in Kommetjie whilst accretion is prevalent along the Milnerton coastline. Shoreline propagation and coastal erosion quantification leads to a better understanding of geomorphology, hydrodynamic and land use influences on coastlines. This further informs climate adaptation strategies, urban planning and can support further development of interactive coastal information systems

Archaeological remote sensing: visualisation and analysis of grass-dominated environments using airborne laser scanning and digital spectral data

The use of airborne remote sensing data for archaeological prospection is not a novel concept, but it is one that has been brought to the forefront of current work in the discipline of landscape archaeology by the increasing availability and application of airborne laser scanning data (ALS). It is considered that ALS, coupled with imaging of the non-visible wavelengths using digital spectral sensors has the potential to revolutionise the field of archaeological remote sensing, overcoming some of the issues identified with the most common current technique of oblique aerial photography. However, as with many methods borrowed from geographic or environmental sciences, archaeologists have yet to understand or utilise the full potential of these sensors for deriving archaeological feature information. This thesis presents the work undertaken between 2008-11 at Bournemouth University that aimed to assess the full information content of airborne laser scanned and digital spectral data systematically with respect to identifying archaeological remains in non-alluvial environments. A range of techniques were evaluated for two study areas on Salisbury Plain, Wiltshire (Everleigh and Upavon) to establish how the information from these sensors can best be extracted and utilised. For the Everleigh Study Area archive airborne data were analysed with respect to the existing transcription from archive aerial photographs recorded by English Heritage's National Mapping Programme. At Upavon, spectral and airborne laser scanned data were collected by the NERC Airborne Research and Survey Facility to the specifications of the project in conjunction with a series of ground-based measures designed to shed light on the contemporary environmental factors influencing feature detectability. Through the study of visual and semi-automatic methods for detection of archaeological features, this research has provided a quantitative and comparative assessment of airborne remote sensing data for archaeological prospection, the first time that this has been achieved in the UK. In addition the study has provided a proof of concept for the use of the remote sensing techniques trialled in temperate grassland environments, a novel application in a field previously dominated by examples from alluvial and Mediterranean landscapes. In comparison to the baseline record of the Wiltshire HER, ALS was shown to be the most effective technique, detecting 76% of all previously know features and 72% of all the total number of features recorded in the study. Combining the spectral data from both January and May raised this total to 83% recovery of all previously known features, illustrating the value of multi-sensor survey. It has also been possible to clarify the strengths and weaknesses of a wide range of visualisation techniques through detailed comparative analysis and to show that some techniques in particular local relief modelling (ALS) and single band mapping (digital spectral data) are more suited to the aims of archaeological prospection than others, including common techniques such as shaded relief modelling (ALS) and True Colour Composites (digital spectral data). In total the use of “non-standard” or previously underused visualisation techniques was shown to improve feature detection by up to 18% for a single sensor type. Investigation of multiple archive spectral acquisitions highlighted seasonal differences in detectability of features that had not been previously observed in these data, with the January spectral data allowing the detection of 7% more features than the May acquisition. A clearer picture of spectral sensitivity of archaeological features was also gained for this environment with the best performing spectral band lying in the NIR for both datasets (706-717nm) and allowing detection c.68% of all the features visible across all the wavelengths. Finally, significant progress has been made in the testing of methods for combining data from different airborne sensors and analysing airborne data with respect to ground observations, showing that Brovey sharpening can be used to combine ALS and spectral data with up to 87% recovery of the features predicted by transcription from the contributing source data. This thesis concludes that the airborne remote sensing techniques studied have quantifiable benefit for detection of archaeological features at a landscape scale especially when used in conjunction with one another. The caveat to this is that appropriate use of the sensors from deployment, to processing, analysis and interpretation of features must be underpinned by a detailed understanding of how and why archaeological features might be represented in the data collected. This research goes some way towards achieving this, especially for grass-dominated environments but it is only with repeated, comparative analyses of these airborne data in conjunction with environmental observations that archaeologists will be able to advance knowledge in this field and thus put airborne remote sensing data to most effective use

ICESat/GLAS Data as a Measurement Tool for Peatland Topography and Peat Swamp Forest Biomass in Kalimantan, Indonesia

Indonesian peatlands are one of the largest near-surface pools of terrestrial organic carbon. Persistent logging, drainage and recurrent fires lead to huge emission of carbon each year. Since tropical peatlands are highly inaccessible, few measurements on peat depth and forest biomass are available. We assessed the applicability of quality filtered ICESat/GLAS (a spaceborne LiDAR system) data to measure peatland topography as a proxy for peat volume and to estimate peat swamp forest Above Ground Biomass (AGB) in a thoroughly investigated study site in Central Kalimantan, Indonesia. Mean Shuttle Radar Topography Mission (SRTM) elevation was correlated to the corresponding ICESat/GLAS elevation. The best results were obtained from the waveform centroid (R2 = 0.92; n = 4,186). ICESat/GLAS terrain elevation was correlated to three 3D peatland elevation models derived from SRTM data (R2 = 0.90; overall difference = −1.0 m, ±3.2 m; n = 4,045). Based on the correlation of in situ peat swamp forest AGB and airborne LiDAR data (R2 = 0.75, n = 36) an ICESat/GLAS AGB prediction model was developed (R2 = 0.61, n = 35). These results demonstrate that ICESat/GLAS data can be used to measure peat topography and to collect large numbers of forest biomass samples in remote and highly inaccessible peatland forests