A remote sensing-guided forest inventory concept using multispectral 3D and height information from ZiYuan-3 satellite data

Wallner, Adelheid; Friedrich, Stefan; Geier, Emanuel; Meder-Hokamp, Christoph; Wei, Zillin; Kindu, Mengistie; Tian, Jiaojiao; Döllerer, Martin; Schneider, Thomas; Knoke, Thomas

doi:10.1093/forestry/cpab055

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…Due to high dynamics in the forest's characteristics and introduced climate change mitigation, there is currently a strong need to develop multi-purpose decision-support tools for forest management as well as management practices that would rely not only on traditional forest inventories but also incorporate remote sensing datasets [64]. Thus, to meet the increasing demand for information on forest resources and their condition, the forest inventories have to become also multi-source [65]. However, operative forest inventories are time and resource-consuming.…”

Section: Discussionmentioning

confidence: 99%

Predicting Species and Structural Diversity of Temperate Forests with Satellite Remote Sensing and Deep Learning

2022

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Anthropogenically-driven climate change, land-use changes, and related biodiversity losses are threatening the capability of forests to provide a variety of valuable ecosystem services. The magnitude and diversity of these services are governed by tree species richness and structural complexity as essential regulators of forest biodiversity. Sound conservation and sustainable management strategies rely on information from biodiversity indicators that is conventionally derived by field-based, periodical inventory campaigns. However, these data are usually site-specific and not spatially explicit, hampering their use for large-scale monitoring applications. Therefore, the main objective of our study was to build a robust method for spatially explicit modeling of biodiversity variables across temperate forest types using open-access satellite data and deep learning models. Field data were obtained from the Biodiversity Exploratories, a research infrastructure platform that supports ecological research in Germany. A total of 150 forest plots were sampled between 2014 and 2018, covering a broad range of environmental and forest management gradients across Germany. From field data, we derived key indicators of tree species diversity (Shannon Wiener Index) and structural heterogeneity (standard deviation of tree diameter) as proxies of forest biodiversity. Deep neural networks were used to predict the selected biodiversity variables based on Sentinel-1 and Sentinel-2 images from 2017. Predictions of tree diameter variation achieved good accuracy (r2 = 0.51) using Sentinel-1 winter-based backscatter data. The best models of species diversity used a set of Sentinel-1 and Sentinel-2 features but achieved lower accuracies (r2 = 0.25). Our results demonstrate the potential of deep learning and satellite remote sensing to predict forest parameters across a broad range of environmental and management gradients at the landscape scale, in contrast to most studies that focus on very homogeneous settings. These highly generalizable and spatially continuous models can be used for monitoring ecosystem status and functions, contributing to sustainable management practices, and answering complex ecological questions.

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Section: Discussionmentioning

confidence: 99%

Predicting Species and Structural Diversity of Temperate Forests with Satellite Remote Sensing and Deep Learning

2022

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“…Wallner et al 16 addressed the dynamic and impactful changes occurring in the Central European forest ecosystem, driven by climate uncertainties and shifts in weather patterns. In response to this, they employed satellite data from ZiYuan-3 (ZY-3) within a Remote Sensing-guided Forest inventory framework.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing based forest cover classification using machine learning

Aziz,

Minallah,

Saeed

et al. 2024

Sci Rep

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Pakistan falls significantly below the recommended forest coverage level of 20 to 30 percent of total area, with less than 6 percent of its land under forest cover. This deficiency is primarily attributed to illicit deforestation for wood and charcoal, coupled with a failure to embrace advanced techniques for forest estimation, monitoring, and supervision. Remote sensing techniques leveraging Sentinel-2 satellite images were employed. Both single-layer stacked images and temporal layer stacked images from various dates were utilized for forest classification. The application of an artificial neural network (ANN) supervised classification algorithm yielded notable results. Using a single-layer stacked image from Sentinel-2, an impressive 91.37% training overall accuracy and 0.865 kappa coefficient were achieved, along with 93.77% testing overall accuracy and a 0.902 kappa coefficient. Furthermore, the temporal layer stacked image approach demonstrated even better results. This method yielded 98.07% overall training accuracy, 97.75% overall testing accuracy, and kappa coefficients of 0.970 and 0.965, respectively. The random forest (RF) algorithm, when applied, achieved 99.12% overall training accuracy, 92.90% testing accuracy, and kappa coefficients of 0.986 and 0.882. Notably, with the temporal layer stacked image of the Sentinel-2 satellite, the RF algorithm reached exceptional performance with 99.79% training accuracy, 96.98% validation accuracy, and kappa coefficients of 0.996 and 0.954. In terms of forest cover estimation, the ANN algorithm identified 31.07% total forest coverage in the District Abbottabad region. In comparison, the RF algorithm recorded a slightly higher 31.17% of the total forested area. This research highlights the potential of advanced remote sensing techniques and machine learning algorithms in improving forest cover assessment and monitoring strategies.

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“…Furthermore, it retains a high level of information redundancy among images. This inherent characteristic establishes a more robust foundation for the generation of three-dimensional terrain data, thereby facilitating subsequent applications such as urban 3D modeling and planning [1], land change detection [2], and forest management [3].…”

Section: Introductionmentioning

confidence: 99%

Highly Applicable Iterative Network for 3-D Reconstruction Based on Multiview Satellite Images

Hong,

Yang,

Pan

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Multi-view satellite images play a crucial role in reconstructing Earth's surface information. Its application in deep learning-based Multi-View Stereo (MVS) methods has recently exhibited impressive performance. However, there is still ample room for improvement, particularly in the reconstruction of the Digital Surface Model (DSM) within scenes characterized by dense features, high repetition, and significant elevation variations. To address the challenges, we present a novel network, Appli-SatNet. The network initially introduces a multi-scale Transformer module to emphasize feature correlations within and across multiple images. Subsequently, a novel Gated Recurrent Units (GRU) iterative optimizer is introduced to derive iterative indices for the cost volume within implicit states. This iterative process enhances the correlation among the cost volumes and enables multiple iterative refinements for prediction elevation maps. Ultimately, this leads to the generation of a high-resolution DSM that matches the resolution of the nadir image. The method is validated on a publicly available TLC SatMVS dataset, which includes plain areas with repetitive textures and inconspicuous features, mountainous regions with significant continuous elevation changes, and urban areas with dramatic elevation variations and high repetition. Comparative to mainstream methods, our approach demonstrated remarkable improvements in reconstruction quality. Moreover, when applied to lowresolution images captured by the Ziyuan-3 (ZY-3) 01 satellite over various complex terrains, it consistently exhibits robust reconstruction accuracy and superior visual quality, underscoring the applicability of our model.

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A remote sensing-guided forest inventory concept using multispectral 3D and height information from ZiYuan-3 satellite data

Cited by 6 publications

References 29 publications

Predicting Species and Structural Diversity of Temperate Forests with Satellite Remote Sensing and Deep Learning

Predicting Species and Structural Diversity of Temperate Forests with Satellite Remote Sensing and Deep Learning

Remote sensing based forest cover classification using machine learning

Highly Applicable Iterative Network for 3-D Reconstruction Based on Multiview Satellite Images

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