More Like this

1. Combining community observations and remote sensing to examine the effects of roads on wildfires in the East Siberian boreal forest

   https://doi.org/10.1139/as-2021-0042  

   Kuklina, Vera ; Sizov, Oleg ; Bogdanov, Victor ; Krasnoshtanova, Natalia ; Morozova, Arina ; Petrov, Andrey N. ( January 2022 , Arctic Science)

   The paper is aimed at assessing the associations between the road networks geography and dynamics of wildfire events in the East Siberian boreal forest. We examined the relationship between the function of roads, their use, and management and the wildfire ignition, propagation, and termination during the catastrophic fire season of 2016 in the Irkutsk Region of Russia. Document analysis and interviews were utilized to identify main forest users and road infrastructure functional types and examine wildfire management practices. We combined community observations and satellite remotely sensed data to assess relationships between the location, extent, and timing of wildfires and different types of roads as fire sources, barriers, and suppression access points. Our study confirms a strong spatial relationship between the wildfire ignition points and roads differentiated by their types with the highest probability of fire ignition near forestry roads and the lowest near subsistence roads. Roads also play an important role in wildfire suppression, working as both physical barriers and access points for firefighters. Our research illustrates the importance of local and Indigenous observations along the roads for monitoring and understanding wildfires, including “zombie fires”. It also has practical implications for fire management collectively developed by authorities and local communities.
2. Exposure of Protected and Unprotected Forest to Plant Invasions in the Eastern United States

   https://doi.org/10.3390/f9110723  

   Riitters, Kurt ; Potter, Kevin ; Iannone, Basil ; Oswalt, Christopher ; Guo, Qinfeng ; Fei, Songlin ( November 2018 , Forests)

   Research Highlights: We demonstrate a macroscale framework combining an invasibility model with forest inventory data, and evaluate regional forest exposure to harmful invasive plants under different types of forest protection. Background and Objectives: Protected areas are a fundamental component of natural resource conservation. The exposure of protected forests to invasive plants can impede achievement of conservation goals, and the effectiveness of protection for limiting forest invasions is uncertain. We conducted a macroscale assessment of the exposure of protected and unprotected forests to harmful invasive plants in the eastern United States. Materials and Methods: Invasibility (the probability that a forest site has been invaded) was estimated for 82,506 inventory plots from site and landscape attributes. The invaded forest area was estimated by using the inventory sample design to scale up plot invasibility estimates to all forest area. We compared the invasibility and the invaded forest area of seven categories of protection with that of de facto protected (publicly owned) forest and unprotected forest in 13 ecological provinces. Results: We estimate approximately 51% of the total forest area has been exposed to harmful invasive plants, including 30% of the protected forest, 38% of the de facto protected forest, and 56% of themore »unprotected forest. Based on cumulative invasibility, the relative exposure of protection categories depended on the assumed invasibility threshold. Based on the invaded forest area, the five least-exposed protection categories were wilderness area (13% invaded), national park (18%), sustainable use (26%), nature reserve (31%), and de facto protected Federal land (36%). Of the total uninvaded forest area, only 15% was protected and 14% had de facto protection. Conclusions: Any protection is better than none, and public ownership alone is as effective as some types of formal protection. Since most of the remaining uninvaded forest area is unprotected, landscape-level management strategies will provide the most opportunities to conserve it.« less
3. Quantitative analysis of hillshed geomorphology and critical zone function: Raising the hillshed to watershed status

   https://doi.org/10.1130/B35724.1  

   Brecheisen, Zachary S. ; Richter, Daniel D. ; Moon, Seulgi ; Halpin, Patrick N. ( December 2021 , GSA Bulletin)

   Landscapes are frequently delineated by nested watersheds and river networks ranked via stream orders. Landscapes have only recently been delineated by their interfluves and ridge networks, and ordered based on their ridge connectivity. There are, however, few studies that have quantitatively investigated the connections between interfluve networks and landscape morphology and environmental processes. Here, we ordered hillsheds using methods complementary to traditional watersheds, via a hierarchical ordering of interfluves, and we defined hillsheds to be landscape surfaces from which soil is shed by soil creep or any type of hillslope transport. With this approach, we demonstrated that hillsheds are most useful for analyses of landscape structure and processes. We ordered interfluve networks at the Calhoun Critical Zone Observatory (CZO), a North American Piedmont landscape, and demonstrated how interfluve networks and associated hillsheds are related to landscape geomorphology and processes of land management and land-use history, accelerated agricultural gully erosion, and bedrock weathering depth (i.e., regolith depth). Interfluve networks were ordered with an approach directly analogous to that first proposed for ordering streams and rivers by Robert Horton in the GSA Bulletin in 1945. At the Calhoun CZO, low-order hillsheds are numerous and dominate most of the observatory’s ∼190 km2 area.more »Low-order hillsheds are relatively narrow with small individual areas, they have relatively steep slopes with high curvature, and they are relatively low in elevation. In contrast, high-order hillsheds are few, large in individual area, and relatively level at high elevation. Cultivation was historically abandoned by farmers on severely eroding low-order hillsheds, and in fact agriculture continues today only on high-order hillsheds. Low-order hillsheds have an order of magnitude greater intensity of gullying across the Calhoun CZO landscape than high-order hillsheds. In addition, although modeled regolith depth appears to be similar across hillshed orders on average, both maximum modeled regolith depth and spatial depth variability decrease as hillshed order increases. Land management, geomorphology, pedology, and studies of land-use change can benefit from this new approach pairing landscape structure and analyses.« less
4. Patch Similarity Convolutional Neural Network for Urban Flood Extent Mapping Using Bi-Temporal Satellite Multispectral Imagery

   https://doi.org/10.3390/rs11212492  

   Peng, Bo ; Meng, Zonglin ; Huang, Qunying ; Wang, Caixia ( November 2019 , Remote Sensing)

   Urban flooding is a major natural disaster that poses a serious threat to the urban environment. It is highly demanded that the flood extent can be mapped in near real-time for disaster rescue and relief missions, reconstruction efforts, and financial loss evaluation. Many efforts have been taken to identify the flooding zones with remote sensing data and image processing techniques. Unfortunately, the near real-time production of accurate flood maps over impacted urban areas has not been well investigated due to three major issues. (1) Satellite imagery with high spatial resolution over urban areas usually has nonhomogeneous background due to different types of objects such as buildings, moving vehicles, and road networks. As such, classical machine learning approaches hardly can model the spatial relationship between sample pixels in the flooding area. (2) Handcrafted features associated with the data are usually required as input for conventional flood mapping models, which may not be able to fully utilize the underlying patterns of a large number of available data. (3) High-resolution optical imagery often has varied pixel digital numbers (DNs) for the same ground objects as a result of highly inconsistent illumination conditions during a flood. Accordingly, traditional methods of flood mapping have majormore »limitations in generalization based on testing data. To address the aforementioned issues in urban flood mapping, we developed a patch similarity convolutional neural network (PSNet) using satellite multispectral surface reflectance imagery before and after flooding with a spatial resolution of 3 meters. We used spectral reflectance instead of raw pixel DNs so that the influence of inconsistent illumination caused by varied weather conditions at the time of data collection can be greatly reduced. Such consistent spectral reflectance data also enhance the generalization capability of the proposed model. Experiments on the high resolution imagery before and after the urban flooding events (i.e., the 2017 Hurricane Harvey and the 2018 Hurricane Florence) showed that the developed PSNet can produce urban flood maps with consistently high precision, recall, F1 score, and overall accuracy compared with baseline classification models including support vector machine, decision tree, random forest, and AdaBoost, which were often poor in either precision or recall. The study paves the way to fuse bi-temporal remote sensing images for near real-time precision damage mapping associated with other types of natural hazards (e.g., wildfires and earthquakes).« less
5. Storm surge and ponding explain mangrove dieback in southwest Florida following Hurricane Irma

   https://doi.org/10.1038/s41467-021-24253-y  

   Lagomasino, David ; Fatoyinbo, Temilola ; Castañeda-Moya, Edward ; Cook, Bruce D. ; Montesano, Paul M. ; Neigh, Christopher S. R. ; Corp, Lawrence A. ; Ott, Lesley E. ; Chavez, Selena ; Morton, Douglas C. ( June 2021 , Nature Communications)

   Mangroves buffer inland ecosystems from hurricane winds and storm surge. However, their ability to withstand harsh cyclone conditions depends on plant resilience traits and geomorphology. Using airborne lidar and satellite imagery collected before and after Hurricane Irma, we estimated that 62% of mangroves in southwest Florida suffered canopy damage, with largest impacts in tall forests (>10 m). Mangroves on well-drained sites (83%) resprouted new leaves within one year after the storm. By contrast, in poorly-drained inland sites, we detected one of the largest mangrove diebacks on record (10,760 ha), triggered by Irma. We found evidence that the combination of low elevation (median = 9.4 cm asl), storm surge water levels (>1.4 m above the ground surface), and hydrologic isolation drove coastal forest vulnerability and were independent of tree height or wind exposure. Our results indicated that storm surge and ponding caused dieback, not wind. Tidal restoration and hydrologic management in these vulnerable, low-lying coastal areas can reduce mangrove mortality and improve resilience to future cyclones.