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Ecocultural restoration involves the reciprocal repair of ecosystems and revitalization of cultural practices to enhance their mutual resilience to natural and anthropogenic disturbances and climate change stressors. Resilient ecocultural systems are adapted to retain structure and function in the face of disturbances that remain within historical ranges of severity. To assist in ecocultural restoration and management, understanding how a system has historically responded to different types of disturbances is therefore invaluable in understanding how social-ecological resilience can be maintained in the face of future stressors and disturbances. However, records of disturbances and ecocultural responses can be limited for certain landscapes and human communities. In this methods paper, we demonstrate a mixed-method process for integrating oral history, field-based knowledge, archival information, and historical and contemporary aerial images to gain insight into the changes on the Klamath River in Northern California from the 1940s through 2020. We georegistered historical imagery, quantified changes between land cover classes, and contextualized these classifications with qualitative assessments of changes in larger surrounding areas. By synthesizing these data sources with field measurements, mining and other land survey maps, timber management plans, fire and flood histories, and interviews with members of the Karuk Tribe, we were able to reconstruct the land use and land cover change histories at five sites. We noted that recovery of canopy cover from fire and logging practices was faster than for flood, which was faster than recovery from mining, consistent with the relative severity of likely soil disturbance. By combining different sources of information with complementary strengths, we were able to provide managers with site-specific information on recovery from different types of disturbance. Though this approach was labor-intensive, with emerging tools for supervised classification of high-resolution imagery, mixed-method analytical historical ecology could be applied more broadly, supporting ecocultural restoration on a larger scale. [Display omitted] • Reconstructing landscape histories helps understand ecosystem response to disturbance. • Combining qualitative and quantitative analysis helps reconstruct land-use. • Synthesizing many historical data sources improves ecocultural restoration planning. • Manual classification could improve with machine learning 'human-in-the-loop' tools. [ABSTRACT FROM AUTHOR]

Monitoring a specific area to analyze a continuous change has become more accessible by using optical images in remote sensing technology. However, several natural and artificial aspects such as fog and air pollution make it difficult to extract correct geometric information. To overcome the limitation of optical images, Synthetic Aperture Radar (SAR) images can be used to access more accurate information with respect to the targeted area. In this manner, optical and SAR images can be utilized together to detect the scale of change even in bad weather conditions. To process optical and SAR images, an image translation process-oriented Deep Adaptation-based Change Detection Technique (DACDT) is proposed. An optimized U-Net++ model is proposed that helps to improve the global and regional impacts of the images. Moreover, a multi-scale loss function is utilized to access the features of different dimensions. In this manner, the final change maps are generated by transferring the features of optical images to the SAR images for better change analysis. The prediction performance of the proposed approach is evaluated on four different datasets such as Gloucester I, Shuguang Village, Gloucester-II, and California. The calculated outcomes define the prediction performance of the proposed solution by registering the accuracy of 98.67%, 99.77%, 97.68%, and 98.87%, respectively. [ABSTRACT FROM AUTHOR]

The polarimetric synthetic aperture radar (PolSAR) system is one of the most successful tools for solving remote-sensing issues. However, the images produced by this system – which uses coherent illumination – are corrupted by the multidimensional speckle noise that gives PolSAR data a multiplicative character. Therefore, tailored processing of PolSAR images is required, e.g. improved hypothesis testing and change detectors. In this work, we propose a novel bivariate distribution – called G I 0 McKay bivariate ( G I 0 MB) - to describe a joint distribution arising from two components of the total scattering power image (SPAN). We derive closed-form expressions for the Kullback–Leibler and Rényi divergences for the G I 0 MB law. We provide new two-sample divergence-based hypothesis tests and evaluate their performance using Monte Carlo experiments. Finally, we apply the new tests to real PolSAR images to evaluate the changes caused by urbanization processes in the Los Angeles and California regions. Results show that our proposals to detect changes in PolSAR images outperform the one based on the likelihood ratio. [ABSTRACT FROM AUTHOR]