Mapping landscape function with hyperspectral remote sensing of natural grasslands on gold mines

Thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy. School of Animal, Plant and Environmental Science, University of the Witwatersrand, Johannesburg, South Africa. October 2016.Mining has negative impacts on the environment in many different ways. One method developed to quantify some of these impacts is Landscape Function Analysis (LFA) and this has been accepted by some mining companies and regulators. In brief, LFA aims at quantifying the organization of vegetative and landscape components in a landscape into patches along a transect and quantifying, in a relative manner, three basic processes important to landscape functioning, namely: soil stability or susceptibility to erosion, infiltration or runoff, and nutrient cycling or organic matter decomposition. However, LFA is limited in large heterogeneous environments, such as those around mining operations, due to its localized nature, and the man hours required to collect a representative set of measurements for such large and complex environments. Remote sensing using satellite-acquired data can overcome these limitations by sampling the entire environment in a rapid and objective manner. What is required is a method of connecting these satellite-based measurements to LFA measurements and then being able to extrapolate these measurements across the entire mine surface. The aim of this research was to develop a method to use satellite-based hyperspectral imagery to predict landscape function analysis (LFA) using partial least squares regression (PLSR). This was broken down into three objectives: (1) Collection of the LFA data in the field and validation of the LFA indices against other environmental variables collected at the same time, (2) validation of PLSR models predicting LFA indices and various environmental variables from ground-based spectra, and (3) production of risk maps based on predicting LFA indices and above-ground biomass using PLSR models and Hyperion satellite-based hyperspectral imagery. Although the study was based in grasslands at two mining regions, West Wits and Vaal River, a suitable Hyperion image was only available for Vaal River. A minimum of 374 points were sampled for LFA indices, ground-based spectra, above-ground biomass and soil cores along 2880 m of LFA transect from both mine sites. Soil cores were weighed fresh before sieving with a 2 mm sieve to separate root and stone fractions. The sieved soil fraction was tested for pH, EC, SOM, and for the West Wits samples, organic nitrogen and total extractable inorganic nitrogen. There was one modification to the LFA method where grass patches were collapsed into homogenous units as it was deemed not feasible to sample 180 m transects at grass tuft scales of 10 – 30 cm, but other patch definitions followed the LFA manual (Tongway and Hindley, 2004). Evidence suggested that some of the different patch types, in particular the bare/biological soil crust – bare grass – sparse grass patch types, represented successional stages in a continuum although this was not conclusive. There also was evidence that the presence or absence of cattle play a role in some processes active in these grasslands and erosion is mainly through deflation, rain splash and sheet wash. Generally the environmental variables supported the LFA indices although the nutrient cycling index was representative of above-ground nutrient cycling but not below-ground nutrient cycling. Models derived with PLSR to predict the LFA indices from ground-based spectral measurements were strong at both mine sites (West Wits: LFA stability r2 = 0.63, P < 0.0001; LFA infiltration r2 = 0.75, P < 0.0001; LFA nutrient cycling r2 = 0.73, P < 0.0001; Vaal River: LFA stability r2 = 0.39, P < 0.0001, LFA infiltration r2 = 0.72, P < 0.0001, LFA nutrient cycling r2 = 0.54, P < 0.0001), as were PLSR models predicting above-ground biomass (West Wits above-ground biomass r2 = 0.55, P = 0.0003; Vaal River above-ground biomass r2 = 0.79, P < 0.0001) and soil moisture (West Wits soil moisture r2 = 0.45, P = 0.0017; Vaal River soil moisture r2 = 0.68, P < 0.0001). However, for soil organic matter (r2 = 0.50, P < 0.0001) and EC (r2 = 0.63, P < 0.0001), Vaal River had strong prediction models while West Wits had weak models for these variables (r2 = 0.31, P = 0.019 and r2 = 0.10 and P < 0.18, respectively). For EC, the wide range of soil values at Vaal River in association with gypsum crusts, and low values throughout West Wits explained these model results but for soil organic matter, no clear explanation for these site differences was identified. Patch-based models could accurately discriminate between spectrally well-defined patch types such S. plumosum patches but were less successful with patch types that were spectrally similar such as the bare/biological soil crust – bare grass – sparse grass patch continuum. Clustering similar patch types together before PLSR modelling did improve these patch-based spectral models. To test the method proposed to predict LFA indices from satellite-based hyperspectral imagery, a Hyperion image matching 6 transects at Vaal River was acquired by NASA’s EO-1 satellite and downloaded from the USGS Glovis website. LFA transects were partitioned to match and extract pixel spectra from the Hyperion data cube. Thirty-one spectra were separated into calibration (20) and validation (11) data. PLSR models were derived from the calibration data, tested with validation data to select the optimum model, and then applied to the entire Hyperion data cube to produce prediction maps for five LFA indices and above-ground biomass. The patch area index (PAI) produced particularly strong models (r2 = 0.79, P = 0.0003, n =11) with validation data, whereas the landscape organization index (LOI) produced weak models. It is argued that this difference between these two essentially similar indices is related to the fact that the PAI is a 2-dimensional index and the LOI is a 1-dimensional index. This difference in these two indices allowed the PAI to compensate for some burned pixels on the transects by “seeing” the density pattern of grass tufts and patches whereas the linear nature of the LOI was more susceptible to the changing dimensions of patch structure due to the effects of fire. Although validation models for the three LFA indices of soil stability, infiltration and nutrient cycling were strong (r2 = 0.72, P = 0.004; r2 = 0.66, P = 0.008; r2 = 0.70, P = 0.005, n = 9 respectively), prediction maps were confounded by the presence of fire on some transects. The poor quality of the Hyperion imagery also meant great care had to be taken in the selection of models to avoid poor quality prediction maps. The 31 bands from the VNIR (478 – 885 nm) portion of the Hyperion spectra were generally the best for PLSR modelling and prediction maps, presumably because of better signal-to-noise ratios due to higher energy in the shorter wavelengths. With two satellite-based hyperspectral sensors already operational, namely the US Hyperion and the Chinese HJ-1A HSI, and a number expected to be launched by various space agencies in the next few years, this research presents a method to use the strengths of LFA and hyperspectral imagery to model and predict LFA index values and thereby produce risk maps of large, heterogeneous landscapes such as mining environments. As this research documents a method of partitioning the landscape rather than the pixel spectra into pure endmembers, it makes a valuable contribution to the fields of landscape ecology and hyperspectral remote sensing.LG201

Forage supply of West African rangelands : Towards a better understanding of ecosystem services by application of hyperspectral remote sensing

Grazing is the predominant type of land use in savanna regions all over the world. Although large savanna areas in Africa are still grazed by wild herbivores, the West African Sudanian savanna region mainly comprises rangeland ecosystems, providing the important ecosystem service of forage supply for domestic livestock. However, these dryland rangelands are threatened by global change, including a predicted in-crease in climatic aridity and variability as well as land degradation caused by overgrazing. In this context, the international research project WASCAL (West African Science Service Centre on Climate Change and Adapted Land Use) was initiated to investigate the effects of climatic change in this region and to develop effective adaptation and mitigation measures. This cumulative dissertation aims at providing a methodology for a regular knowledge-driven monitoring of forage resources in West Africa. Due to the vast and remote nature of Sudanian savannas, remote sensing technologies are required to achieve this goal. Hence, as a first step, it was necessary to test whether hyperspectral near-surface remote sensing offers the means to model and estimate the two most important aspects of forage supply, i.e. forage quantity (green biomass) and quality (metabolisable energy) (Chapter 2.1). Evidence was provided that partial least squares regression was able to generate robust and transferable forage models. In a second step, direct and indirect drivers of forage supply on the plot and site level were identified by using path modelling within the well-defined concept of social-ecological systems (Chapter 2.2). Results indicate that the provisioning ecosystem service of forage supply is mainly driven by land use, while climatic aridity exerts foremost indirect control by determining the way people use their environment. Building on these findings, upscaling of models was tested to generate maps of forage quality and quantity from satellite images (Chapter 2.3). Here, two different available data sources, i.e. multi- and hyperspectral satellites, were compared to serve the overall objective to install a regular forage monitoring system. In conclusion, preliminary forage maps could be created from both systems. An independent validation would be a research desiderate for future studies. Moreover, both systems feature certain shortcomings that might only be overcome by future satellite missions

Prediction of topsoil organic carbon using airborne and satellite hyperspectral imagery

The Airborne Hyperspectral Scanner (AHS) and the Hyperion satellite hyperspectral sensors were evaluated for their ability to predict topsoil organic carbon (C) in burned mountain areas of northwestern Spain slightly covered by heather vegetation. Predictive models that estimated total organic C (TOC) and oxidizable organic C (OC) content were calibrated using two datasets: a ground observation dataset with 39 topsoil samples collected in the field (for models built using AHS data), and a dataset with 200 TOC/OC observations predicted by AHS (for models built using Hyperion data). For both datasets, the prediction was performed by stepwise multiple linear regression (SMLR) using reflectances and spectral indices (SI) obtained from the images, and by the widely-used partial least squares regression (PLSR) method. SMLR provided a performance comparable to or even better than PLSR, while using a lower number of channels. SMLR models for the AHS were based on a maximum of eight indices, and showed a coefficient of determination in the leave-one-out cross-validation R2 = 0.60–0.62, while models for the Hyperion sensor showed R2 = 0.49–0.61, using a maximum of 20 indices. Although slightly worse models were obtained for the Hyperion sensor, which was attributed to its lower signal-to-noise ratio (SNR), the prediction of TOC/OC was consistent across both sensors. The relevant wavelengths for TOC/OC predictions were the red region of the spectrum (600–700 nm), and the short wave infrared region between ~2000–2250 nm. The use of SMLR and spectral indices based on reference channels at ~1000 nm was suitable to quantify topsoil C, and provided an alternative to the more complex PLSR method

Identify the opportunities provided by developments in earth observation and remote sensing for national scale monitoring of soil quality

Defra wish to establish to what extent national-scale soil monitoring (both state and change) of a series of soil indicators might be undertaken by the application of remote sensing methods. Current soil monitoring activities rely on the field-based collection and laboratory analysis of soil samples from across the landscape according to different sampling designs. The use of remote sensing offers the potential to encompass a larger proportion of the landscape, but the signal detected by the remote sensor has to be converted into a meaningful soil measurement which may have considerable uncertainty associated with it. The eleven soil indicators which were considered in this report are pH, organic carbon, bulk density, phosphorus (Olsen P), nitrogen (total N), magnesium (extractable), potassium (extractable), copper (aqua regia extractable), cadmium (aqua regia extractable), zinc (aqua regia extractable) and nickel (aqua regia extractable). However, we also comment on the potential use of remote sensing for monitoring of soil depth and (in particular) peat depth, plus soil erosion and compaction. In assessing the potential of remote sensing methods for soil monitoring of state and change, we addressed the following questions: 1. When will these be ready for use and what level of further development is required? 2. Could remote sensing of any of these indicators replace and/or complement traditional field based national scale soil monitoring? 3. Can meaningful measures of change be derived? 4. How could remote soil monitoring of individual indicators be incorporated into national scale soil monitoring schemes? To address these questions, we undertook a comprehensive literature and internet search and also wrote to a range of international experts in remote sensing. It is important to note that the monitoring of the status of soil indicators, and the monitoring of their change, are two quite different challenges; they are different variables and their variability is likely to differ. There are particular challenges to the application of remote sensing of soil in northern temperate regions (such as England and Wales), including the presence of year-round vegetation cover which means that soil spectral reflectance cannot be captured by airborne or satellite observations, and long-periods of cloud cover which limits the application of satellite-based spectroscopy. We summarise the potential for each of the indicators, grouped where appropriate. Unless otherwise stated, the remote sensing methods would need to be combined with ground-based sampling and analysis to make a contribution to detection of state or change in soil indicators. Soil metals (copper (Cu), cadmium (Cd), zinc (Zn), nickel (Ni)): there is no technical basis for applying current remote sensing approaches to monitor either state or change of these indicators and there are no published studies which have shown how this might be achieved. Soil nutrients: the most promising remote sensing technique to improve estimates of the status of extractable potassium (K) is the collection and application of airborne radiometric survey (detection of gamma radiation by low-flying aircraft) but this should be investigated further. This is unlikely to assist in monitoring change. Based on published literature, it may be possible to enhance mapping the state of extractable magnesium (Mg), but not to monitor change, using hyperspectral (satellite or airborne) remote sensing in cultivated areas. This needs to be investigated further. There are no current remote sensing methods for detecting state or change of Olsen (extractable) phosphorus (P). Organic carbon and total nitrogen: Based on published literature, it may be possible to enhance mapping the state of organic carbon and total nitrogen (but not to monitor change), using hyperspectral (satellite or airborne) remote sensing in cultivated areas only. In applying this approach the satellite data are applied using a statistical model which is trained using ground-based sampling and analysis of soil

The earth observing one (EO-1) Hyperion and Advanced land imager sensors for use in tundra classification studies within the Upper Kuparuk river basin, Alaska

The heterogeneity of Arctic vegetation can make land cover classification very difficult when using medium to small resolution imagery (Schneider et al., 2009; Muller et al., 1999). Using high radiometric and spatial resolution imagery, such as the SPOT 5 and IKONOS satellites, have helped arctic land cover classification accuracies rise into the 80 and 90 percentiles (Allard, 2003; Stine et al., 2010; Muller et al., 1999). However, those increases usually come at a high price. High resolution imagery is very expensive and can often add tens of thousands of dollars onto the cost of the research. The EO-1 satellite launched in 2002 carries two sensors that have high spectral and/or high spatial resolutions and can be an acceptable compromise between the resolution versus cost issues. The Hyperion is a hyperspectral sensor with the capability of collecting 242 spectral bands of information. The Advanced Land Imager (ALI) is an advanced multispectral sensor whose spatial resolution can be sharpened to 10 meters. This dissertation compares the accuracies of arctic land cover classifications produced by the Hyperion and ALI sensors to the classification accuracies produced by the Systeme Pour l' Observation de le Terre (SPOT), the Landsat Thematic Mapper (TM) and the Landsat Enhanced Thematic Mapper Plus (ETM+) sensors. Hyperion and ALI images from August 2004 were collected over the Upper Kuparuk River Basin, Alaska. Image processing included the stepwise discriminant analysis of pixels that were positively classified from coinciding ground control points, geometric and radiometric correction, and principle component analysis. Finally, stratified random sampling was used to perform accuracy assessments on satellite derived land cover classifications. Accuracy was estimated from an error matrix (confusion matrix) that provided the overall, producer's and user's accuracies. This research found that while the Hyperion sensor produced classification accuracies that were equivalent to the TM and ETM+ sensor (approximately 78%), the Hyperion could not obtain the accuracy of the SPOT 5 HRV sensor. However, the land cover classifications derived from the ALI sensor exceeded most classification accuracies derived from the TM and ETM+ sensors and were even comparable to most SPOT 5 HRV classifications (87%). With the deactivation of the Landsat series satellites, the monitoring of remote locations such as in the Arctic on an uninterrupted basis throughout the world is in jeopardy. The utilization of the Hyperion and ALI sensors are a way to keep that endeavor operational. By keeping the ALI sensor active at all times, uninterrupted observation of the entire Earth can be accomplished. Keeping the Hyperion sensor as a "tasked" sensor can provide scientists with additional imagery and options for their studies without overburdening storage issues

EVALUATION OF A REMOTE SENSING BASED METHOD FOR THE ASSESSMENT OF AGRICULTURAL CROP RESIDUES ON THE SOIL SURFACE

Increased agricultural mechanization in the recent past and susceptibility of certain soils to degradation generate widespread concern among experts on the overall environmental sustainability of some of the current agricultural practices in Europe. A number of solutions could be adopted to better preserve soil resources, some of which are already supported by the Common Agricultural Policy (CAP). Researchers demonstrated that erosion and reduction in soil organic matter are among the most acute degradation issues in Europe and that the release of crop residues on the soil surface after harvesting can greatly reduce their incidence. The use of a permanent soil cover (e.g. by use of crop residues) is one of the three fundamental principles of Conservation Agriculture. Quantifying the amount of crop residues on the ground is important for soil and water protection, modelling of erosion processes and legislation enforcement purposes. However, common monitoring methods based on ground sampling are expensive and likely to be impracticable on vast surfaces. Remote sensing can offer a valid alternative for monitoring. The present research intends to contribute to the efforts towards the establishments of methods for the assessment and monitoring, through remote sensing, of the effects of conservation agriculture practices on the environment, with focus on soil resources. In this respect, the research specific objective is the evaluation of a remote sensing based method for the quantification of crop residue cover in a conservation agriculture farm in Northern Italy by use of hyperspectral satellite imagery. Results achieved show that not only crop residues percent cover is linearly related to certain remote sensing-based indices, therefore making possible to estimate how well soil is preserved from weathering, but also that spaceborne hyperspectral sensors such as Hyperion appear to have great potentiality towards monitoring of other environmental targets due to their very high spectral and spatial resolution. The research was deeply inspired by the outcomes of a European project (\u201cSustainable Agriculture and Soil Conservation through simplified cultivation techniques\u201d - SoCo) aimed at improving protection of soil resources in the European agriculture sector through a stock taking and promotion of soil-friendly agriculture practices and systems, in particular simplified cultivation techniques, within the current legislative framework

Coastal and Inland Aquatic Data Products for the Hyperspectral Infrared Imager (HyspIRI)

The HyspIRI Aquatic Studies Group (HASG) has developed a conceptual list of data products for the HyspIRI mission to support aquatic remote sensing of coastal and inland waters. These data products were based on mission capabilities, characteristics, and expected performance. The topic of coastal and inland water remote sensing is very broad. Thus, this report focuses on aquatic data products to keep the scope of this document manageable. The HyspIRI mission requirements already include the global production of surface reflectance and temperature. Atmospheric correction and surface temperature algorithms, which are critical to aquatic remote sensing, are covered in other mission documents. Hence, these algorithms and their products were not evaluated in this report. In addition, terrestrial products (e.g., land use land cover, dune vegetation, and beach replenishment) were not considered. It is recognized that coastal studies are inherently interdisciplinary across aquatic and terrestrial disciplines. However, products supporting the latter are expected to already be evaluated by other components of the mission. The coastal and inland water data products that were identified by the HASG, covered six major environmental and ecological areas for scientific research and applications: wetlands, shoreline processes, the water surface, the water column, bathymetry and benthic cover types. Accordingly, each candidate product was evaluated for feasibility based on the HyspIRI mission characteristics and whether it was unique and relevant to the HyspIRI science objectives

Digital mapping of soil properties using multivariate statistical analysis and ASTER data in an Arid Region

Modeling and mapping of soil properties has been identified as key for effective land degradation management and mitigation. The ability to model and map soil properties at sufficient accuracy for a large agriculture area is demonstrated using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. Soil samples were collected in the El-Tina Plain, Sinai, Egypt, concurrently with the acquisition of ASTER imagery, and measured for soil electrical conductivity (EC_e), clay content and soil organic matter (OM). An ASTER image covering the study area was preprocessed, and two predictive models, multivariate adaptive regression splines (MARS) and the partial least squares regression (PLSR), were constructed based on the ASTER spectra. For all three soil properties, the results of MARS models were better than those of the respective PLSR models, with cross-validation estimated R^2 of 0.85 and 0.80 for EC_e, 0.94 and 0.90 for clay content and 0.79 and 0.73 for OM. Independent validation of EC_e, clay content and OM maps with 32 soil samples showed the better performance of the MARS models, with R^2 = 0.81, 0.89 and 0.73, respectively, compared to R^2 = 0.78, 0.87 and 0.71 for the PLSR models. The results indicated that MARS is a more suitable and superior modeling technique than PLSR for the estimation and mapping of soil salinity (EC_e), clay content and OM. The method developed in this paper was found to be reliable and accurate for digital soil mapping in arid and semi-arid environments

Estimating Soil Organic Carbon in Cultivated Soils Using Soil Test Data, Remote Sensing Imagery from Satellites (Landsat 8 and PlantScope), and Web Soil Survey Data

Soil organic carbon (SOC) is an important soil parameter of cultivated soils that needs to be monitored and mapped regularly to enhance soil health and productivity. SOC levels in cultivated areas is difficult to monitor for farmers and is costly to analyze using traditional methods. The objective of this study was to estimate surface SOC distribution in selected soils of Major Land Resource Areas (MLRA) 102A (Rolling Till Plain, Brookings County, SD) and 103 (Central Iowa and Minnesota Till Prairies, Lac qui Parle County, MN) using soil sample data, Web Soil Survey (WSS) data, and satellite imagery (Landsat 8 and PlanetScope). Different satellite imagery bands and band combinations were used to reach more accurate results. The dominant soils in the area are Haplustolls, Calciustolls, and Endoaquolls formed in silty sediments, local silty alluvium, and till. Sites were selected and soil samples were collected in May 2018 after planting. SOC and soil properties were measured at the 0-15 cm depth. SOC was mainly affected by soil texture in the studied selected soils. Multiple-linear regression was used to build SOC prediction models from soil test data. The final SOC model (using stepwise regression) is SOCp = 3.98 + (-0.210 pH) + (-0.220 Sand [g kg-1]) + (0.040 Sum of Extractable Cation, SOEC [cmolc kg-1]). The Ridge Regression (RR) (CV = 0.066, MSE = 0.063) and Principal Component Regression (PCR) (CV = 0.071, MSE = 0.068) were used to deal with multicollinearity and RR was determined to be as the best model, with 82.7% of variation in SOC explained by the RR model. Landsat 8 and PlanetScope spectral bands and different indices were also used to develop SOC prediction models. The stepwise regression analyses revealed that the Landsat 8 prediction model had multicollinearity problem. Ridge regression and PCR were applied, and RR was chosen as the best model with SOCp = -26.7 + (0.310 BSIL) + (-23.2 Band 5L) + (75.8 Band 2L) + (-51.1 Band 3L) + ( -3.05 Band 7L). The RR model (CV = 0.24, MSE = 0.22) explained 37.0% of the variation in SOC for Landsat 8. The reduced PlanetScope model was SOCp = -25.1 + (2980 Band4P) + (0.327 BSIP). Approximately 60.0% of the variation in SOC was explanined by the Ordinary Least Square (OLS) (CV = 0.15, MSE = 0.14) model and was free of multicollinearity. WSS data showed similar patterns as soil test data for SOC predictions. The best model for WSS data was a linear regression, SOCp = 3.37 + (-0.0200 Sand WSS [g kg-1]) and 49.0% of the variation in SOC was explained by this model. WSS data were then added as variables into the spatial (satellite) estimation models. The Landsat 8 and WSS data explained 53.3%, PlanetScope and WSS data explained 68.8% of the SOC variation. Based on these results, deciding on the number of soil sampling points, and the use of specific variables in the model is very crucial for the model development. Estimating SOC by minimizing the number of needed soil sampling points, using satellite imagery, and public free sources provides an easy, efficient and cost-effective way to monitor SOC levels and identify the best management systems for producers and natural resource managers. This project produced accurate SOC prediction models using soil test data, satellite imagery and Web Soil Survey data. This SOC estimation model helps farmers, resource managers, and researchers to monitor SOC concentration on the soil surface using remote sensing alone, or with WSS data, or with a minimal amount of soil test data