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1. Decadienal afforestation and risks to montane grassland amphibians in the Udzungwa Plateau, Tanzania

   https://doi.org/10.1111/btp.13247  

   Lawson, Lucinda P. ; Loader, Simon P. ; Lyakurwa, John V. ; Menegon, Michele ; Liedtke, H. Christoph ( July 2023 , Biotropica)

   Human modification of the environment has drastically changed ecosystems around the globe. While forest‐loss (deforestation) has been well studied for its effects on ecosystems, afforestation also has major impacts. The Udzungwa Plateau in Tanzania is naturally a mix of forests and grasslands. However, non‐native pine plantations have recently increased in the area displacing the native grasslands of the montane plateau. In this study, we explored the effects of land use change on amphibian communities by comparing amphibian surveys before non‐native plantations were introduced (~2000) and after (2017–2019). To put these amphibian surveys into wider context across the plateau and to distinguish local population loss compared to plateau‐wide extinctions, we sample diversity at new sampling locations and locations with multiple surveys through time. We then assessed landscape change through remotely sensed data and ground surveys. Amphibian encounter surveys found approximately the same species diversity across all sampling locations, but local extinctions appear likely. Remaining amphibians appear to be limited to small refugia in non‐pine wetlands. Approximately half (45%) of the species found have limited distributional ranges (<72,000 km²) and are described as habitat specialists by the IUCN Red List. Monitoring pine plantation growth using remote sensing techniques suggested only limited extensions of plantations between 2000 through to 2013–2016, but ground surveys found small pines (<3 years old) throughout almost all areas identified as grasslands on satellite imagery. Our study highlights the plight of this narrowing biome on the African continent and calls for more research on the impact of non‐native plantations on natural communities.

   Abstract in Swahili is available with online material.

    

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2. A Multiscale Deep Learning Approach for High-Resolution Hyperspectral Image Classification

   https://doi.org/10.1109/LGRS.2020.2966987  

   Safari, Kazem ; Prasad, Saurabh ; Labate, Demetrio ( January 2020 , IEEE Geoscience and Remote Sensing Letters)

   Hyperspectral imagery (HSI) has emerged as a highly successful sensing modality for a variety of applications ranging from urban mapping to environmental monitoring and precision agriculture. Despite the efforts by the scientific community, developing reliable algorithms of HSI classification remains a challenging problem especially for high-resolution HSI data where there is often larger intraclass variability combined with scarcity of ground truth data and class imbalance. In recent years, deep neural networks have emerged as a promising strategy for problems of HSI classification where they have shown a remarkable potential for learning joint spectral-spatial features efficiently via backpropagation. In this paper, we propose a deep learning strategy for HSI classification that combines different convolutional neural networks especially designed to efficiently learn joint spatial-spectral features over multiple scales. Our method achieves an overall classification accuracy of 66.73% on the 2018 IEEE GRSS hyperspectral dataset – a high-resolution dataset that includes 20 urban land-cover and land-use classes 

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3. Applied nucleation facilitates tropical forest recovery: Lessons learned from a 15‐year study

   https://doi.org/10.1111/1365-2664.13684  

   Holl, Karen D. ; Reid, J. Leighton ; Cole, Rebecca J. ; Oviedo‐Brenes, Federico ; Rosales, Juan A. ; Zahawi, Rakan A. ; Garcia, ed., Cristina ( June 2020 , Journal of Applied Ecology)

   Applied nucleation, mostly based upon planting tree islands, has been proposed as a cost‐effective strategy to meet ambitious global forest and landscape restoration targets.

   We review results from a 15‐year study, replicated at 15 sites in southern Costa Rica, that compares applied nucleation to natural regeneration and mixed‐species tree plantations as strategies to restore tropical forest. We have collected data on planted tree survival and growth, woody vegetation recruitment and structure, seed rain, litterfall, epiphytes, birds, bats and leaf litter arthropods.

   Our results indicate that applied nucleation and plantation restoration strategies are similarly effective in enhancing the recovery of most floral and faunal groups, vegetation structure and ecosystem functions, as compared to natural regeneration.

   Seed dispersal and woody recruitment are higher in applied nucleation and plantation than natural regeneration treatments; canopy cover has increased substantially in both natural regeneration and applied nucleation treatments; and mortality of planted N‐fixing tree species has increased in recent years. These trends have led to rapid changes in vegetation composition and structure and nutrient cycling.

   The applied nucleation strategy is cheaper than mixed‐species tree plantations, but there may be social obstacles to implementing this technique in agricultural landscapes, such as perceptions that the land is not being used productively.

   Applied nucleation is likely to be most effective in cases where: planted vegetation nuclei enhance seed dispersal and seedling establishment of other species; the spread of nuclei is not strongly inhibited by abiotic or biotic factors; and the approach is compatible with restoration goals and landowner preferences.

   Synthesis and applications. Results from our 15‐year, multi‐site study show that applied nucleation can be a cost‐effective strategy for facilitating tropical forest regeneration that holds promise for helping to meet large‐scale international forest restoration commitments.

    

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4. Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data

   https://doi.org/10.3389/feart.2020.560933  

   Liu, Xue ; Fatoyinbo, Temilola E. ; Thomas, Nathan M. ; Guan, Weihe Wendy ; Zhan, Yanni ; Mondal, Pinki ; Lagomasino, David ; Simard, Marc ; Trettin, Carl C. ; Deo, Rinki ; et al ( January 2021 , Frontiers in Earth Science)

   null (Ed.)

   Coastal mangrove forests provide important ecosystem goods and services, including carbon sequestration, biodiversity conservation, and hazard mitigation. However, they are being destroyed at an alarming rate by human activities. To characterize mangrove forest changes, evaluate their impacts, and support relevant protection and restoration decision making, accurate and up-to-date mangrove extent mapping at large spatial scales is essential. Available large-scale mangrove extent data products use a single machine learning method commonly with 30 m Landsat imagery, and significant inconsistencies remain among these data products. With huge amounts of satellite data involved and the heterogeneity of land surface characteristics across large geographic areas, finding the most suitable method for large-scale high-resolution mangrove mapping is a challenge. The objective of this study is to evaluate the performance of a machine learning ensemble for mangrove forest mapping at 20 m spatial resolution across West Africa using Sentinel-2 (optical) and Sentinel-1 (radar) imagery. The machine learning ensemble integrates three commonly used machine learning methods in land cover and land use mapping, including Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network (NN). The cloud-based big geospatial data processing platform Google Earth Engine (GEE) was used for pre-processing Sentinel-2 and Sentinel-1 data. Extensive validation has demonstrated that the machine learning ensemble can generate mangrove extent maps at high accuracies for all study regions in West Africa (92%–99% Producer’s Accuracy, 98%–100% User’s Accuracy, 95%–99% Overall Accuracy). This is the first-time that mangrove extent has been mapped at a 20 m spatial resolution across West Africa. The machine learning ensemble has the potential to be applied to other regions of the world and is therefore capable of producing high-resolution mangrove extent maps at global scales periodically. 

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5. Automated High-Resolution Time Series Mapping of Mangrove Forests Damaged by Hurricane Irma in Southwest Florida

   https://doi.org/10.3390/rs12111740  

   McCarthy, Matthew J. ; Jessen, Brita ; Barry, Michael J. ; Figueroa, Marissa ; McIntosh, Jessica ; Murray, Tylar ; Schmid, Jill ; Muller-Karger, Frank E. ( June 2020 , Remote Sensing)

   null (Ed.)

   In September of 2017, Hurricane Irma made landfall within the Rookery Bay National Estuarine Research Reserve of southwest Florida (USA) as a category 3 storm with winds in excess of 200 km h−1. We mapped the extent of the hurricane’s impact on coastal land cover with a seasonal time series of satellite imagery. Very high-resolution (i.e., <5 m pixel) satellite imagery has proven effective to map wetland ecosystems, but challenges in data acquisition and storage, algorithm training, and image processing have prevented large-scale and time-series mapping of these data. We describe our approach to address these issues to evaluate Rookery Bay ecosystem damage and recovery using 91 WorldView-2 satellite images collected between 2010 and 2018 mapped using automated techniques and validated with a field campaign. Land cover was classified seasonally at 2 m resolution (i.e., healthy mangrove, degraded mangrove, upland, soil, and water) with an overall accuracy of 82%. Digital change detection methods show that hurricane-related degradation was 17% of mangrove forest (~5 km2). Approximately 35% (1.7 km2) of this loss recovered one year after Hurricane Irma. The approach completed the mapping approximately 200 times faster than existing methods, illustrating the ease with which regional high-resolution mapping may be accomplished efficiently. 

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