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基于无人机的异质土壤景观碳输出量估算——以CarboZALF试验区为例

UAV-Based Estimation of Carbon Exports from Heterogeneous Soil Landscapes--A Case Study from the CarboZALF Experimental Area.

作者信息

Wehrhan Marc, Rauneker Philipp, Sommer Michael

机构信息

Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Soil Landscape Research, Eberswalder Straße 84, Müncheberg 15374, Germany.

Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Landscape Hydrology, Eberswalder Straße 84, Müncheberg 15374, Germany.

出版信息

Sensors (Basel). 2016 Feb 19;16(2):255. doi: 10.3390/s16020255.

DOI:10.3390/s16020255
PMID:26907284
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4801631/
Abstract

The advantages of remote sensing using Unmanned Aerial Vehicles (UAVs) are a high spatial resolution of images, temporal flexibility and narrow-band spectral data from different wavelengths domains. This enables the detection of spatio-temporal dynamics of environmental variables, like plant-related carbon dynamics in agricultural landscapes. In this paper, we quantify spatial patterns of fresh phytomass and related carbon (C) export using imagery captured by a 12-band multispectral camera mounted on the fixed wing UAV Carolo P360. The study was performed in 2014 at the experimental area CarboZALF-D in NE Germany. From radiometrically corrected and calibrated images of lucerne (Medicago sativa), the performance of four commonly used vegetation indices (VIs) was tested using band combinations of six near-infrared bands. The highest correlation between ground-based measurements of fresh phytomass of lucerne and VIs was obtained for the Enhanced Vegetation Index (EVI) using near-infrared band b899. The resulting map was transformed into dry phytomass and finally upscaled to total C export by harvest. The observed spatial variability at field- and plot-scale could be attributed to small-scale soil heterogeneity in part.

摘要

使用无人机进行遥感的优势在于图像具有高空间分辨率、时间灵活性以及来自不同波长域的窄带光谱数据。这使得能够检测环境变量的时空动态，例如农业景观中与植物相关的碳动态。在本文中，我们使用安装在固定翼无人机Carolo P360上的12波段多光谱相机拍摄的图像，对新鲜植物生物量和相关碳（C）输出的空间模式进行了量化。该研究于2014年在德国东北部的CarboZALF-D试验区进行。从经辐射校正和校准的紫花苜蓿（Medicago sativa）图像中，使用六个近红外波段的波段组合测试了四种常用植被指数（VI）的性能。使用近红外波段b899的增强植被指数（EVI）在紫花苜蓿新鲜植物生物量的地面测量值与植被指数之间获得了最高相关性。由此生成的地图被转换为干植物生物量，最终按收获量放大到总碳输出量。在田间和地块尺度上观察到的空间变异性部分可归因于小规模土壤异质性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6fa/4801631/51725614c012/sensors-16-00255-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6fa/4801631/ad60c6995e36/sensors-16-00255-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6fa/4801631/4da7448d65a2/sensors-16-00255-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6fa/4801631/56a5ad9fbcad/sensors-16-00255-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6fa/4801631/41924e870092/sensors-16-00255-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6fa/4801631/51725614c012/sensors-16-00255-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6fa/4801631/ad60c6995e36/sensors-16-00255-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6fa/4801631/dff5618e6483/sensors-16-00255-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6fa/4801631/aa6cdb8aeacb/sensors-16-00255-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6fa/4801631/361733410df8/sensors-16-00255-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6fa/4801631/4da7448d65a2/sensors-16-00255-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6fa/4801631/56a5ad9fbcad/sensors-16-00255-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6fa/4801631/41924e870092/sensors-16-00255-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6fa/4801631/51725614c012/sensors-16-00255-g019.jpg

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本文引用的文献

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Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images.利用基于对象的无人机 (UAV) 图像分析进行早春玉米田杂草制图。
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3
Vignette and exposure calibration and compensation.
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