An Assessment of the Spectra Characteristics of Vegetation in South Western Nigeria

Omodanisi, E. O.; Salami, Ayobami T.

doi:10.1016/j.ieri.2014.09.036

Cited by 5 publications

(4 citation statements)

References 10 publications

(4 reference statements)

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“…Several studies have established the use of spectral indices to assess the greenness, health status and chlorophyll content of forest vegetation which is done by using various band combination for its computation [33][34][35][36]. In this study, five spectral indices were performed using Landsat images of 1999 to 2019 for this study.…”

Section: Spectral Indicesmentioning

confidence: 99%

Green Space Assessment and Management in Biscay Province, Spain using Remote Sensing Technology

Makinde

Martin

Vicario

2021

GaEE

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Our ecosystem, particularly forest lands, contains huge amounts of carbon storage in the world today. This study estimated the above ground biomass and carbon stock in the green space of Bilbao Spain using remote sensing technology. Landsat ETM+ and OLI satellite images for year 1999, 2009 and 2019 were used to assess its land use land cover (LULC), change detection, spectral indices and model biomass based on linear regression. The result of the LULC showed that there was an increase in forest vegetation by 12.5% from 1999 to 2009 and a further increase by 2.3% in 2019. However, plantation cover had decreased by 3.5% from 1999–2009; while wetlands had also decreased by 9% within the same period. There was, however, an increase in plantation cover from 2009 to 2019 by 2.1% but a further decrease in wetlands of 4.3%. Further results revealed a positive correlation across the three decades between the widely used Normalized Differential Vegetation Index (NDVI) with other spectral indices such as Enhance Vegetation Index (EVI) and Normalized Differential Moisture Index (NDMI) for biomass were: for 1999 EVI (R2 = 0.1826), NDMI (R2 = 0.0117), for 2009 EVI (R2 = 0.2192), NDMI (R2 = 0.3322), for 2019EVI (R2 = 0.1258), NDMI (R2 = 0.8148). A reduction in the total carbon stock from 14,221.94 megatons in 1999 to 10,342.44 megatons 2019 was observed. This study concluded that there has been a reduction in the amount of carbon which the Biscay Forest can sequester.

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Section: Spectral Indicesmentioning

confidence: 99%

Green Space Assessment and Management in Biscay Province, Spain using Remote Sensing Technology

Makinde

Martin

Vicario

2021

GaEE

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“…Pada dasarnya analisis indeks vegetasi memanfaatkan panjang gelombang biru, hijau, merah dan inframerah dekat. Adanya penurunan kesehatan vegetasi (seperti vegetasi stress) akibat gangguan dari akumulasi rembesan migas ataupun gangguan lainnya akan mengubah pola spektral yang menjadi turun pada Inframerah dekat (Yang, 1999;Noomen, 2007;Omodanisi dan Salami, 2014). Selain itu pada panjang gelombang tampak pantulan meningkat dan berubah dari posisi yang seharusnya sehingga terjadi pergeseran batas tepi panjang gelombang merah (Smith dkk., 2004).…”

Section: Anomali Kondisi Vegetasiunclassified

Pemanfaatan Teknologi Light Detection and Ranging (Lidar) Dalam Pemodelan Banjir Akibat Luapan Air Sungai

Wikantika¹,

Harto²,

asriyah³

2017

Preprint

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Penelitian ini bertujuan untuk membuat model genangan banjir akibat luapan air sungai Cikapundung dari data DEM hasil pengukuran teknologi LIDAR untuk menunjukkan kawasan berpotensi banjir sehingga dapat membantu dalam upaya mitigasi bencana banjir.Analisis pemodelan banjir memerlukan data topografi yang akurat untuk mendukung kualitas hasil pemodelan banjir. Saat ini, LIDAR merupakan teknologi penginderaan jauh terbaik yang digunakan untuk membuat relief permukaan bumi dalam bentuk tiga dimensi yang dimana sering disebut sebagai DEM (Digital Elevation Model). Tugas akhir ini membahas tentang pemanfaatan teknologi LIDAR dalam analisis genangan banjir akibat luapan sungai berdasarkan simulasi model hidrodinamik. Pemetaan genangan banjir berdasarkan simulasi model hidrodinamik dapat memberikan gambaran yang cukup baik mengenai daerah-daerah di sekitar sungai yang berpotensi tergenang banjir. Hasil pemetaan genangan banjir dalam penelitian ini sangat bermanfaat untuk upaya mitigasi bencana banjir pada daerah studi.

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“…The probe was positioned 2.2 m above the measured quadrat, which resulted in a 1-m diameter field of view for the measurement. The mixed reflectance spectra of S. chamaejasme-grass-soil and grass-soil were acquired [45,46]. …”

Section: Field Spectra Measurementsmentioning

confidence: 99%

IKONOS Image-Based Extraction of the Distribution Area of Stellera chamaejasme L. in Qilian County of Qinghai Province, China

Liu

et al. 2016

Remote Sensing

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Stellera chamaejasme L. (S. chamaejasme) is one of the primary toxic grass species (poisonous plants) distributed in the alpine meadows of Qinghai Province, China. In this study, according to the distinctive phenological characteristics of S. chamaejasme, the spectral differences between S. chamaejasme in the full-bloom stage and other pasture grasses were analyzed and the red, blue, and near-infrared bands of IKONOS image were determined as the diagnostic bands of S. chamaejasme recognition. Feature indexes related to S. chamaejasme were established using the diagnostic bands, and NDV I blue " pρ nir´ρblue q { pρ nir`ρblue q obtained as S. chamaejasme sensitive index based on the linear regression analysis between the indexes derived from field spectra and the actual cover fraction of S. chamaejasme communities. The distribution area of S. chamaejasme was extracted by using the index NDV I blue derived from IKONOS multispectral image in Qilian County of Qinghai Province, China and the verified result reached an overall accuracy of 90.71%. The study indicated that high resolution multispectral satellite images (such as IKONOS images) had significant potential in remote sensing recognition of toxic grass species.

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An Assessment of the Spectra Characteristics of Vegetation in South Western Nigeria

Cited by 5 publications

References 10 publications

Green Space Assessment and Management in Biscay Province, Spain using Remote Sensing Technology

Green Space Assessment and Management in Biscay Province, Spain using Remote Sensing Technology

Pemanfaatan Teknologi Light Detection and Ranging (Lidar) Dalam Pemodelan Banjir Akibat Luapan Air Sungai

IKONOS Image-Based Extraction of the Distribution Area of Stellera chamaejasme L. in Qilian County of Qinghai Province, China

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