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首页> 外文期刊>Journal of Forest Planning >Detection of Afforestation, Reforestation and Deforestation (ARD) by Visual Photo Interpretation of High Spatial Resolution Images - A Fundamental Case Study -
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Detection of Afforestation, Reforestation and Deforestation (ARD) by Visual Photo Interpretation of High Spatial Resolution Images - A Fundamental Case Study -

机译：通过高分辨率空间图像的可视图像解释检测造林，再造林和毁林（ARD）-一个基本案例研究-

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Kyoto Protocol Article 3.3 calls for the identification of land use changes to better evaluate emissions and sinks of greenhouse gases. Here we investigated the ability of high spatial resolution imagery to examine changes in afforestation, reforestation and deforestation (ARD). Initially, we checked capability of the imagery in identifying land cover types by visual interpretation. Although digital orthophotos were capable of identifying various land-covers, a panchromatic HRV (HRV-P) image wasn't able to distinguish between young forests, crop fields and grasslands due to poorer picture quality in grey levels and spatial resolution than orthophotos. The poor quality of HRV-P images caused disagreement of ground object identification with the orthophotos by approximately 20-30% in the visual interpretation. Taking into the picture quality and the error causes into consideration, we visually interpreted ARD in Kumamoto prefecture using orthophotos for the beginning of the period and HRV-P images for the end of the period. A field validation showed that user's accuracy of detecting deforestation (D) was 92%, and that of detecting afforestation and reforestation (AR) was barely 50%. Although it was impossible to separate land-use changes from land-cover changes perfectly by visual interpretation, detecting deforestation was very accurate. The major causes of errors were interpreting cut-overs as D, and interpreting crop fields and grasslands as forest on the HRV-P images. Annual occurrence rates for AR and D were 0.001% and 0.048% of the land area, respectively. Annual occurrence of D was 0.032% of the land area according to a census. The interpreted D area was consistent with the census information. Thus ARD detection by visual photo interpretation, which clearly shows human-induced ARD areas, is suitable for meeting the monitoring requirements of the Kyoto Protocol. The interpretation using a Geographical Information System was better at identifying ARD areas because it used high resolution images with geographical coordinates, since the land-use change type and their locations were identifiable. We expect to be able to estimate total of land-use change areas with spatial information by decreasing the uncertainties in ARD visual interpretationand by introducing an efficient sampling method, which can estimate country-wide changes.
机译：《京都议定书》第3.3条要求确定土地用途的变化，以更好地评估温室气体的排放和汇。在这里，我们研究了高空间分辨率影像检查造林，再造林和毁林（ARD）变化的能力。最初，我们通过视觉解释检查了图像识别土地覆盖类型的能力。尽管数字正射照片能够识别各种土地覆盖物，但由于全色HRV（HRV-P）图像的灰度和空间分辨率均比正射照片差，因此无法区分年轻的森林，农田和草地。 HRV-P图像的质量较差，导致在视觉解释中地面物体与正射照片的识别不一致约20-30％。考虑到图像质量和错误原因，我们在视觉上解释了熊本县的ARD，使用正射照片作为周期的开始，并使用HRV-P图像作为周期的结束。现场验证表明，用户检测到森林砍伐（D）的准确度为92％，而检测到森林砍伐和再造林（AR）的准确度仅为50％。尽管不可能通过视觉解释将土地利用变化与土地覆盖变化完美地分开，但检测到的森林砍伐是非常准确的。错误的主要原因是在HRV-P图像上将割接区解释为D，并将耕地和草地解释为森林。 AR和D的年发生率分别为土地面积的0.001％和0.048％。根据人口普查，D的年发生率为土地面积的0.032％。解释的D区域与人口普查信息一致。因此，通过可视照片判读进行的ARD检测可以清楚地显示人为引起的ARD区域，适合满足《京都议定书》的监控要求。使用地理信息系统进行的解释更好地识别了ARD区域，因为它使用了具有地理坐标的高分辨率图像，因为土地用途的变化类型及其位置是可识别的。我们希望能够通过减少ARD视觉解释的不确定性并引入一种可以估算全国范围变化的有效采样方法，来利用空间信息来估算土地利用变化区域的总量。

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