利用风筝航拍技术监测沿海边缘生境:基于遥感的案例研究

B. Madurapperuma, J. Dellysse
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引用次数: 6

摘要

利用中等分辨率的陆地卫星图像监测沿海生态系统的气候和/或人为脆弱性复原力具有挑战性。一个简单、低成本的风筝航空摄影平台(KAP)对于获得小区域的高分辨率图像以开发沿海GIS模型至关重要。本研究从植被生物屏障质量和海平面上升的角度考察了两个沿海灌木生态系统和红树林生态系统的海啸后救济。KAP平台由两个带有双带通红-近红外滤光片的轻型自动相机、Picavet稳定装置、GPS跟踪器和Parafoil Kite组成。利用结构-运动(SFM)和遥感软件(Agisoft PhotoScan和ENVI)对KAP图像进行处理,构建马赛克图像、正校正和地理参考数字高程模型(DEM)。KAP已在三种情况下用于沿海制图:(i)针对外来入侵物种刺槐(Prosopis juliflora)的目标特征提取和用于沿海灌木和草本植被分类的纹理分析;(ii)用于海平面上升的DEM; (iii)用于红树林生物屏障质量估算的归一化植被指数(NDVI)。图像处理得到平均密度为35点/m2的点云;分辨率为17 cm的DEM;以及平均分辨率为4.0厘米的正射影像马赛克。结果表明,目标导向特征提取对滨缘灌木和毛豆的分类准确率为62%,监督分类准确率为51%。当NDVI阈值≥4时,Rekawa的红树林植被与草地和其他沿海灌木植被类型区分,红树林面积为0.33 ha(占总面积1.15 ha的28%)。Kahandamodara海滩沿岸植被以Ipomoea pes-capre为主,盖度为26%。总之,KAP具有广泛的潜力,可以将科学与高时空分辨率的原位数据连接起来,用于沿海栖息地制图,研究人员可以在低成本的预算内利用这些数据。关键词:风筝测绘,海岸,DEM,红树林,NDVI
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Coastal Fringe Habitat Monitoring using Kite Aerial Photography: A Remote Sensing-based Case Study
Monitoring coastal ecosystem resilience for climatic and/or anthropogenic vulnerabilities is challenging with moderately resolution Landsat images. A simple, low-cost Kite Aerial Photograph platform (KAP) was vital to obtain high-resolution images for a small area to develop coastal GIS models. This study examines post-tsunami relief in two coastal shrub ecosystem and a mangrove ecosystem in terms of vegetation bioshield mass and sea level rise perspectives. A KAP platform was created using two light-weight automatic cameras with dual bandpass Red-NIR filters, a Picavet stabilizing rig, a GPS tracker and a Parafoil Kite. The KAP images were processed to build mosaic images, orthorectified and geo-referenced Digital Elevation Model (DEM) using structure-from motion (SFM) and remote sensing software (Agisoft PhotoScan and ENVI respectively). KAP has been utilised for coastal mapping under three scenarios: (i) object-orient feature extraction for discriminate Prosopis juliflora, an invasive alien species, and texture analysis for coastal shrub and herbaceous vegetation classification (ii) DEM for sea level rise, and (iii) Normalized Difference Vegetation Index (NDVI) for mangrove bioshield mass estimation. The image processing produced a point cloud with an average density of 35 points/m2; a DEM with 17 cm resolution; and an orthophoto mosaic with an average resolution of 4.0 cm. The results showed that object orient feature extraction can discriminate Prosopis juliflora from the coastal shrubs with 62% accuracy, while supervised classification accuracy was 51%. Mangrove vegetation in Rekawa was discriminated from grassland and other coastal shrub vegetation types at ≥4 NDVI threshold resulted in 0.33 ha of mangroves (28% of 1.15 ha of the total area). The Kahandamodara beach coastal vegetation was dominant by Ipomoea pes-capre with 26% coverage. In conclusion, KAP has a wide potential to bridge science with high spatial/temporal resolution in-situ data for coastal habitat mapping, where the researchers can utilize the data within a low-cost budget.Keywords: kite mapping, coast, DEM, mangrove, NDVI
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