Hedgerow map of Bavaria, Germany, based on orthophotos and convolutional neural networks

IF 3.8 Q2 ENVIRONMENTAL SCIENCES
Verena Huber-García , Jennifer Kriese , Sarah Asam , Mariel Dirscherl , Michael Stellmach , Johanna Buchner , Kristel Kerler , Ursula Gessner
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引用次数: 0

Abstract

Hedgerows play a significant role in biodiversity preservation, carbon sequestration, soil stability and the ecological integrity of rural landscapes. Understanding their current condition and future development is therefore crucial for a range of stakeholders such as municipalities, state agencies or environmental organizations. The wall-to-wall mapping and characterization of hedgerows in-situ is, however, very labour-, time- and cost-intensive. This impedes a regular monitoring at adequate intervals. In the Federal State of Bavaria, Germany, the hedgerow biotope mapping is repeated every 20–30 years for each district. State-wide consistent and up-to-date data are hence not available. In this study we present an approach for mapping all hedgerows in Bavaria using orthophotos and deep learning. We used hedgerow polygons of the federal in-situ biotope mapping from 5 focus districts as well as additional manually digitized polygons as training and test data and orthophotos as input in a DeepLabV3 Convolutional Neural Network (CNN). The CNN has a Resnet50 Backbone and was optimized using the Dice loss as cost function. The orthophotos were acquired in 2019–2021. They have a spatial resolution of 20 cm and were fed to the CNN at tiles of 125 × 125 m. The generated hedgerow probability tiles were post-processed through merging and averaging the overlapping tile boarders, shape simplification and filtering. The resulting hedgerow vector data set achieved medium overall accuracies (precision = 0.43, recall = 0.53, F1-score = 0.48). The model generally overestimated the number of hedgerows, and hedgerows were often confused with riparian as well as urban vegetation. Looking at each hedgerow polygon individually, the mapping accuracy varied considerably, with a median F1-score of 0.51 for all detected objects. In addition, we found differences in accuracies among districts in different landscapes. For example, the Hassberge district, a landscape rich of hedgerows, reached a F1-score of 0.61. A comprehensive comparison with the Copernicus High Resolution Layer (HRL) Small Woody Features (SWF) revealed significant differences between the datasets. About 43 % of the hedgerows in our data set were not present in the SWF layer. Especially narrow, elongated vegetated structures are not captured in the SWF product. This highlights the potential to use our state-wide hedgerow map of Bavaria in combination with the SWF dataset, but also by itself, for a range of administrative, statistical and nature conservation applications.

Abstract Image

基于正射影像和卷积神经网络的德国巴伐利亚树篱地图
植物篱在生物多样性保护、固碳、土壤稳定和乡村景观生态完整性等方面发挥着重要作用。因此,了解它们的现状和未来发展对市政当局、国家机构或环境组织等一系列利益相关者至关重要。然而,在现场对树篱进行墙到墙的测绘和表征是非常耗费人力、时间和成本的。这妨碍了以适当的间隔进行定期监测。在德国巴伐利亚联邦州,每个地区每20-30年重复一次树篱生物群落地图。因此,没有全国性的一致和最新的数据。在这项研究中,我们提出了一种使用正射影像和深度学习来绘制巴伐利亚所有树篱的方法。我们使用了来自5个重点地区的联邦原位生物群落地图的灌木树冠多边形,以及额外的人工数字化多边形作为训练和测试数据,并将正射影像作为DeepLabV3卷积神经网络(CNN)的输入。CNN有一个Resnet50骨干网,并使用Dice损失作为成本函数进行优化。这些正射影像是在2019-2021年获得的。它们的空间分辨率为20厘米,以125 × 125米的尺寸馈送给CNN。对生成的树篱概率块进行后处理,对重叠块边界进行合并平均、形状简化和滤波。所得到的篱流向量数据集达到了中等的总体准确率(精密度= 0.43,召回率= 0.53,F1-score = 0.48)。该模型普遍高估了植物篱的数量,并且经常将植物篱与河岸植被和城市植被混淆。单独观察每个树篱多边形,映射精度变化很大,所有检测到的物体的f1得分中位数为0.51。此外,我们发现在不同的景观中,不同地区的精度存在差异。例如,哈斯伯格区,一个景观丰富的绿篱,达到f1得分0.61。与哥白尼高分辨率层(HRL)小木本特征(SWF)进行综合比较,发现数据集之间存在显著差异。在我们的数据集中,大约43%的树篱没有出现在SWF层中。特别是狭窄、细长的植被结构在SWF产品中没有被捕获。这凸显了将我们的巴伐利亚州树篱地图与SWF数据集结合使用的潜力,也凸显了将其单独用于一系列行政、统计和自然保护应用的潜力。
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来源期刊
CiteScore
8.00
自引率
8.50%
发文量
204
审稿时长
65 days
期刊介绍: The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems
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