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Quantitative Assessment of Remotely Sensed Data for Landcover Change and Environmental Management

Domaine:

geospatialenvironment and energy

Type de record:

paper
Créateur:
InnMom
Éditeur:
Uni
Hôte:
Abstract This paper examines the relevance and application of quantitative techniques in geographic study with emphasis on landcover change and environmental management in a typical urban city of Warri and its environs in Nigeria. It uses an experimental study that adopts Principal Component Analysis (PCA) and Accuracy Assessment in reducing data dimensions and enhancing image visualization for onward classification into landcover classes using 1987 TM, 2002 ETM+ and ETM+ 2011. The 2011 ETM+ was later excluded due to scan line and cloud cover errors.  The PCA results show that 1987 Bands of 145 has variance of 834.71 (88.09% of total components) while the 2002 Bands of 147 has variance of 1287.21 (85.344% of total components). Supervised classification results show overall accuracy of 96.19% (for 1987) and 96.30% (for 2002) respectively. The study reveals that there was increase in urban landcover (17.2% to 34.93%) and swamp (10.11% to 11.61%). Correspondingly, light vegetation and thick vegetation decreased from 41.76% to 27.38% and 26.31% to 22.36% while water also reduced from 4.63% to 3.73%. The study indicates a higher demand for urban settlement which requires landuse control to avoid urban blight and environmental decay.  Abstrak Makalah ini membahas relevansi dan penerapan teknik kuantitatif dalam penelitian geografis dengan penekanan pada perubahan tutupan lahan dan pengelolaan lingkungan di kota urban khas Warri dan sekitarnya di Nigeria. Menggunakan studi eksperimental yang mengadopsi Principal Component Analysis (PCA) dan Penilaian Akurasi dalam mengurangi dimensi data dan meningkatkan visualisasi gambar untuk seterusnya klasifikasi ke dalam kelas tutupan lahan menggunakan 1987 TM 2002 ETM + dan ETM + 2011. 2011 ETM + kemudian dikeluarkan karena garis scan dan kesalahan awan. Hasil PCA menunjukkan bahwa 1.987 Bands dari 145 memiliki varians dari 834,71 (88,09% dari total komponen) sedangkan 2002 Bands dari 147 memiliki varians dari 1.287,21 (85,344% dari total komponen). Hasil klasifikasi diawasi menunjukkan akurasi keseluruhan masing-masing 96,19% (untuk 1987) dan 96,30% (tahun 2002). Penelitian ini mengungkapkan bahwa ada peningkatan tutupan lahan perkotaan (17,2% menjadi 34,93%) dan rawa (10.11% menjadi 11,61%). Sejalan dengan itu, vegetasi ringan dan vegetasi tebal menurun dari 41,76% menjadi 27,38% dan 26,31% untuk 22,36% sementara air juga berkurang dari 4,63% menjadi 3,73%. Studi ini menunjukkan permintaan yang lebih tinggi untuk pemukiman perkotaan yang membutuhkan kontrol penggunaan lahan untuk menghindari hawar perkotaan dan pembusukan lingkungan.

Visit

doi.org

Tasks

computer visionimage classification

Languages

IsekiriPévéSaya

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