Assessing the utility of the spot 6 sensor in detecting and mapping Lantana camara for a community clearing project in KwaZulu-Natal, South Africa

Lantana camara is a significant weed in South Africa which is causing severe impacts on agriculture by reducing grazing areas. This study assessed the potential of the SPOT 6 multispectral sensor and two broadband vegetation indices (NDVI and SR) for detecting and mapping Lantana camara in a community grazing land in KwaZulu-Natal, South Africa. The SPOT 6 bands and vegetation indices successfully classified Lantana camara with an overall accuracy of 75% on an independent test dataset using the random forest algorithm. Furthermore, it was tested if the random forest model based on variable importance (VIP) could improve the classification accuracy using the best subset of bands and indices. A backward feature elimination technique was used to select the best subset of VIP bands and indices to improve the classification. By eliminating SPOT bands 1 and 4 which yielded the lowest VIP scores the random forest model improved the classification accuracy to 83.33% on an independent test dataset. The study indicates the potential of satellite remote sensing in weed detection and mapping in South Africa using readily available multispectral data to assist poorer communities in grazing management

Leaf water content estimation by functional linear regression of field spectroscopy data

11 p.Grapevine water status is critical as it affects fruit quality and yield. We assessed the po-tential of field hyperspectral data in estimating leaf water content (Cw) (expressed as equivalent water thickness) in four commercial vineyards of Vitis vinifera L. reflecting four grape varieties (Mencı´a, Cabernet Sauvignon, Merlot and Tempranillo). Two regression models were evaluated and compared: ordinary least squares regression (OLSR) and functional linear regression (FLR). OLSR was used to fit Cw and vegetation indices, whereas FLR considered reflectance in four spectral ranges centred at the 960, 1190, 1465 and 2035 nm wavelengths. The best parameters for the FLR model were determined using cross-validation. Both models were compared using the coefficient of determination (R2) and percentage root mean squared error (%RMSE). FLR using continuous stretches of the spectrum as input produced more suitable Cw models than the vegetation indices, considering both the fit and degree of adjustment and the interpretation of the model. The best model was obtained using FLR in the range centred at 1465 nm (R2 ¼ 0.70 and %RMSE ¼ 8.485). The results depended on grape variety but also suggested that leaf Cw can be predicted on the basis of spectral signature.S

Determining optimum wavelengths for leaf water content estimation from re ectance: a distance correlation approach

P. 1-10This paper proposes a method to estimate leaf water content from reflectance in four commercial vineyard varieties by estimating the local maxima of a distance correlation function. First, it applies four different functional regression models to the data and compares the models to test the viability of estimating water content from reflectance. It then applies our methodology to select a small number of wavelengths (optimum wavelengths) from the continuous spectrum, which simplifies the regression problem. Finally, it compares the results to those obtained by means of two different methods: a nonparametric kernel smoothing for variable selection in functional data and a wavelet-based weighted LASSO functional linear regression. Our approach proved to have some advantages over these two testing approaches, mainly in terms of the computing time and the lack of assumption of an underlying model. Finally the paper concludes that estimating water content from a few wavelengths is almost equivalent to doing so using larger wavelength intervalsS

Field Spectroscopy: A Non-Destructive Technique for Estimating Water Status in Vineyards

Water status controls plant physiology and is key to managing vineyard grape quality and yield. Water status is usually estimated by leaf water potential (LWP), which is measured using a pressure chamber; however, this method is di cult, time-consuming, and error-prone. While traditional spectral methods based on leaf reflectance are faster and non-destructive, most are based on vegetation indices derived from satellite imagery (and so only take into account discrete bandwidths) and do not take full advantage of modern hyperspectral sensors that capture spectral reflectance for thousands of wavelengths. We used partial least squares regression (PLSR) to predict LWP from reflectance values (wavelength 350–2500 nm) captured with a field spectroradiometer. We first identified wavelength ranges that minimized regression error. We then tested several common data pre-processing methods to analyze the impact on PLSR prediction precision, finding that derivative pre-processing increased the determination coe cients of our models and reduced root mean squared error (RMSE). The models fitted with raw data obtained their best results at around 1450 nm, while the models with derivative pre-processed achieved their best estimates at 826 nm and 1520 nm.Education Department of the Junta de Castilla y León-Spai

Evaluation of Point Hyperspectral Reflectance and Multivariate Regression Models for Grapevine Water Status Estimation

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Assessing the utility of Landsat 8 multispectral sensor and the MaxEnt species distribution model to monitor Uromycladium acaciae damage in KwaZulu-Natal, South Africa.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.South Africa has approximately 1.27 million hectares of plantation forests, with the forestry industry contributing 1% to the state’s Gross Domestic Product (GDP). A major threat to the industry is an escalating number of tree-damaging insect pests and pathogens. Uromycladium acaciae is a pathogen which causes wattle rust in black wattle (Acacia mearnsii) plantation forests; after its first appearance in 2013 in KwaZulu-Natal, it has since spread to most areas in South Africa where suitable hosts are present, causing severe economic losses to the industry. Traditional field-based methods of assessing forest damage can be labour intensive and time consuming. The effective management of these biotic threats requires quick and efficient methods of assessing forest health. Remote sensing has the potential to assess vast areas of forest plantations in a timely and efficient manner. Therefore, the primary aim of this research is to assess U. acaciae canopy damage using freely available Landsat 8 multispectral satellite imagery and the partial least squares discriminant analysis algorithm (PLS-DA). The study was done on two plantation farms near Richmond, KwaZulu-Natal which are managed by NCT Forestry. The model detected forest canopy damage with an accuracy of 88.24% utilising seven bands and the PLS-DA algorithm. The Variable Importance in Projection (VIP) method was used to optimise the variables to be included in the model by selecting the most influential bands. These were identified as coastal aerosol band (430 nm - 450 nm), red band (640 nm - 670 nm), near infrared (850 nm - 880 nm) and NDVI. The model was run with only the VIP selected bands and an accuracy of 82.35% was produced. The study highlighted the potential of remote sensing to (1) detect canopy damage caused by U. acaciae and (2) provide a monitoring framework for analysing forest health using freely available Landsat 8 imagery. The secondary aim of this study is to use the maximum entropy species distribution model (SDM) to determine potential forestry areas that may be at risk of U. acaciae infection. Species distribution modelling using bioclimatic predictors can define the climatic range associated with the disease caused by this pathogen. The climatic range will help identify high risk areas and forecast potential outbreaks. This study assessed the capacity of the MaxEnt species distribution model (SDM) and bioclimatic variables to estimate forestry areas that have a suitable climate for U. acaciae development. The model was developed using 19 bioclimatic variables sourced from WorldClim. The variables are used as predictors of risk for U. acaciae infection and are applied to the landscape occupied by black wattle plantations. The results produced an area under the curve (AUC) value of = 0.97 suggesting strong discriminatory power of the model. The potential distribution of U. acaciae under future climate conditions was also assessed by applying the model to the bioclimatic variables developed from future climate surfaces acquired from WorldClim. The results emphasized (1) the usefulness of species distribution models for forest management and (2) highlighted how climate change can influence the distribution of U. acaciae due to the expansion and contraction of suitable climatic ranges. Overall, the results from the study indicate (1) Landsat 8 multispectral imagery can be used to detect forest canopy damage caused by U. acaciae, (2) PLS-DA variable importance in the projection can successfully select the subset of multispectral bands that are most important in detecting damage caused by U. acaciae, (3) the MaxEnt species distribution model and bioclimatic variables can be used to identify geographic locations at risk of U. acaciae infection and (4) the variable permutation metric successfully identified the most important bioclimatic variables for U. acaciae development and highlighted the climatic patterns associated with the occurrence of the disease caused by this pathogen

A multiscale remote sensing assessment of subtropical indigenous forests along the wild coast, South Africa

The subtropical forests located along South Africa’s Wild Coast region, declared as one of the biodiversity hotspots, provide benefits to the local and national economy. However, there is evidence of increased pressure exerted on the forests by growing population and reduced income from activities not related to forest products. The ability of remote sensing to quantify subtropical forest changes over time, perform species discrimination (using field spectroscopy) and integrating field spectral and multispectral data were all assessed in this study. Investigations were conducted at pixel, leaf and sub-pixel levels. Both per-pixel and sub-pixel classification methods were used for improved forest characterisation. Using SPOT 6 imagery for 2013, the study determined the best classification algorithm for mapping sub-tropical forest and other land cover types to be the maximum likelihood classifier. Maximum likelihood outperformed minimum distance, spectral angle mapper and spectral information divergence algorithms, based on overall accuracy and Kappa coefficient values. Forest change analysis was made based on spectral measurements made at top of the atmosphere (TOC) level. When applied to the 2005 and 2009 SPOT 5 images, subtropical forest changes between 2005-2009 and 2009-2013 were quantified. A temporal analysis of forest cover trends in the periods 2005-2009 and 2009-2013 identified a decreasing trend of -3648.42 and -946.98 ha respectively, which translated to 7.81 percent and 2.20 percent decrease. Although there is evidence of a trend towards decreased rates of forest loss, more conservation efforts are required to protect the Wild Coast ecosystem. Using field spectral measurements data, the hierarchical method (comprising One-way ANOVA with Bonferroni correction, Classification and Regression Trees (CART) and Jeffries Matusita method) successfully selected optimal wavelengths for species discrimination at leaf level. Only 17 out of 2150 wavelengths were identified, thereby reducing the complexities related to data dimensionality. The optimal 17 wavelength bands were noted in the visible (438, 442, 512 and 695 nm), near infrared (724, 729, 750, 758, 856, 936, 1179, 1507 and 1673 nm) and mid-infrared (2220, 2465, 2469 and 2482 nm) portions of the electromagnetic spectrum. The Jeffries-Matusita (JM) distance method confirmed the separability of the selected wavelength bands. Using these 17 wavelengths, linear discriminant analysis (LDA) classified subtropical species at leaf level more accurately than partial least squares discriminant analysis (PLSDA) and random forest (RF). In addition, the study integrated field-collected canopy spectral and multispectral data to discriminate proportions of semi-deciduous and evergreen subtropical forests at sub-pixel level. By using the 2013 land cover (using MLC) to mask non-forested portions before sub-pixel classification (using MTMF), the proportional maps were a product of two classifiers. The proportional maps show higher proportions of evergreen forests along the coast while semi-deciduous subtropical forest species were mainly on inland parts of the Wild Coast. These maps had high accuracy, thereby proving the ability of an integration of field spectral and multispectral data in mapping semi-deciduous and evergreen forest species. Overall, the study has demonstrated the importance of the MLC and LDA and served to integrate field spectral and multispectral data in subtropical forest characterisation at both leaf and top-of-atmosphere levels. The success of both the MLC and LDA further highlighted how essential parametric classifiers are in remote sensing forestry applications. Main subtropical characteristics highlighted in this study were species discrimination at leaf level, quantifying forest change at pixel level and discriminating semi-deciduous and evergreen forests at sub-pixel level

A quantitative assessment of the impacts of water status and chemical bioassays on structural attributes of Eucalyptus clones and plantation soil nitrogen using hyperspectral data.

Ph.D. University of KwaZulu-Natal, Durban 2012.Abstract available in PDF file

Remote sensing grass quantity under different grassland management treatments practised in the Southern African rangelands.

Doctor of Philosophy in Environmental Science. University of KwaZulu-Natal, Pietermaritzburg 2016.Abstract available in PDF file