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Forests are an essential part of the ecosystem and play an irreplaceable role in maintaining the balance of the ecosystem and protecting biodiversity. The monitoring of forest distribution plays an important role in the conservation and management of forests. This paper analyzes and compares the performance of imagery from GF-1 WFV, Landsat 8, and Sentinel-2 satellites with respect to forest/non-forest classification tasks using the random forest algorithm (RF). The results show that in the classification task of this paper, although the differences in classification accuracy among the three satellite datasets are not remarkable, the Sentinel-2 data have the highest accuracy, GF-1 WFV the second highest, and Landsat 8 the lowest. In addition, it was found that remotely sensed data of different processing levels show little influence on the classification accuracy with respect to the forest/non-forest classification task. However, the classification accuracy of the top of the atmosphere reflectance product was the most stable, and the vegetation index has a marginal effect on the distinction between forest and non-forest areas. [ABSTRACT FROM AUTHOR]

Forests are subject to a huge variety of often competing socio-economic demands and environmental change. This paper assesses the related conflicts that occur along what we label to be a "Global Forest Environmental Frontier". Assessing 11 contributions to a special issue on the same topic, it summarizes the main contents of these papers and concludes with an assessment of major trends. The contributions to the special issue take both a regional and topic-related approach, assessing forest environmental conflicts on all five forested continents and investigating issues such as forest biodiversity conservation, climate change adaptation and mitigation, environmental justice and equity, development, and forest management and conservation discourses. Taken together, they provide an overview on the multiple facets of the Global Forest Environmental Frontier, but also identify some shared patterns and trajectories, which are outlined at the end of this paper. [ABSTRACT FROM AUTHOR]

Summary: Mature forests have structural habitat features that can take hundreds of years to develop, and large reserves alone are unlikely to ensure conservation of the species that rely on these features. This paper outlines a proposed new approach to managing mature forest features, the ‘mature habitat management approach’, in areas outside of reserves. The objective was to maintain a network of current and future mature forest habitat distributed across the landscape. The approach is designed to complement the existing reserve network and management actions and is tenure‐blind. Spatial information on the availability of mature forest habitat at the local (1‐km radius) and landscape (5‐km radius) scales is used for decisions on retention within a 1‐km radius of a harvest area, to reach the minimum target of 20% and 30% retention of mature forest at the local and landscape spatial scales, respectively. We believe this approach could contribute to meeting the conservation needs of many species that require mature forest features for refuge and breeding in Tasmania and elsewhere. [ABSTRACT FROM AUTHOR]

Plant diversity is an important parameter in maintaining forest ecosystem services, functions and stability. Timely and accurate monitoring and evaluation of large-area wall-to-wall maps on plant diversity and its spatial heterogeneity are crucial for the conservation and management of forest resources. However, traditional botanical field surveys designed to estimate plant diversity are usually limited in their spatiotemporal resolutions. Using Sentinel-1 (S-1) and Sentinel-2 (S-2) data at high spatiotemporal scales, combined with and referenced to botanical field surveys, may be the best choice to provide accurate plant diversity distribution information over a large area. In this paper, we predicted and mapped plant diversity in a subtropical forest using 24 months of freely and openly available S-1 and S-2 images (10 m × 10 m) data over a large study area (15,290 km2). A total of 448 quadrats (10 m × 10 m) of forestry field surveys were captured in a subtropical evergreen-deciduous broad-leaved mixed forest to validate a machine learning algorithm. The objective was to link the fine Sentinel spectral and radar data to several ground-truthing plant diversity indices in the forests. The results showed that: (1) The Simpson and Shannon-Wiener diversity indices were the best predicted indices using random forest regression, with ȓ2 of around 0.65; (2) The use of S-1 radar data can enhance the accuracy of the predicted heterogeneity indices in the forests by approximately 0.2; (3) As for the mapping of Simpson and Shannon-Wiener, the overall accuracy was 67.4% and 64.2% respectively, while the texture diversity's overall accuracy was merely 56.8%; (4) From the evaluation and prediction map information, the Simpson, Shannon-Wiener and texture diversity values (and its confidence interval values) indicate spatial heterogeneity in pixel level. The large-area forest plant diversity indices maps add spatially explicit information to the ground-truthing data. Based on the results, we conclude that using the time-series of S-1 and S-2 radar and spectral characteristics, when coupled with limited ground-truthing data, can provide reasonable assessments of plant spatial heterogeneity and diversity across wide areas. It could also help promote forest ecosystem and resource conservation activities in the forestry sector. [ABSTRACT FROM AUTHOR]