Utility of Satellite and Aerial Images for Quantification of Canopy Cover and Infilling Rates of the Invasive Woody Species Honey Mesquite (Prosopis Glandulosa) on Rangeland

Woody plant encroachment into grasslands and rangelands is a world-wide phenomenon but detailed descriptions of changes in geographical distribution and infilling rates have not been well documented at large land scales. Remote sensing with either aerial or satellite images may provide a rapid means for accomplishing this task. Our objective was to compare the accuracy and utility of two types of images with contrasting spatial resolutions (1-m aerial and 30-m satellite) for classifying woody and herbaceous canopy cover and determining woody infilling rates in a large area of rangeland (800 km<sup>2</sup>) in north Texas that has been invaded by honey mesquite (<em>Prosopis glandulosa</em>). Accuracy assessment revealed that the overall accuracies for the classification of four land cover types (mesquite, grass, bare ground and other) were 94 and 87% with kappa coefficients of 0.89 and 0.77 for the 1-m and 30-m images, respectively. Over the entire area, the 30-m image over-estimated mesquite canopy cover by 9 percentage units (10 <em>vs.</em> 19%) and underestimated grass canopy cover by the same amount when compared to the 1-m image. The 30-m resolution image typically overestimated mesquite canopy cover within 225 4-ha sub-cells that contained a range of mesquite covers (1–70%) when compared to the 1-m image classification and was not suitable for quantifying infilling rates of this native invasive species. Documenting woody and non-woody canopy cover on large land areas is important for developing integrated, regional-scale management strategies for rangeland and grassland regions that have been invaded by woody plants

Integrating remote sensing and geostatistics in mapping Seriphium plumosum (bankrupt bush) invasion.

Master of Science in of Geography. University of KwaZulu-Natal. Pietermaritzburg, 2016The impacts of plant species invasion in natural ecosystems have attracted geo-scientific studies globally. Several studies have demonstrated that the effects of invasive species can permanently alter an ecosystem structure and affect its provision of goods and services, e.g. the provision of food and fibre, aesthetics, recreation and tourism, and regulating the spread of diseases. Plant invasion causes transformation of ecosystems including replacement of native vegetation. This study focuses on invasive plant impacting on grasslands called Seriphium plumosum. The plant is known to have allelopathic effects, killing grass species and turning grazing lands into degraded shrublands. The major challenge in grassland management is the eradication and management of S. plumosum. Central to this challenge is locating, mapping and estimating the invasion status/cover over large areas. Remote sensing based earth observation approaches offer a viable method for invasion plants mapping. Moreover, mapping of vegetation requires robust statistical analysis to determine relationships between field and remotely sensed data. Such relationships can be achieved using spatial autocorrelation. In this study, Getis statistics transformed images and geostatistical techniques, which involve modelling the spatial autocorrelation of canopy variables have been used in mapping S. plumosum. Getis statistics was used to transform SPOT (Satellites Pour l’Observation de la Terre)-6 image bands into spatially dependent Getis indices layer variables for mapping S. plumosum. Stepwise multiple Regression, ordinary kriging and cokriging were used to evaluate the cross-correlated information between SPOT6-derived Getis indices transformed layer variables and field sampled S. plumosum canopy density and percentage. To select the best SPOT6-derived Getis indices to map S. plumosum, 308 spectral Getis indices transformed layer variables were statistically evaluated. Results indicated that Rook, Positive and Horizontal Getis indices are most suitable for mapping S. plumosum with 0.83, 0.828 and 0.828 importance. The most accurate Getis index obtained using 5x5 (Lag 5) moving window yielded 0.83 mapping importance. Cokriging with the most important Getis index yielded the best in S. plumosum density prediction with root mean square error (RMSE) of 25.8 compared to ordinary kriging with RMSE of 26.1 and regression with RMSE of 35.6. This study demonstrated that Getis statistics and geostatistics were successful in mapping and predicting S. plumosum. The current study provides insights critical for developing sound framework for planning and management of S. plumosum in agro-ecological systems

Monitoring the Sustainable Intensification of Arable Agriculture:the Potential Role of Earth Observation

Sustainable intensification (SI) has been proposed as a possible solution to the conflicting problems of meeting projected increases in food demand and preserving environmental quality. SI would provide necessary production increases while simultaneously reducing or eliminating environmental degradation, without taking land from competing demands. An important component of achieving these aims is the development of suitable methods for assessing the temporal variability of both the intensification and sustainability of agriculture. Current assessments rely on traditional data collection methods that produce data of limited spatial and temporal resolution. Earth Observation (EO) provides a readily accessible, long-term dataset with global coverage at various spatial and temporal resolutions. In this paper we demonstrate how EO could significantly contribute to SI assessments, providing opportunities to quantify agricultural intensity and environmental sustainability. We review an extensive body of research on EO-based methods to assess multiple indicators of both agricultural intensity and environmental sustainability. To date these techniques have not been combined to assess SI; here we identify the opportunities and initial steps required to achieve this. In this context, we propose the development of a set of essential sustainable intensification variables (ESIVs) that could be derived from EO data

Remote Sensing of Grassland Variables Across Seasons and Using Multiple Spectral Devices

The regeneration and conservation of semi-natural grasslands is considered important to land managers such as Natural England, especially grasslands protected by legislation such as UK Biodiversity Action Plan (BAP) priority habitats or Sites of Special Scientific Interest (SSSI). Monitoring the condition of these grasslands is necessary, but conventional methods of measuring grassland condition are time consuming and limited in their spatial coverage. This thesis tested the hypothesis that remote sensing (RS) techniques can provide a cost- and time-effective solution to grassland condition monitoring. This thesis used partial least squares regression (PLSR) to explore the relationship between grassland spectral reflectance and the mass or % cover of a range of condition-related grassland variables plus a metric (an average and equally weighted measure of whether the CSM criteria were sufficiently met referred to as CSMcondition) representing condition as defined in the UK by the Common Standards Monitoring. The relationship between grassland variables and CSM-condition was also assessed. Each study differed with the grasslands targeted, the seasons when data were collected and the devices deployed. The first study was conducted on a range of different grassland types, the second study was conducted on chalk grasslands of differing levels of improvement across three seasons (spring, summer and autumn) and the third study was conducted on these same chalk grasslands but using data from three different spectral devices collected during the summer. All three studies were conducted at patch level (1m2) with the third study including the extrapolated predictions from trained statistical models to field level (200x1m) using spectral data from a CROPSCAN MSR 16R hand-held device. All three studies used spectral data from a CROPSCAN MSR 16R hand-held device and the third study included the analysis of spectral data from a Spectral Vista Corporation (SVC) HR1024i hand-held device and a Rikola camera mounted on an uncrewed aerial vehicle (UAV). The results suggest that some of the condition-related variables considered in this thesis are predicted with reasonable accuracy and precision at patch level, but producing reliable results requires a sufficient quantity of data to train the statistical models (at least 30 quadrats of samples in the context of this thesis) especially if the results are to be extrapolated to field level as additional data are required for the external validation of the results. When analysing data collected at patch level during the summer; the mass of bryophytes, dead material and graminoids plus the % cover of forbs can be predicted to a moderate level of accuracy when analysing data from all seven grasslands. When analysing data from all Parsonage Down NNR grasslands; the mass of bryophytes, the % cover of live material, % cover-based live:dead ratio and CSM-condition could be predicted to a high level of accuracy. Moisture content plus the % cover of dead material, forbs and gram:forb ratio were all predicted to a moderate level of accuracy as well as CSM-condition predicted by grassland variable values. When using data from all Ingleborough NNR grasslands; the % cover of forbs and biomass plus the mass of bryophytes, dead material and live material could be predicted to a moderate level of accuracy. When using patch level data collected across three seasons; the % cover of dead material, live material and live:dead ratio plus the mass of graminoids could be predicted when using three seasons of data collected on one grassland, or for all three Parsonage grasslands, to at least a moderate level of accuracy although some models trained with % cover data had a high accuracy. Forbs (mass and % cover) plus the mass of gram:forb ratio, live material and live:dead ratio could be predicted to at least a moderate level of accuracy for some grasslands. When using data from all grasslands collected in one season to predict grassland variables; the mass of a range of grassland variables could be predicted to a moderate level of accuracy for the spring and autumn months but not when using % cover data. When the use of data from three different spectral devices were compared to see which produced the most accurate models; using CROPSCAN and SVC data produced similar results, with slightly stronger results from the CROPSCAN, but using data from the Rikola camera produced weaker results. When the results of trained PLSR models were extrapolated to field level, the projected predicted grassland variable values from models trained with CROPSCAN MSR 16R data looked promising but the results have not been externally validated using a separate data set. Variable importance in projection (VIP) was used to establish which spectral bands are most important for predicting each grassland variable plus CSM-condition and which grassland variables are most important in predicting CSM-condition. It was generally found that the upper parts of the spectral range of each device (NIR and SWIR) were the most crucial for predicting grassland variables, with the red edge (647nm) and particular visible bands (470nm) also having some importance. When grassland variables were used to predict CSM-condition, which variables were most important depended on whether the grassland variable was mass-based or % cover-based. When using mass data; graminoid:forb ratio mass and live:dead ratio masswere consistently important across grasslands and seasons with biomass, graminoid:bryophyte mass and moisture content having importance for particular grasslands and seasons. When using % cover data; forbs cover, graminoids cover and live:dead ratio cover were consistently important across grasslands and seasons with dead material cover and live material cover having importance for particular grasslands and seasons. This thesis also explored which grassland variables could be predicted most consistently by calculating coefficient of variance (CV) on data collected across grasslands, seasons and/or using different spectral devices. Overall, these results suggest that none of the grassland variables considered in this thesis can be consistently predicted strongly across all the different grasslands or seasons considered in this thesis. When using % cover variable data; forbs cover and live:dead ratio cover produced relatively consistent results across grasslands, seasons and when using data from different spectral devices while bryophytes cover, graminoids cover and gram:forb ratio cover were consistent under some specific circumstances. When using mass data; moisture content stands out as relatively consistent compared to other variables across grasslands, seasons and when using different spectral devices. When using CROPSCAN MSR 16R spectral data as predictors, live material mass and live:dead ratio mass plus biomass produced relatively consistent results. Dead material mass produced relatively consistent results when using different devices as predictors, but not when using data collected over different season

Sustainable intensification of arable agriculture:The role of Earth Observation in quantifying the agricultural landscape

By 2050, global food production must increase by 70% to meet the demands of a growing population with shifting food consumption patterns. Sustainable intensification has been suggested as a possible mechanism to meet this demand without significant detrimental impact to the environment. Appropriate monitoring techniques are required to ensure that attempts to sustainably intensify arable agriculture are successful. Current assessments rely on datasets with limited spatial and temporal resolution and coverage such as field data and farm surveys. Earth Observation (EO) data overcome limitations of resolution and coverage, and have the potential to make a significant contribution to sustainable intensification assessments. Despite the variety of established EO-based methods to assess multiple indicators of agricultural intensity (e.g. yield) and environmental quality (e.g. vegetation and ecosystem health), to date no one has attempted to combine these methods to provide an assessment of sustainable intensification. The aim of this thesis, therefore, is to demonstrate the feasibility of using EO to assess the sustainability of agricultural intensification. This is achieved by constructing two novel EO-based indicators of agricultural intensity and environmental quality, namely wheat yield and farmland bird richness. By combining these indicators, a novel performance feature space is created that can be used to assess the relative performance of arable areas. This thesis demonstrates that integrating EO data with in situ data allows assessments of agricultural performance to be made across broad spatial scales unobtainable with field data alone. This feature space can provide an assessment of the relative performance of individual arable areas, providing valuable information to identify best management practices in different areas and inform future management and policy decisions. The demonstration of this agricultural performance assessment method represents an important first step in the creation of an operational EO-based monitoring system to assess sustainable intensification, ensuring we are able to meet future food demands in an environmentally sustainable way