Investigating the utility of LiDAR for modelling forest canopy gaps and species classification

Date
2018-03
Journal Title
Journal ISSN
Volume Title
Publisher
Stellenbosch : Stellenbosch University
Abstract
ENGLISH ABSTRACT: Canopy gaps result from the ineffective use of growing space, presence of features (such as rocks) prohibiting harvesting operations, and naturally by wind, disease, drought, and fires. Traditionally, canopy gaps are detected and interpreted using in situ methods and aerial photography. More recently, remote sensing has been utilized for detecting and delineating canopy gaps. However, optical remote sensing sensors are limited by their spatial resolution. Light detection and ranging (LiDAR) provides new opportunity for canopy gap detection and delineation. Literature reveals that no study to date has used LiDAR within an object-based image analysis environment (OBIA) to model canopy gaps in South Africa. This research thus aims to investigate the utility of LiDAR for modelling forest canopy gaps and use the delineated canopy gaps for species modelling within a commercial plantation. The first component evaluated the utility of a LiDAR-derived CHM and intensity raster to detect and delineate canopy gaps within a Eucalyptus grandis plantation. Canopy gaps were modelled using LiDAR canopy height model (CHM), intensity raster, and a combination of CHM and intensity raster. Thematic accuracies were above 95%, with KHAT values ranging from 0.88 to 0.96. Models were evaluated using an independent test set, yielding thematic accuracies above 90%, with KHAT values ranging from 0.82 to 0.91. A comparative area-based assessment was undertaken on all three datasets and yielded train accuracies ranging from 75% to 95% and test accuracies ranging from 79% to 92%. The combined dataset, i.e. CHM and intensity raster yielded the best overall classification results. Additionally, delineated canopy gaps were spatially analysed using Getis-Ord Gi* and FRAGSTATS. Getis-Ord Gi* results showed spatial clustering of canopy gaps within the plantation. Furthermore, FRAGSTATS analysed the spatial characterisation of canopy gaps and found varied patch densities (PD) and percentage of landscape (PLAND) occupied by canopy gaps. Canopy gaps were found to be generally irregularly shaped within the plantation. The second component used delineated canopy gaps and LiDAR-derived intensity and texture features to discriminate Eucalyptus grandis and Eucalyptus dunnii using the random forest (RF) algorithm. Classification models were built using LiDAR intensity and texture information extracted from canopy gaps, and a combination of canopy gaps and forest canopy. Promising results were obtained using a combination of intensity and texture features extracted from canopy gaps alone, with a train out of bag (OOB) error of 7.89 (KHAT = 0.84) and test accuracy of 90.91% (KHAT = 0.81). Improved species discrimination results were obtained using a combination of intensity and texture features and a combination of canopy gaps and forest canopy, with a train OOB error of 3.66 (KHAT = 0.92) and test accuracy of 94.74% (KHAT = 0.88). The framework developed in this study, i.e. using LiDAR and machine learning, shows promise and robustness, and could potentially assist foresters and forest managers in better understanding the mechanisms underpinning the formation and distribution of canopy gaps. Additionally, this framework shows promise for species discrimination. Therefore, this methodology could potentially be operationalised within commercial forestry for timely and accurate canopy gap detection and species classification.
AFRIKAANSE OPSOMMING: Boomkap gapings word veroorsaak deur die ondoeltreffende gebruik van groeiende ruimte, die teenwoordigheid van kenmerke (soos rotse) wat oesbedrywighede verbied en natuurlik deur wind, siekte, droogte, en brande. Boomkap gapings was tradisioneel opgespoor en geïnterpreteer deur veldwerk en lugfotografie. Onlangs is afstandswaarneming aangewend vir die opsporing en afbakening van boomkap gapings. Optiese sensors vir afstandswaarneming word beperk deur ruimtelike resolusie. Ligopsporing en –verpreiding (LiDAR) bied nuwe geleenthede vir die opsporing en afbakening van boomkap gapings. Literatuur toon dat geen studie tot dusver LiDAR binne ‘n objekgebaseerde beeldanalise-omgewing (OBIA) gebruik was om boomkap gapings in Suid-Afrika te modelleer nie. Hierdie navorsingsdoel was om LiDAR te ondersoek vir die modellering van boomkap gapings en gebruik die afgebakende boomkap gapings vir spesie modellering binne ‘n kommersiële plantasie. Die eerste komponent het ‘n LiDAR-afgeleide boskap hoogte model (CHM) en intensiteit raster geëvalueer om boomkap gapings in ‘n Eucalyptus grandis plantasie op te spoor en te delinieer. Boomkap gapings is gemodelleer deur LiDAR CHM, intensiteit raster en ‘n kombinasie van CHM en intensiteit raster. Tematiese akkuraatheid was bo 95%, met KHAT waardes wat wissel van 0.88 tot 0.96. Modelle is geëvalueer met behulp van ‘n onafhanklike toetsstel, wat tematiese akkuraatheid bo 90% lewer, met KHAT waardes tussen 0.82 en 0.91. ‘n Vergelykende area-gebaseerde assessering is onderneem op al drie datastelle en het oplei akkuraathede opgelewer, wat wissel van 75% tot 95% en toets akkuraathede wat wissel van 79% tot 92%. Die gekombineerde dataset, d.w.s. CHM en intensiteit raster het die beste algehele klassifikasie resultate behaal. Verder is afgebakende boomkap gapings ruimtelik ontleed met Getis-Ord Gi* en FRAGSTATS. Getis-Ord Gi* resultate het ruimtelike groepering van boomkap gapings in die plantasie getoon. Gevolglik het FRAGSTATS die ruimtelike karakterisering van boomkap gapings ontleed en gevarieerde pleisterdigtheid (PD) en persentasie landskap (PLAND) aangetref. Daar was gevind dat boomkap gapings in die plasie onreëlmatige vorme het. Die tweede komponent gebruik afgebakende boomkap gapings en LiDAR-afgeleide intensiteit en tekstuur-eienskappe om Eucalyptus grandis en Eucalyptus dunnii te onderskei deur die ewekansige woud (RF) algoritme te gebruik. Sistematiek modelle is gebou met behulp van LiDAR-intensiteit en tekstuur inligting wat uit boomkap gapings uigetrek is en ‘n kombinasie van boomkap gapings en boskap. Belowende resultate is verkry deur die kombinasie van intensiteit en tekstuur eienskappe net uit boskap gapings te onttrek, met ‘n oplei uit sak (OOB) fout van 7.89 (KHAT = 0.84) en toets akkuraatheid van 90.91% (KHAT = 0.81). Verbeterde spesies diskriminasie resultate is verkry deur ‘n kombinasie van intensiteit en tekstuur informasie en ‘n kombinasie van boskap gapings en boskap met ‘n oplei OOB fout van 3.66 (KHAT = 0.92) en ‘n toets akkuraatheid van 94.74% (KHAT = 0.88). Die raamwerk wat in hierdie studie ontwikkel is, naamlik die gebruik van LiDAR en masjienleer, toon robuustheid en kan bosbouers en bosbestuurders potensieel help om die vorming en verspreiding van boskap gapings te verstaan. Daarbenewens het hierdie raamwerk belofte vir spesies diskriminasie. Daarom kan hierdie metodologie moontlik binne die kommersiële bosbou aangewend word vir tydige en akkurate boskap gaping en spesies-klassifikasie.
Description
Thesis (MA)--Stellenbosch University, 2018.
Keywords
Canopy gaps, LiDAR, OBIA, Laser radar, LIDAR - Light Detection and Ranging, Remote sensing, Object-based Image Analysis, UCTD
Citation