10 research outputs found

    Informing reforestation practices : quantifying live forest above ground biomass of a randomly mixed natural forest plantation using GIS and remote sensing models.

    Get PDF
    Master of Science in Geography. University of KwaZulu-Natal, Pietermaritzburg, 2017.Restoration of natural forests is viewed as one of the effective and viable approaches for mitigating and adapting to climate change. However, maximising the carbon capture and storage of naturally mixed forest plantations is currently a challenge for forest managers, due to the complex nature of species interaction and environmental controls that inhibit the distribution and growth rates of certain species. Monitoring the amount of carbon captured and stored in natural forest ecosystem is vital in verifying their productivity and detecting areas of concern that could be unproductive. In this study the productivity of the Buffelsdraai reforestation site was monitored using above ground biomass (AGB) of planted trees. While there are traditional approaches for monitoring forest AGB with high accuracy, these approaches are unfavourable because they are timeous and spatially restricted. Fortunately, the inception of remote sensing has provided viable approaches for estimating forest AGB at a synoptic scale and with low cost. The purpose of this study was to apply remote sensing and GIS models to quantify the ecological benefits of the Buffelsdraai reforestation project on AGB productivity. The study investigated the potential of the spatially optimised three band texture combinations in predicting and mapping forest AGB and structural diversity. This research study has potential to contribute to the importance of spatial planning and design of naturally mixed forest plantations to improve their diversity and AGB productivity. The first part of the study focused on mapping the temporal and spatial distribution of forest AGB using spatially optimised three band texture combinations computed from SPOT-6 imagery and random forest regression algorithm. The results indicated that the three band texture combinations were superior in predicting forest AGB compared to raw texture bands and two band texture combinations. The second part of the thesis focussed on assessing the effects of forest structural diversity and topographic variables on forest AGB productivity using GIS and remotely sensed data. The forest structural diversity measures were predicted using three band texture combinations modelled using random forest and stochastic gradient boosting algorithms. The topographic variables were derived using the digital elevation model in ArcMap 10.3. Results indicated that random forest yielded overall higher accuracies in predicting the forest structural diversity measures compared to stochastic gradient boosting. More importantly, the study showed that forest diversity and topographic variables have significant influences on forest AGB variability. Overall the study provided insight into the management of natural forests and to the importance of spatial planning and design of these mixed forests

    Commercial forest species discrimination and mapping using image texture computed from WorldView-2 pan sharpened imagery in KwaZulu-Natal, South Africa.

    Get PDF
    Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Forest species discrimination is vital for precise and dependable information, essential for commercial forest management and monitoring. Recently, the adoption of remote sensing approaches has become an important source of information in commercial forest management. However, previous studies have utilized spectral data or vegetation indices to detect and map commercial forest species, with less focus on the spatial elements. Therefore, this study using image texture aims to discriminate commercial forest plantations (i.e. A. mearnsii, E. dunnii, E. grandis and P. patula) computed from a 0.5m WorldView-2 pan-sharpened image in KwaZuluNatal, South Africa. The first objective of the study was to discriminate commercial forest species using image texture computed from a 0.5m WorldView-2 pan-sharpened image and the Partial Least Squares Discriminate Analysis (PLS-DA) algorithm. The results indicated that the image texture model (overall accuracy (OA) = 77%, kappa = 0.69) outperformed both the vegetation indices model (OA = 69%, kappa = 0.59) and raw spectral bands model (OA = 64%, kappa = 0.52). The most successful texture parameters selected by PLS-DA were mean, correlation, and homogeneity, which were primarily computed from the red-edge, NIR1 and NIR2 bands. Lastly, the 7x7 moving window was commonly selected by the PLS-DA model when compared to the 3x3 and 5x5 moving windows. The second objective of the study was to explore the utility of texture combinations computed from a fused 0.5m WorldView-2 image in discriminating commercial forest species in conjunction with the PLS-DA and Sparse Partial Least Squares Discriminate Analysis (SPLS-DA) algorithm. The accuracies achieved using SPLS-DA model, which performed variable selection and dimension reduction simultaneously yielded an overall accuracy of 86%. In contrast, the PLS-DA and variable importance in the projection (VIP) produced an overall classification accuracy of 81%. Generally, the finding of this study demonstrated the ability of image texture to precisely provide adequate information that is essential for tree species mapping and monitoring

    Quantifying ecosystem services within a reforested urban landscape using remote sensing in eThekwini region of KwaZulu-Natal, South Africa.

    Get PDF
    Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Abstract available in PDF

    Assessing the utility of remotely sensed data and integrated topographic characteristics for determining tree stand structural complexity in a re-forested urban landscape.

    Get PDF
    Master of Science in Environmental Science. University of KwaZulu-Natal 2017.Transformation of natural landscapes into impervious built-up surfaces through urbanisation is known to significantly interfere with urban ecological integrity and its ability to provide environmental goods and services as well as accelerate climate change and associated impacts. Urban reforestation is widely promulgated as an ideal mitigation practice against impacts associated with urbanisation, however reforestation often has to compete with multiple and more “lucrative” urban land uses. This necessitates the optimisation of ecological benefits derived from reforestation within the limited available land. Such optimisation demands spatially explicit monitoring and evaluation (M&E). The recent proliferation of tree stand structural complexity (SSC) – a multidimensional index of the ecological performance of tree stands - offers great potential as an alternative indicator of ecological performance, instead of the one-dimensional traditional indicators such as Leaf Area Index, stem diameter and tree height. Furthermore, the recent advancements in remote sensing (RS) technology offers an improved potential of determining ecological performance across an urban reforested landscape. However, remotely sensed data costs and reliability often hinder their operational adoption. Consequently, the recent advancements in the freely available Sentinel 2 (S-2) data offer great potential for a cost effective operational M&E of SSC. The aim of this study was to i) Examine the utility of the freely available S-2 multispectral instrument imagery to determine SSC using the Partial Least Squares (PLS) regression technique within a re-forested urban landscape ii) Explore the potential of integrating topographic datasets with the S-2 data to determine SSC and iii) To rank the value of these variables in determining SSC. Tree structural data from a re-forested urban area was collected and a SSC index used to determine the area’s ecological performance. Multiple vegetation indices (VIs) were derived from the S-2 imagery while topographic variables (i.e. Topographic Wetness Index (TWI), slope, Area Solar Radiation (ASR), and elevation) were derived from a Digital Elevation Model (DEM). Results showed that the PLS model (n = 90) using the most important S-2 VIs (S2 REP, REIP, IRECI, GNDVI) produced a moderate predictive accuracy (0.215 NRMSECV) while topographybased model produced a high prediction accuracy (0.147 NRMSECV). Integrating the S-2 data with topographic information produced the highest prediction accuracy (0.13 NRMSECV). Furthermore, results indicate that SSC significantly varied across all topographic variables, with TWI and slope as the most important determinants of SSC. These results provide valuable spatially explicit information about the ecological performance of the reforested urban areas. Additionally, the study demonstrates the value of topographic data as an alternative predictor of SSC as well as the value of integrating the S-2 data with topographic characteristics in determining the performance of reforested areas

    Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data

    Get PDF
    Tropical forest ecosystems are undergoing rapid transformation as a result of changing environmental conditions and direct human impacts. However, we cannot adequately understand, monitor or simulate tropical ecosystem responses to environmental changes without capturing the high diversity of plant functional characteristics in the species-rich tropics. Failure to do so can oversimplify our understanding of ecosystems responses to environmental disturbances. Innovative methods and data products are needed to track changes in functional trait composition in tropical forest ecosystems through time and space. This study aimed to track key functional traits by coupling Sentinel-2 derived variables with a unique data set of precisely located in-situ measurements of canopy functional traits collected from 2434 individual trees across the tropics using a standardised methodology. The functional traits and vegetation censuses were collected from 47 field plots in the countries of Australia, Brazil, Peru, Gabon, Ghana, and Malaysia, which span the four tropical continents. The spatial positions of individual trees above 10 cm diameter at breast height (DBH) were mapped and their canopy size and shape recorded. Using geo-located tree canopy size and shape data, community-level trait values were estimated at the same spatial resolution as Sentinel-2 imagery (i.e. 10 m pixels). We then used the Geographic Random Forest (GRF) to model and predict functional traits across our plots. We demonstrate that key plant functional traits can be accurately predicted across the tropicsusing the high spatial and spectral resolution of Sentinel-2 imagery in conjunction with climatic and soil information. Image textural parameters were found to be key components of remote sensing information for predicting functional traits across tropical forests and woody savannas. Leaf thickness (R2 = 0.52) obtained the highest prediction accuracy among the morphological and structural traits and leaf carbon content (R2 = 0.70) and maximum rates of photosynthesis (R2 = 0.67) obtained the highest prediction accuracy for leaf chemistry and photosynthesis related traits, respectively. Overall, the highest prediction accuracy was obtained for leaf chemistry and photosynthetic traits in comparison to morphological and structural traits. Our approach offers new opportunities for mapping, monitoring and understanding biodiversity and ecosystem change in the most species-rich ecosystems on Earth

    Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data

    Get PDF
    Funding Information: This work is a product of the Global Ecosystems Monitoring (GEM) network (gem.tropicalforests.ox.ac.uk). J.A.G. was funded by the Natural Environment Research Council (NERC; NE/T011084/1 and NE/S011811/1) and the Netherlands Organisation for Scientific Research (NWO) under the Rubicon programme with project number 019.162LW.010. The traits field campaign was funded by a grant to Y.M. from the European Research Council (Advanced Grant GEM-TRAIT: 321131) under the European Union‘s Seventh Framework Programme (FP7/2007-2013), with additional support from NERC Grant NE/D014174/1 and NE/J022616/1 for traits work in Peru, NERC Grant ECOFOR (NE/K016385/1) for traits work in Santarem, NERC Grant BALI (NE/K016369/1) for plot and traits work in Malaysia and ERC Advanced Grant T-FORCES (291585) to Phillips for traits work in Australia. Plot setup in Ghana and Gabon were funded by a NERC Grant NE/I014705/1 and by the Royal Society-Leverhulme Africa Capacity Building Programme. The Malaysia campaign was also funded by NERC GrantNE/K016253/1. Plot inventories in Peru were supported by funding from the US National Science Foundation Long-Term Research in Environmental Biology program (LTREB; DEB 1754647) and the Gordon and Betty Moore Foundation Andes-Amazon Program. Plots inventories in Nova Xavantina (Brazil) were supported by the National Council for Scientific and Technological Development (CNPq), Long Term Ecological Research Program (PELD), Proc. 441244/2016-5, and the Foundation of Research Support of Mato Grosso (FAPEMAT), Project ReFlor, Proc. 589267/2016. During data collection, I.O. was supported by a Marie Curie Fellowship (FP7-PEOPLE-2012-IEF-327990). GEM trait data in Gabon was collected under authorisation to Y.M. and supported by the Gabon National Parks Agency. D.B. was funded by the Fondation Wiener-Anspach. W.D.K. acknowledges support from the Faculty Research Cluster ‘Global Ecology’ of the University of Amsterdam. M.S. was funded by a grant from the Ministry of Education, Youth and Sports of the Czech Republic (INTER-TRANSFER LTT19018). Y.M. is supported by the Jackson Foundation. We thank the two anonymous reviewers and Associate Editor G. Henebry for their insightful comments that helped improved this manuscript.Peer reviewedPostprin

    Using spectral and textural information to detect and map Parthenium hysterophorus L. in Mtubatuba, South Africa.

    Get PDF
    Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Parthenium hysterophorus L. (parthenium) is an alien invasive species that has had severe environmental and human impacts in three continents. Sustainable management and control of the invasive species requires an understanding of its distribution and rate of spread. Our first study focuses on the use of spectral information of commercial sensor RapidEye and freely available Sentinel-2 imagery to detect parthenium and other land cover classes. Sentinel-2 outperformed RapidEye to classify most land cover classes, with an overall classification accuracy of 82% and 71%, respectively. This was likely due to the superior spectral resolution of Sentinel-2. However, RapidEye performed better when classifying parthenium, potentially due to the fact that there were some patches that were smaller than the Sentinel-2 spatial resolution. Nonetheless, Sentinel-2 represents a good opportunity to map larger parthenium stands and other land cover types. The second study focused on mapping parthenium using texture analysis and SPOT-6 imagery. It compared the mapping ability between the panchromatic and multispectral bands using the PLS-DA algorithm. The panchromatic band achieved a higher overall classification accuracy than the multispectral bands (77% and 73%, respectively). Furthermore, the panchromatic band achieved superior performance compared to multispectral bands for parthenium. This may be attributed to the higher spatial resolution of the panchromatic band as it has been shown that finer spatial resolution is beneficial in texture analysis. Overall texture analysis using SPOT 6 imagery was the most successful combination which allowed us to accurately map parthenium distribution

    Utilisation de la télédétection pour l’analyse de la dynamique de la biomasse aérienne sèche totale des forêts et des palmiers à huile d’une plantation mature dans le Bassin du Congo

    Get PDF
    Le stockage de la biomasse aérienne (BA) sèche totale des forêts est indispensable à la lutte contre les changements climatiques. Depuis quelques décennies, il y a une tendance à l’introduction de cultures agro-industrielles, comme les plantations de palmiers à huile, dans les forêts tropicales dans le Bassin du Congo. Ces conversions participent à l’augmentation ou à la diminution des émissions ou absorptions de dioxyde de carbone (CO2) dans l’atmosphère, tout en occasionnant des changements climatiques. Dans cette région, la disponibilité des données de terrain et de télédétection est relativement limitée pour évaluer la BA. L’estimation de la BA des palmiers à huile n’est également pas maitrisée dans le Bassin du Congo. Les incertitudes rapportées dans les études précédentes utilisant la télédétection demeurent encore élevées. Plusieurs approches à fort potentiel restent encore à développer ou à évaluer. À titre d’exemple, l’approche MARS (régressions multivariées par spline adaptative) pour estimer la BA n’a pas encore été testée, notamment avec des données combinées optiques, LiDAR et radar. Les pertes et les gains de la BA dus aux changements des forêts en palmiers à huile dans le Bassin du Congo, particulièrement au Gabon, n’ont pas encore été quantifiés. La présente étude vise alors à contribuer au développement des méthodes d’estimation de la BA par l’utilisation de la télédétection pour comprendre l’impact des plantations des palmiers à huile sur les variations de la BA des forêts. Au cours de la présente étude, nous avons développé les premiers modèles allométriques d’estimation de la BA des palmiers à huile à l’aide de mesures in situ originales, que nous avons acquises dans le Bassin du Congo. Des modèles de BA des palmiers à huile ont également été établis avec MARS et les régressions linéaires multiples (RLM) en utilisant des indices dérivés de la transformée de Fourier (indices FOTO) à partir d’images satellitaires FORMOSAT-2 et PlanetScope. Finalement, cette thèse propose aussi des modèles MARS qui combinent des données de télédétection optiques (SPOT 7), LiDAR et radar polarimétrique interférométrique (PolInSAR) pour estimer la BA des forêts tropicales. À l’aide des estimations fournies par les modèles construits, la dynamique des BA des forêts et des plantations de palmiers à huile a été analysée. Les résultats ont montré que le modèle allométrique local de BA, utilisant la variable composée formée par le diamètre à hauteur de poitrine, la hauteur et la densité, ou le nombre de feuilles, permettait d’avoir les meilleures estimations (erreur quadratique moyenne relative (%RMSE) = 5,1 %). Un modèle allométrique de BA relativement performant a également été construit en utilisant seulement le diamètre et la hauteur (%RMSE = 8,2 %). Pour l’estimation des BA des palmiers à partir d’images FORMOSAT-2 et PlanetScope, les résultats démontrent que l’approche MARS permet les évaluations les plus précises (%RMSE ≤ 9,5 %). Cela est particulièrement vrai lorsque les images FORMOSAT-2 sont considérées (%RMSE ≤ 6,4 %). Les modèles de régression linéaire multiple donnent aussi des résultats avec des erreurs faibles, mais n’atteignent pas l’approche MARS (%RMSE ≥ 6,6 %). Cette dernière a été utilisée pour développer une série de modèles afin d’estimer les BA des forêts de la région d’étude. Les résultats montrent que le modèle utilisant la variable individuelle de la hauteur médiane de la canopée (RH50) dérivée des données LiDAR a estimé la biomasse avec plus de précision (%RMSE = 28 %). La combinaison de données de télédétection (optique, LiDAR et radar) a réduit de près de 4 % les erreurs d’estimation de la biomasse du modèle exploitant la variable individuelle (RH50). Les analyses de la dynamique de BA due aux remplacements des forêts en palmeraies ont enfin permis de constater que les forêts sont plus des vecteurs de gains de BA que les palmeraies particulièrement pour les forêts matures (512 t ha-1 de plus de BA que les palmeraies, soit un surplus de 88 %). Ce constat est identique pour les forêts secondaires vieilles (168 t ha-1, soit 70 % de surplus de BA que les palmeraies) et les forêts secondaires jeunes-adultes ou inondables (74 t ha-1 de plus que les palmeraies, soit un excédent de 51 %). En revanche, l’installation de plantations de palmiers à huile dans les zones de sols nus ou forêts en repousse pourrait être gagnante en termes de BA, car celles-ci ne présentent que 72 t ha-1 de BA (100 % moins que les palmiers). C’est le cas aussi dans les zones occupées par les forêts secondaires jeunes-adultes avec des BA minimales et des sols nus ou des forêts en repousse avec des BA maximales de 52 t ha-1 (20 t ha-1, soit 38 % de BA de moins que les palmeraies)

    Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass

    Get PDF
    This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques

    Estimación de biomasa aérea de eucalipto (Eucalyptus grandis) y pino (Pinus spp) en plantaciones forestales comerciales, usando imágenes satelitales Sentinel

    Get PDF
    La estimación de la biomasa aérea forestal es necesaria para diversas aplicaciones técnicas y científicas, lo que permite mejorar el manejo de los bosques y plantaciones . Dado que las mediciones locales son costosas, existe un gran interés en obtener estimaciones confiables sobre grandes áreas a partir de datos de sensores remotos. Actualmente, dichas estimaciones se obtienen con una variedad de fuentes de datos, métodos estadísticos y estándares de predicción. Los datos de percepción remota en combinación con algoritmos de aprendizaje automático basados en arboles de decisión han generado resultados favorables en la estimación de valores de biomasa aérea (AGB, siglas en inglés). En este estudio, la biomasa aérea se estimó para dos especies de árboles principales, Eucalyptus grandis (E. grandis) y Pinus spp (P. spp), de plantaciones forestales comerciales en el departamento del Cauca, Colombia. La biomasa aérea se estimó combinando los datos SAR (Radar de apertura sintética) de banda C del satélite Sentinel-1A, las imágenes de textura generadas a partir de los datos de Sentinel-1A, los índices de vegetación producidos con los datos de Sentinel-2A y datos de inventarios forestales. Se usaron regresiones paramétrica lineales y regresiones no paramétricas con Random Forest para establecer una relación entre los valores medidos en campo y los parámetros de percepción remota. El uso de un modelo de Random Forest en combinación de índices de vegetación con la retrodispersión de Radar de Apertura Sintética (SAR, siglas en inglés ) como variables predictoras mostró el mejor resultado para el bosque de E. grandis, con un coeficiente de valor de determinación de 0,273 y un valor RMSE de 346,62 t.ha-1. En P. spp, el mejor resultado se pudo encontrar en la misma combinación (R2 = 0,617 y EMC = 9.025 t.ha-1). Este estudio muestra que los datos satelitales Sentinel tienen la capacidad de estimar AGB en plantaciones forestales comerciales y que el algoritmo de aprendizaje automático Random Forest puede ser muy útil para hacerlo.Abstract: The estimation of the forest aboveground biomass (AGB) is necessary for diverse technical and scientific applications, which allows to improve the management of forests and plantations. Since local measurements are expensive, there is necessary to get reliable estimates over large areas from remote sensing data. Currently, these estimations are obtained with a variety of data sources, statistical methods and prediction standards. Remote sensing data in combination with machine learning algorithms based on decision trees have generated favorable results in the estimation of aboveground biomass values. In this study, aboveground biomass was estimated for two main tree species, Eucalyptus grandis (E. grandis) and Pinus spp (P. spp), from commercial forest plantations in Cauca, Colombia. AGB was estimated by combining C-band SAR data from Sentinel-1A satellite, texture images generated from Sentinel-1A data, vegetation indices produced with Sentinel-2A data, and forest inventory data. Linear parametric regressions and nonparametric regressions as Random Forest were used to establish a relationship between the values measured in field and the parameters of remote sensing. The use of a Random Forest model in combination of vegetation indices with SAR data as predictor variables showed the best result for the E. grandis forest, with a coefficient of determination value of 0.273 and an RMSE value of 346, 62 t.ha-1. In P. spp, the best result could be found in the same combination (R2 = 0.617 and RMSE = 9.025 t.ha-1). This study shows that Sentinel satellite data have the ability to estimate AGB in commercial forest plantations and that the Random Forest machine learning algorithm can be very useful to do so.Maestrí
    corecore