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Generating automated forestry geoinformation products from remotely sensed imagery
Thesis (MSc)--Stellenbosch Unviersity, 2018.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2018
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| _version_ | 1867614097313366016 |
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| access_status_str | Open Access |
| author | Luck, Wolfgang |
| author2 | Van Niekerk, Adriaan |
| author_browse | Luck, Wolfgang Van Niekerk, Adriaan |
| author_facet | Van Niekerk, Adriaan Luck, Wolfgang |
| author_sort | Luck, Wolfgang |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MSc)--Stellenbosch Unviersity, 2018. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/105111 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:46:37.536Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/105111 Generating automated forestry geoinformation products from remotely sensed imagery Luck, Wolfgang Van Niekerk, Adriaan Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies. Landsat satellites Geoinformatics Forest management -- Spectral analysis Remote-sensing images Forest management -- Geographic information systems Forest management -- GIS Landsat TM ETM+ UCTD Thesis (MSc)--Stellenbosch Unviersity, 2018. ENGLISH ABSTRACT: Private industry, national government departments, and the international community rely on geoinformation to optimise activities, enforce legislation, and assess global markets concerned with the use of natural resources. One of the main challenges faced by the remote sensing community is to derive thematic information from available imagery at the required speed, consistency, quality, and cost. The international space science community has organised itself within the Committee on Earth Observation Satellites to combine information systems for providing global information services as part of the resulting Global Earth Observation System of Systems (GEOSS). The European Union also initiated a programme called Copernicus, formally known as Global Monitoring for Environment and Security (GMES), which aims to provide information services as a contribution to GEOSS. Copernicus/GMES provides three core services consisting of land, marine, and emergency response, and two pilot services relating to the atmosphere and security aspects; each core service consists of several service elements. The forest monitoring programme is part of the land core services. At present only Landsat archives provide a consistent and affordable data source to serve the land core services on local and regional levels. This is particularly true for all services addressing the thematic field of forestry, as the relatively low vegetation dynamics of forestry (compared to other cultivated crops) can be served by Landsat data as it optimally provides one cloud-free (cloudless) observation (not necessarily cloud-free scene) per season. Landsat data is also freely available from the United States Geological Survey (USGS) and several other ground receiving stations around the world. This makes it financially viable to use Landsat data for providing operational services that would otherwise be too expensive to deliver using commercially purchased satellite imagery. Another advantage of Landsat data is that it is well calibrated, and includes multispectral bands covering the full passive remote sensing spectrum from blue light to thermal radiation. While the blue band is useful for the characterisation of atmospheric effects, the red, near-infrared, and shortwave infrared bands are suitable for the characterisation of different vegetation types, in particular forests. The spatial resolution of Landsat imagery (15–30 m) is also suitable for forestry applications, in particular when the monitoring of tree clusters instead of individual trees is required. Given these unique attributes of Landsat imagery, this study focused on the development of a processing chain for the automatic extraction of forestry geoinformation products from Landsat thematic mapper (TM)/enhanced thematic mapper (ETM)+ imagery. The products generated consist of a plantation and indigenous forest mask, and a broad genus classification for plantation forests. The products were generated and validated in three regions in South Africa, namely Cape Town (Western Cape Province); the Natal Midlands (KwaZulu-Natal Province); and the eastern escarpment and Lowveld region (Limpopo and Mpumalanga provinces). The results show that the products have an overall accuracy exceeding 93% for all areas, which will make them useful for forestry operations and planning. Although the resulting forestry products are evaluated in a South African context, the methodology can also be applied in other regions. The methods can also be adapted for application on other data sources such as those offered by the recently-launched Copernicus Sentinel 2 satellite and other commercially-operated satellites such as RapidEye, Resourcesat, and Spot. AFRIKAANSE OPSOMMING: Die privaatsektor, nasionale regeringsdepartemente en die internasionale gemeenskap maak staat op geoinligting om aktiwiteite te optimaliseer, die nakoming van wetgewing af te dwing, en toegang te verkry tot globale markte wat verband hou met die gebruik van natuurlike hulpbronne. Een van die grootste uitdagings van die afstandwaarnemings-gemeenskap is om tematiese inligting van beskikbare beelde teen die vereiste spoed, konsekwentheid, kwaliteit en koste te bekom. Die internasionale ruimtewetenskapgemeenskap het hulself binne die Komitee vir Aardobservasie-satteliete (KAOS) georganiseer ten einde inligtingstelsels te kombineer om globale inligtingsdienste as deel van die gevolglike Globale Aardobservasiestelsel van Stelsels (GAOSS) te verskaf. Die Europese Unie het ook met ’n program, Copernicus (voorheen bekend as Globale Monitering vir Omgewing en Sekuriteit (GMOS)), begin wat daarop gemik is om inligting as ’n bydrae tot GEOSS te verskaf. Copernicus/GMOS bied drie kerndienste, naamlik land-, mariene- en noodreaksies, asook twee loodsdienste wat betrekking het op atmosferiese- en sekuriteitsaspekte. Elke kerndiens bestaan uit verskeie dienselemente en die bosbou-moniteringsprogram is ʼn deel van die landkerndienste. Tans voorsien slegs Landsat-argiewe ’n konsekwente en bekostigbare databron om die landkerndienste op plaaslike- en streeksvlakke te dien. Dit is veral waar vir alle dienste wat die tematiese gebied van bosbou aanspreek omdat die relatief lae plantegroei-dinamika van bosbou (in vergelyking met ander verboude gewasse) goed deur Landsat-data beskryf word. Onder optimale omstandighede word meer as een wolkvrye (wolklose) waarneming (nie noodwendig ’n wolkvrye beeld nie) per seisoen oor bosbougebiede gemaak. Landsat-data is ook vrylik beskikbaar van die Verenigde State se Geologiese Opname (VSGO) en van verskeie ander grond-ontvangstasies regoor die wêreld. Dit maak dit finansieel lewensvatbaar om Landsat-data te gebruik vir die verskaffing van operasionele dienste wat te duur sou wees om te voorsien indien satellietdata kommersieël aangekoop is. Nog ’n voordeel van Landsat-data is dat dit goed gekalibreer is en multispektrale bande insluit wat die volle passiewe afstandswaarnemingspektrum, van blou lig tot termiese straling, dek. Die blou band is nuttig vir die karakterisering van atmosferiese effekte, terwyl die rooi, nabye-infrarooi en kortgolf-infrarooi bande geskik vir die karakterisering van verskillende tipes plantegroei, veral woude. Die ruimtelike resolusie van Landsat-beelde (15–30 m) is ook geskik vir bosboutoepassings, veral waar die monitering van groeperinge in bome in plaas van individuele bome vereis word. Gegewe hierdie unieke eienskappe van Landsat-beelde het hierdie studie op die ontwikkeling van ’n verwerkingsketting vir die outomatiese onttrekking van bosbou-geoinligtingsprodukte van Landsat TM/ETM+ beelde gefokus. Die geskepte produkte bestaan uit ’n plantasie en inheemse woudmasker en 'n breë genusklassifikasie vir bosbouplantasies. Die produkte is gegenereer en geëvalueer in drie streke in Suid-Afrika, naamlik Kaapstad (Wes-Kaap Provinsie); die Natal Midland (KwaZulu-Natal Provinsie); en die Oos-platorand en Laeveld streek (Limpopo en Mpumalanga provinsies). Die resultate toon dat die produkte hoogs akkuraat is met algemene akkuraatheid van oor die 93% vir al die areas, wat hulle baie nuttig sal maak vir bosboubedrywighede en -beplanning. Alhoewel die gevolglike bosbouprodukte in ’n Suid-Afrikaanse konteks geëvalueer word, kan hulle ook in ander wêreldstreke toegepas word. Die metodes kan ook aangepas word vir toepassing op ander databronne soos die onlangs-gelanseerde Copernicus Sentinel-2-satelliet, asook ander kommersieel-bedrewe satelliete soos RapidEye, Resourcesat en Spot. Masters 2018-11-28T07:31:30Z 2018-12-07T07:00:02Z 2018-11-28T07:31:30Z 2018-12-07T07:00:02Z 2018-12 Thesis http://hdl.handle.net/10019.1/105111 en_ZA Stellenbosch University xxix, 153 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Landsat satellites Geoinformatics Forest management -- Spectral analysis Remote-sensing images Forest management -- Geographic information systems Forest management -- GIS Landsat TM ETM+ UCTD Luck, Wolfgang Generating automated forestry geoinformation products from remotely sensed imagery |
| title | Generating automated forestry geoinformation products from remotely sensed imagery |
| title_full | Generating automated forestry geoinformation products from remotely sensed imagery |
| title_fullStr | Generating automated forestry geoinformation products from remotely sensed imagery |
| title_full_unstemmed | Generating automated forestry geoinformation products from remotely sensed imagery |
| title_short | Generating automated forestry geoinformation products from remotely sensed imagery |
| title_sort | generating automated forestry geoinformation products from remotely sensed imagery |
| topic | Landsat satellites Geoinformatics Forest management -- Spectral analysis Remote-sensing images Forest management -- Geographic information systems Forest management -- GIS Landsat TM ETM+ UCTD |
| url | http://hdl.handle.net/10019.1/105111 |
| work_keys_str_mv | AT luckwolfgang generatingautomatedforestrygeoinformationproductsfromremotelysensedimagery |