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    • Modelling Woody Vegetation in Sudano-Sahe-lina Zone of Nigeria Using Remote Sensing
    • Remote Sensing of Forest Succession in Hong Kong's Country Parks
    • Remote Sensing of Secondary Vegetation Succession in Hong Kong's Country Parks
    • Road Defect Detection Using Deep Learning Method
    • Solar Energy Supply in Cloud-prone Areas of Hong Kong
    • Tree Thermal Image
    • 70 Years of Forest Succession in the Degraded Tropical Landscape of Hong Kong
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Remote Sensing of Secondary Vegetation Succession in Hong Kong's Country Parks

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    A multi-scale object-based approach was applied to sequential aerial photographs and recent high-resolution satellite images to map structural changes in natural vegetation over the last 70 years, from 1945 to 2014. Temporal changes in the spatial extents of structural classes, and rates of change were determined for each of the periods; from 1945 to 1963, 1963 to 1989, 1989 to 2001, and 2001 to 2014. Also, spatial patterns of forest succession related to topography and morphology of the landscape were analyzed to determine the influence of changes in specific landscape structural parameters on forest succession. The mapping of structural changes associated with forest recovery over the past 70 years also enabled the recording and study of changes in species composition along the successional and environmental gradients for better understanding of the processes of ecosystem recovery in the degraded tropical landscape. This study highlights the roles of GIS and Remote Sensing for effective restoration in locating sites for assisting shrub encroachment, as well as for accelerating secondary succession where shrubland has already been established, and for managing natural succession by planting late successional tree species where the oldest forest pioneers are established.


    Figure 1: Patterns and processes of structural succession (natural and plantation) of landscape from a grassland dominated to a woodland dominated landscape.


    Figure 2: CCA ordination diagram: Showing the relationship of species with the environmental variables ( AspN = northeness (cos of aspect), Slp = slope, Elv = elevation, Curv = Curvature, CI = Convergence Index, C = Soil Carbon, C:N = Carbon and Nitrogen ratio), Geen horizontal lines indicate Aspect surface whereas vertical brown lines indicate elevation surface. Species are shown by “+”sign in grey color overlaid with abbreviated names of dominant species, and S=sites are shown with filled circles of different colors corresponding to age of the site (GT70 = greater than 70-year old forest (forest since 1945), LT70 = less than 70-year old forest (forest since 1963) LT52 = less than 52-year old forest (forest since 1989), LT26 = less than 26-year old forest (forest since 2001), and LT14 = less than 14-year old forest (forest since 2014).
    Alanchin = Alangium chinense, Ardiquin = Ardisia quinquegona, Biscjava = Bischofia javanica, Bridbala = Bridelia balansae, Camecaud = Camellia caudata, Camewald = Camellia sinensis var. waldenae, Castfabe = Castanopsis faberi, Castlamo = Castanopsis lamontii, Choeaxil = Choerospondias axillaris, Cornhong = Cornus hongkongensis, Cratcoch = Cratoxylum cochinchinense, Crottigl = Croton tiglium, Crypchin = Cryptocarya chinensis, Cunnlanc = Cunninghamia lanceolata, Desmchin = Desmos chinensis, Dioseria = Diospyros eriantha, Dipldubi = Diplospora dubia, Dysohong = Dysoxylum hongkongense, Elaelour = Elaeagnus loureiroi, Elaechin = Elaeocarpus chinensis, Elaedubi = Elaeocarpus dubius, Euonlaxi = Euonymus laxiflorus, Euontsoi = Euonymus tsoi, Eurygrof = Eurya groffi, Eurymaca = Eurya macartneyi, Euryniti = Eurya nitida, Ficufist = Ficus fistulosa, Ficuvari = Ficus variegata, Garcmult = Garcinia multiflora, Garcoblo = Garcinia oblongifolia, Glocerio = Glochidion eriocarpum, Glocwrig = Glochidion wrightii, Gordaxil = Gordonia axillaris, Helicoch = Helicia cochinchinensis, Ilexaspr = Ilex asprella, Ilexviri = Ilex viridis, Iteachin = Itea chinensis, Lasichin = Lasianthus chinensis, Lasivert = Lasianthus verticillatus, Ligujapo = Ligustrum japonicum, Liguliuk = Ligustrum liukiuense, Litsoblo = Litsea rotundifolia var. oblongifolia, Machchek = Machilus chekiangensis, Machchin = Machilus chinensis, Machkwan = Machilus kwangtungensis, Machpauh = Machilus pauhoi, Melamala = Melastoma malabathricum, Meliptel = Melicope pteleifolia, Meliford = Meliosma fordii, Melirigi = Meliosma rigida, Memeligu = Memecylon ligustrifolium, Myrssegu = Myrsine seguinii, Prunmont = Prunus arborea var. montana, Prunphae = Prunus phaeosticta, Psycasia = Psychotria asiatica, Sarcglab = Sarcandra glabra, Sarclaur = Sarcosperma laurinum, Sterlanc = Sterculia lanceolate, Sympanom = Symplocos anomala, Symplanc = Symplocos lancifolia, Syzyjamb = Syzygium jambos, Triacoch = Triadica cochinchinensis, Vibuodor = Viburnum odoratissimum.

Remote Sensing of Secondary Vegetation Succession in Hong Kong's Country Parks

This study aimed to use remotely sensed images to investigate the dynamics of secondary forest succession after a complete clearance of forest in Hong Kong during WWII. The study area (~2800 ha), included Tai Mo Shan and Shing Mun Country Parks in the New Territories of Hong Kong.


Other Research Projects

  • Augmented Teaching and Learning Advancement System
     
    Jockey Club Smart City Tree Management Project
     
    Identification of Rock Outcrops Using Remote Sensing Techniques
    Remote Sensing of Secondary Vegetation Succession in Hong Kong's Country Parks
  • Estimating Time-series of Anthropogenic Heat Flux at City Scale
    Characterization of Asian Dust Storms with Geostationary Satellites MTSAT
    iBeacon Positioning
     
     
    Land Use and Land Cover Mapping of Pearl River Delta region and Hong Kong
  • MOOC course: Introduction to Urban Geo-Informatics
     
     
    A UV-based Remote Sensing Technology For Sulphur Dioxide Detection And Monitoring From Ship Emissions
    Coastal Water Quality Monitoring in Hong Kong
     
     
    A Practical Application of Integrated Micro-Environmental Monitoring System for Construction Sites
  • 70 Years of Forest Succession in the Degraded Tropical Landscape of Hong Kong
    Impact of The Super Typhoon Manghkut on The Secondary Forest of Hong Kong
    Development of Hyperspectral Library to Distinguish Urban Tree Species in Hong Kong
    Remote Sensing of Forest Succession in Hong Kong's Country Parks
  • Modelling Woody Vegetation in Sudano-Sahe-lina Zone of Nigeria Using Remote Sensing
    LiDAR Technique Helps to Acquire Basic Tree Information
     
    Road Defect Detection Using Deep Learning Method
     
    Tree Thermal Image
     
     
  • Solar Energy Supply in Cloud-prone Areas of Hong Kong
     
     
    Brownfield Classification
     
    Establishment of Hong Kong AERONET Station
     
    Environmental adaptability of settlement
     
  • Assessing the Impact of Land Use Morphology on Air Pollution and Human Mobility for COVID-19 Incidence
     
    Development of AI-based algorithms for classification of tree species and retrieval of tree parameters using handheld laser scanning

     Estimation of solar irradiation on the urban building rooftop in Hong Kong
     
     
    An integrated knowledge-based Remote Sensing and GIS dynamic model for the urban thermal environment
     
  • Machine learning-based estimation of solar potential on three-dimensional urban envelopes
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