Mapping grass nutrient phosphorus (P) and sodium (NA) across different grass communities using Sentinel-2 data

A research report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in partial fulfillment of the requirement for the degree of Master of Science (Environmental Sciences) at the School of Geography, Archaeology & Environmental Studies March 2017Accurate estimates and mapping of grass quality is important for effective rangeland management. The purpose of this research was to map different grass species as well as nutrient Phosphorus (P) and Sodium (Na) concentration across grass communities using Sentinel-2 imagery in Telperion game reserve. The main objectives of the study were to: map the most common grass communities at the Telperion game reserve using Sentinel-2 imagery using artificial neural network (ANN) classifier and to evaluate the use of Sentinel-2 (MSI) in quantifying grass phosphorus and sodium concentration across different grass communities. Grass phosphorus and sodium concentrations were estimated using Random Forest (RF) regression algorithm, normalized difference vegetation index (NDVI) and the simple ratios (SR) which were calculated from all two possible band combination of Sentinel-2 data. Results obtained demonstrated woody vegetation as the dominant vegetation and Aristida congesta as the most common grass species. The overall classification accuracy = 81%; kappa =0.78 and error rate=0.18 was achieved using the ANN classifier. Regression model for leaf phosphorus concentration prediction both NDVI and SR data sets yielded similar results (R2 =0.363; RMSE=0.017%) and (R2 =0.36 2; RMSE=0.0174%). Regression model for leaf sodium using NDVI and SR data sets yielded dissimilar results (R2 =0.23; RMSE=16.74 mg/kg) and (R2 =0.15; RMSE =34.08 mg/kg). The overall outcomes of this study demonstrate the capability of Sentinel 2 imagery in mapping vegetation quality (phosphorus and sodium) and quantity. The study recommends the mapping of grass communities and both phosphorus and sodium concentrations across different seasons to fully understand the distribution of different species across the game reserve as well as variations in foliar concentration of the elements. Such information will guide the reserve managers on resource use and conservation strategies to implement within the reserve. Furthermore, the information will enable conservation managers to understand wildlife distribution and feeding patterns. This will allow integration of effective conservation strategies into decisions on stocking capacity.MT 201

Potential of Sentinel-2 spectral configuration to assess rangeland quality

Sentinel-2 is intended to improve vegetation assessment at local to global scales. Today, estimation of leaf nitrogen (N) as an indicator of rangeland quality is possible using hyperspectral systems. However, few studies based on commercial imageries have shown a potential of the red-edge band to accurately predict leaf N at the broad landscape scale. We intend to investigate the utility of Sentinel-2 for estimating leaf N concentration in the African savanna. Grass canopy reflectance was measured using the analytical spectral device (ASD) in concert with leaf sample collections for leaf N chemical analysis. ASD reflectance data were resampled to the spectral bands of Sentinel-2 using published spectral response functions. Random forest (RF), partial least square regression (PLSR), and stepwise multiple linear regression (SMLR) were used to predict leaf N using all 13 bands. Using leave-one-out cross validation, the RF model explained 90% of leaf N variation, with a root mean square error of 0.04 (6% of the mean), which is higher than that of PLSR and SMLR. Using RF, spectral bands centered at 705 nm (red edge) and two shortwave infrared bands centered at 2190 and 1610 nm were found to be the most important bands in predicting leaf N.The Council for Scientific and Industrial Research, Department of Science and Technology (DST), National Research Foundation’s Thuthuka program, University of Twente’s Faculty of Geo-information Science and Earth Observation (UTITC), and Wageningen University.http://www.sherpa.ac.uk/romeo/issn/1931-3195/am2016Geography, Geoinformatics and Meteorolog

Remote Sensing of Pasture Quality

Worldwide, farming systems are undergoing significant changes due to economic, environmental and social drivers. Agribusinesses must increasingly deliver products specified in terms of safety, health and quality. Increasing constraints are being placed on them by the market, the community and by government to achieve a financial benefit within social and environmental limits (Dynes et al. 2003). In order to meet these goals, producers must know the quantity and quality of the inputs into their feeding systems, be able to reliably predict the products and by-products being generated, and have the skills to be able to manage their business accordingly. Easy access to accurate and objective evaluation of forage is the first key component to meeting these objectives in livestock systems (Dynes et al. 2003) and remote sensing has considerable potential to be informative and cost-effective (Pullanagari et al. 2012b)

Testing the use of the new generation multispectral data in mapping vegetation communities of Ezemvelo Game Reserve

A research report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science (Geographical Information Systems and Remote Sensing) at the School of Geography, Archaeology & Environmental Studies) Johannesburg. 2017Vegetation mapping using remote sensing is a key concern in environmental application using remote sensing. The new high resolution generation has made possible, the mapping of spatial distribution of vegetation communities. The aim of this research is to test the use of new generation multispectral data for vegetation classification in Ezemvelo Game Reserve, Bronkhorspruit. Sentinel-2 and RapidEye images were used covering the study area with nine vegetation classes: eight from grassland (Mixed grassland, Wetland grass, Aristida congesta, Cynadon dactylon, Eragrostis gummiflua, Eragrostis Chloromelas, Hyparrhenia hirta, Serephium plumosum) and one from woodland (Woody vegetation). The images were pre-processed, geo-referenced and classified in order to map detailed vegetation classes of the study area. Random Forest and Support Vector Machines supervised classification methods were applied to both images to identify nine vegetation classes. The softwares used for this study were ENVI, EnMAP, ArcGIS and R statistical packages (R Development Core, 2012) .These were used for Support Vector Machines and Random Forest parameters optimization. Error matrix was created using the same reference points for Sentinel-2 and RapidEye classification. After classification, results were compared to find the best approach to create a current map for vegetation communities. Sentinel-2 achieved higher accuracies using RF with overall accuracy of 86% and Kappa value of 0.84. Sentinel-2 also achieved overall accuracy of 85% with a Kappa value of 0.83 using SVM. RapidEye achieved lower accuracies using RF with an overall accuracy of 82% and Kappa value of 0.79. RapidEye using SVM produced overall accuracy of 81% and a Kappa value of 0.79. The study concludes that Sentinel-2 multispectral data and RF have the potential to map vegetation communities. The higher accuracies achieved in the study can assist management and decision makers on assessing the current vegetation status and for future references on Ezemvelo Game Reserve. Keywords Random forest, Support Vector Machines, Sentinel-2, RapidEye, remote sensing, multispectral, hyperspectral and vegetation communitiesLG201

Remote sensing of wetland tree species in the iSimangaliso Wetland Park, KwaZulu-Natal, South Africa.

Doctor of Science in Geography.The impact of global change is expected to result in changes in the distribution and composition of species. Coastal swamp and mangrove forests are some of the most threatened forest types in the world. Remote sensing is a suitable tool for monitoring species distribution and varying condition because of its spatial extent and repeatability. The ability of remote sensing to separate between species can be attributed primarily to its capability to quantify the absorption features in the electromagnetic spectrum which relate to plant biochemical and biophysical properties such as pigments, nutrients (proteins and starch), leaf water content, leaf angle distribution, leaf area index and foliage biomass. For some species, these phenological variations are extreme, as in the case of deciduous tree species, thus enhancing the ability to differentiate between species, whereas others are less pronounced, such as with evergreen tree species, making spectral distinction between species much more challenging. Few studies have assessed the pigment and nutrient phenology of evergreen tree species in subtropical forested wetlands, let alone their spectral differences. This study assesses whether multi-season data across a number of phenological phases of evergreen wetland tree species will improve their classification accuracy when compared to a single season and single phenological event. The objectives were to (i) assess whether tree species had unique seasonal profiles of foliar biochemicals; (ii) ascertain the spectral bands of plant properties which remain important across phenological phases for species classification; (iii) determine whether leaf reflectance spectra from multiple seasons would improve species classification when compared to a single season; and (iv) whether multi-season imagery would improve species discrimination when compared to a single season. Thus, the study made use of leaf level and canopy level spectra collected using a handheld spectrometer and spaceborne RapidEye imagery, respectively. Six dominant evergreen tree species from forested wetlands in the subtropical region of KwaZulu-Natal, South Africa, were sampled across four seasons (winter, spring, summer and autumn). Differences in foliar biochemical concentration were assessed for two pigments, including carotenoids and chlorophylls, as well as two nutrients, nitrogen and phosphorous. The results showed that the majority of species had no significant changes in foliar pigments across the four seasons. Foliar nitrogen showed a significantly higher variability in the spring, summer and autumn seasons compared to the winter, whereas foliar phosphorus also varied across the seasons but to a lesser degree. The highest percentage of species pairs was separable using foliar nitrogen, compared to the pigments and phosphorus, emphasizing the importance of nutrients such as leaf proteins for species discrimination. The study found a changing relationship between leaf spectra and foliar nutrient concentration across the four seasons for the six evergreen tree species. Twenty-two spectral bands which are related to known absorption features of plant properties were identified across the four seasons as important for tree species discrimination. The relationship between leaf spectra and foliar nitrogen was highest during the spring, summer and autumn seasons for narrow bands associated with absorption features of proteins compared to the red-edge region. The spectra band combination 2130 nm and 2240 nm yielded the highest coefficient of determination between leaf spectra and foliar nitrogen across three of the four seasons. Season-specific prediction models were found to be more accurate in predicting foliar nitrogen than prediction models from across all seasons. The twenty-two bands were effective for the data reduction of the hyperspectral data and yielded a similar overall accuracy compared to 421 bands. Multi-seasonal data improved tree species classification for multispectral sensors with a few bands. The classification, in which multi-season leaf spectra or canopy data from RapidEye imagery was used, resulted in higher overall and user’s accuracies when compared to the single-season classifications. In contrast, the use of multi-season data for the classification of leaf spectra with 22 narrow bands, showed no statistical significance of differences compared to the classification results of the single season in which the highest overall accuracy of all single seasons had been obtained. The value of an increased classification accuracy should however be measured against the increase of cost when using images from multiple seasons. The study concludes that although seasonal profiles of foliar biochemicals overlap, multi-season information do improve species discrimination at foliar biochemical, leaf-spectra and canopy-spectra levels

Mapping key fibre biochemical concentrations in KwaZulu-Natal grasslands using remotely sensed technologies.

Master of Agriculture in Geography. University of KwaZulu-Natal, Pietermaritzburg 2017.Abstract available in PDF file

REMOTE SENSING OF FOLIAR NITROGEN IN CULTIVATED GRASSLANDS OF HUMAN DOMINATED LANDSCAPES

Foliar nitrogen (N) concentration of plant canopies plays a central role in a number of important ecosystem processes and continues to be an active subject in the field of remote sensing. Previous efforts to estimate foliar N at the landscape scale have primarily focused on intact forests and grasslands using aircraft imaging spectrometry and various techniques of statistical calibration and modeling. The present study was designed to extend this work by examining the potential to estimate the foliar N concentration of residential, agricultural and other cultivated grassland areas within a suburbanizing watershed. In conjunction with ground-based vegetation sampling, we developed Partial Least Squares (PLS) models for predicting mass-based foliar N across management types using input from airborne and field based imaging spectrometers. Results yielded strong predictive relationships for both ground- and aircraft-based sensors across sites that included turf grass, grazed pasture, hayfields and fallow fields. We also report on relationships between imaging spectrometer data and other important variables such as canopy height, biomass, and water content, results from which show strong promise for detection with high quality imaging spectrometry data and suggest that cultivated grassland offer opportunity for empirical study of canopy light dynamics. Finally, we discuss the potential for application of our results, and potential challenges, with data from the planned HyspIRI satellite, which will provide global coverage of data useful for vegetation N estimation

The utility of new generation multispectral sensors in assessing aboveground biomass of Phragmites australis in wetlands areas in the City of Tshwane Metropolitan Municipality; South Africa.

Master of Science in Environmental Science. University of KwaZulu-Natal. Pietermaritzburg, 2017.Abstract available in PDF file

Exploring issues of balanced versus imbalanced samples in mapping grass community in the telperion reserve using high resolution images and selected machine learning algorithms

ABSTRACT Accurate vegetation mapping is essential for a number of reasons, one of which is for conservation purposes. The main objective of this research was to map different grass communities in the game reserve using RapidEye and Sentinel-2 MSI images and machine learning classifiers [support vector machine (SVM) and Random forest (RF)] to test the impacts of balanced and imbalance training data on the performance and the accuracy of Support Vector Machine and Random forest in mapping the grass communities and test the sensitivities of pixel resolution to balanced and imbalance training data in image classification. The imbalanced and balanced data sets were obtained through field data collection. The results show RF and SVM are producing a high overall accuracy for Sentinel-2 imagery for both the balanced and imbalanced data set. The RF classifier has yielded an overall accuracy of 79.45% and kappa of 74.38% and an overall accuracy of 76.19% and kappa of 73.21% using imbalanced and balanced training data respectively. The SVM classifier yielded an overall accuracy of 82.54% and kappa of 80.36% and an overall accuracy of 82.21% and a kappa of 78.33% using imbalanced and balanced training data respectively. For the RapidEye imagery, RF and SVM algorithm produced overall accuracy affected by a balanced data set leading to reduced accuracy. The RF algorithm had an overall accuracy that dropped by 6% (from 63.24% to 57.94%) while the SVM dropped by 7% (from 57.31% to 50.79%). The results thereby show that the imbalanced data set is a better option when looking at the image classification of vegetation species than the balanced data set. The study recommends the implementation of ways of handling misclassification among the different grass species to improve classification for future research. Further research can be carried out on other types of high resolution multispectral imagery using different advanced algorithms on different training size samples.EM201