scholarly journals Ranking Importance of Topographical Surface and Subsurface Parameters on Paludification in Northern Boreal Forests Using Very High Resolution Remotely Sensed Datasets

2020 ◽  
Vol 12 (2) ◽  
pp. 577 ◽  
Author(s):  
Ahmed Laamrani ◽  
Osvaldo Valeria
Keyword(s):  
High Resolution ◽  
Boreal Forests ◽  
Critical Role ◽  
Mineral Soil ◽  
Coniferous Forests ◽  
Slope Aspect ◽  
Limiting Factor ◽  
Organic Layer ◽  
Topographic Wetness Index ◽  
Variable Surface

The accumulation of organic material on top of the mineral soil over time (a process called paludification) is common in Northern Boreal coniferous forests. This natural process leads to a marked decrease in forest productivity overtime. Topography both at the surface of the forest floor (i.e., above ground) and the subsurface (i.e., top of mineral soil which is underground) is known to play a critical role in the paludification process. Until recently, the availability of more accurate topographic information regarding the surface and subsurface was a limiting factor for land management and modeling of spatial organic layer thickness (OLT) variability, a proxy for paludification. However so far, no research has assessed which of these two topographic variables has the greatest influence on paludification. This study aims to assess which topographic variable (surface or subsurface) better explains paludification, using high-resolution remote sensing technology (i.e., Light Detection and Ranging: LiDAR and Ground Penetrating Radar: GPR). To this end, field soil measurements were made in over 1614 sites distributed throughout the reference Valrennes Experimental site in Canadian northern coniferous forests. Then, a machine learning model (i.e., Random Forest, RF) was implemented to rank a set of selected predictor topographic variables (i.e., slope, aspect, mean curvature, plan curvature, profile curvature, and topographic wetness index) using the Mean Decrease Gini (MDG) index as an indicator of importance. Results showed that overall 83% of the overall variance was explained by the RF selected model, while the derived subsurface topography predictors had the lowest MDGs for predicting paludification. On the other hand, the surface slope predictor had the highest MDGs and better explained paludification. This finding would be particularly useful for implanting sustainable management strategies based on the surface variables of paludified northern boreal forests. This study has also highlighted the potential of LiDAR data to provide surface topographic spatial detail information for planning and optimizing forest management activities in paludified boreal forests. This is even of great importance when we know that LiDAR variables are easier to obtain compared to GPR derived variables (subsurface topographic variables).

scholarly journals Flood Susceptibility Prediction Using Hybrid-Based Approaches of Support Vector Regression Model and Meta-Heuristic Algorithms

2021 ◽  
Author(s):  
Hossein Hamedi Sorajar ◽  
Ali Asghar Alesheikh ◽  
Mahdi Panahi ◽  
Saro Lee
Keyword(s):  
Land Use ◽  
Critical Role ◽  
Influential Factor ◽  
Slope Aspect ◽  
Support Vector ◽  
Human Beings ◽  
Operating Characteristics ◽  
Topographic Wetness Index ◽  
Support Vector Regression Model ◽  
Testing Dataset

Abstract Landslides are one of the most destructive natural phenomena in the world, which occur mostly in mountainous areas and cause damage to the economic sectors, agricultural lands, residential areas and infrastructures of any country, and also threaten the lives and property of human beings. Therefore, landslide susceptibility mapping (LSM) can play a critical role in identifying prone areas and reducing the damage caused by landslides in each area. In the present study, deep learning algorithms including convolutional neural network (CNN) and long short-term memory (LSTM) were used to identify landslide prone areas in Ardabil province, Iran. Equql to 312 landslide locations were identified and randomly divided into train and test datasets at 70–30% ratios. Then, according to previous studies and environmental conditions in the study area, twelve factors affecting the occurrence of landslides were selected, namely altitude, slope angle, slope aspect, topographic wetness index (TWI), profile curvature, plan curvature, land-use, lithology, distance to faults, distance to rivers, distance to roads, and rainfall. The ratio of the importance of each influential factor in landslide occurrence was obtained through information gain ranking filter (IGRF) method and it was found that land-use and profile curvature had the highest and lowest impacts, respectively. Afterwards, LSMs were generated using CNN and LSTM algorithms. In the next step, the performance of the models was evaluated based on the area under curve (AUC) value of receiver operating characteristics curve and the root mean square error (RMSE) method. The AUC values for CNN and LSTM models were 0.821 and 0.832, respectively. Furthermore, the RMSE values in the CNN model for each of the training and testing dataset were 0.121 and 0.132, respectively. The RMSE values in the LSTM model for each of the training and testing dataset were 0.185 and 0.188, respectively. Therefore, it can be concluded that CNN performance is slightly better than LSTM; but in general, both models have close performance and the accuracy of both models is acceptable.

scholarly journals Diversity of Understory Communities in Boreal Forests: Influences of Forest Type, Latitude, and Spatial Scale

Forests ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 1003
Author(s):  
Yu-Song Jin ◽  
Yu-Kun Hu ◽  
Jing Wang ◽  
Dan-Dan Liu ◽  
Ying-Hua Lin ◽  
...  
Keyword(s):  
Beta Diversity ◽  
Boreal Forests ◽  
Forest Type ◽  
Critical Role ◽  
Understory Vegetation ◽  
Coniferous Forests ◽  
Lower Latitude ◽  
Forest Types ◽  
Vegetation Diversity ◽  
Alpha And Beta Diversity

Understory vegetation hosts high biodiversity and plays a critical role in the ecosystem processes of boreal forests. However, the drivers of understory plant diversity in this high-latitude ecosystem remain uncertain. To investigate the influences of forest type and latitude on understory beta diversity at different scales, we quantified the species composition of Vaccinium uliginosum Linnaeus communities under broadleaf and coniferous forests at two latitudes at the quadrat (2 × 2 m) and plot (10 × 10 m) scales in the Greater Xing’an Mountains, NE China. At the quadrat scale, species alpha diversity of V. uliginosum communities was higher in broadleaf forests than that in coniferous forests at both latitudes. The differences in species beta diversity (the Sørensen’s dissimilarity) in two forest types depended on the latitude: beta diversity in broadleaf forests was higher than that in coniferous forests at the higher latitude, while beta diversity in coniferous forests was higher at the lower latitude. At the plot scale, alpha and beta diversity of V. uliginosum communities decreased from broadleaf forests to coniferous forests at the higher latitude, and they did not show significant differences between forest types at the lower latitude. These results indicate the interactive effects of forest type and latitude on beta diversity of understory vegetation. Moreover, the influences of forest type and latitude on species alpha and beta diversity were different across the two spatial scales, suggesting that the assembly mechanisms underlying species diversity may be different at different scales. Understanding the maintenance of understory vegetation diversity will benefit the conservation and management of boreal forests.

scholarly journals ON THE USE OF SENTINEL-2 IMAGES AND HIGH RESOLUTION DTM FOR LANDSLIDE SUSCEPTIBILITY MAPPING IN A DEVELOPING URBAN SETTLEMENT (MAMAK, ANKARA, TURKEY)

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 469-476 ◽  
Author(s):  
T. Yanar ◽  
S. Kocaman ◽  
C. Gokceoglu
Keyword(s):  
High Resolution ◽  
Urban Planning ◽  
Landslide Susceptibility ◽  
Earth Observation ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Topographic Wetness Index ◽  
Landslide Susceptibility Map ◽  
Earth Observation Satellites ◽  
Sentinel 2

<p><strong>Abstract.</strong> Urban planning starts with the selection of suitable sites. The main factors and components for site selection are the geological-geotechnical parameters that directly affect the natural hazards, such as landslide and flood, construction costs and the location and distribution of existing infrastructure. The presence and accuracy of up-to-date maps in planning are very important. With the increase of high resolution Earth observation satellites, the required data can be obtained with high temporal frequency and spatial availability. From these data, the base parameters for planning can be extracted with semi- or fully-automatic methods. Among the Earth observation satellites, the Sentinel-2 mission of European Space Agency (ESA) provides high resolution optical images and the data are freely available also at different processing levels such as orthorectified images.</p> <p>In this study, the possibility of the landslide susceptibility map production which should be one of the base maps in urban planning by using Sentinel-2 satellite images was investigated in Mamak District of Ankara City, Turkey. The land cover and land use data were produced from Sentinel-2 images by using a supervised classification method in SNAP Tool provided by ESA. The lithological definitions were received from the General Directorate of Mineral Research and Explorations. The topographical parameters such as slope, aspect, topographic wetness index, etc. were extracted from a high resolution digital terrain model (DTM) of the area. Manually extracted landslide inventory data were employed in the logistic regression method and the produced landslide susceptibility map of the study area is presented here.</p>

scholarly journals Patterns of bryophyte succession in a 160-year chronosequence in deciduous and coniferous forests of boreal Alaska

2017 ◽  
Vol 47 (8) ◽  
pp. 1021-1032 ◽  
Author(s):  
Mélanie Jean ◽  
Heather D. Alexander ◽  
Michelle C. Mack ◽  
Jill F. Johnstone
Keyword(s):  
Leaf Litter ◽  
Populus Tremuloides ◽  
Boreal Forests ◽  
Coniferous Forests ◽  
Stand Age ◽  
Organic Layer ◽  
Layer Depth ◽  
Litter Cover ◽  
Canopy Composition ◽  
Compositional Patterns

Bryophytes are dominant components of boreal forest understories and play a large role in regulating soil microclimate and nutrient cycling. Therefore, shifts in bryophyte communities have the potential to affect boreal forests’ ecosystem processes. We investigated how bryophyte communities varied in 83 forest stands in interior Alaska that ranged in age (since fire) from 8 to 163 years and had canopies dominated by deciduous broadleaf (Populus tremuloides Michx. or Betula neoalaskana Sarg.) or coniferous trees (Picea mariana Mill B.S.P.). In each stand, we measured bryophyte community composition, along with environmental variables (e.g., organic layer depth, leaf litter cover, moisture). Bryophyte communities were initially similar in deciduous vs. coniferous forests but diverged in older stands in association with changes in organic layer depth and leaf litter cover. Our data suggest two tipping points in bryophyte succession: one at the disappearance of early colonizing taxa 20 years after fire and another at 40 years after fire, which corresponds to canopy closure and differential leaf litter inputs in mature deciduous and coniferous canopies. Our results enhance understanding of the processes that shape compositional patterns and ecosystem services of bryophytes in relation to stand age, canopy composition, and changing disturbances such as fire that may trigger changes in canopy composition.

The use of ground penetrating radar for remote sensing the organic layer – mineral soil interface in paludified boreal forests

2013 ◽  
Vol 39 (1) ◽  
pp. 74-88 ◽  
Author(s):  
Ahmed Laamrani ◽  
Osvaldo Valeria ◽  
Li Zhen Cheng ◽  
Yves Bergeron ◽  
Christian Camerlynck
Keyword(s):  
Remote Sensing ◽  
Ground Penetrating Radar ◽  
Boreal Forests ◽  
Mineral Soil ◽  
Organic Layer ◽  
Layer Mineral ◽  
Ground Penetrating ◽  
Soil Interface

scholarly journals A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4893 ◽  
Author(s):  
Hejar Shahabi ◽  
Ben Jarihani ◽  
Sepideh Tavakkoli Piralilou ◽  
David Chittleborough ◽  
Mohammadtaghi Avand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Segmentation ◽  
Vegetation Index ◽  
Scale Parameter ◽  
Slope Aspect ◽  
Support Vector ◽  
Topographic Wetness Index ◽  
Slope Length ◽  
Optimal Scale ◽  
Object Based

Gully erosion is a dominant source of sediment and particulates to the Great Barrier Reef (GBR) World Heritage area. We selected the Bowen catchment, a tributary of the Burdekin Basin, as our area of study; the region is associated with a high density of gully networks. We aimed to use a semi-automated object-based gully networks detection process using a combination of multi-source and multi-scale remote sensing and ground-based data. An advanced approach was employed by integrating geographic object-based image analysis (GEOBIA) with current machine learning (ML) models. These included artificial neural networks (ANN), support vector machines (SVM), and random forests (RF), and an ensemble ML model of stacking to deal with the spatial scaling problem in gully networks detection. Spectral indices such as the normalized difference vegetation index (NDVI) and topographic conditioning factors, such as elevation, slope, aspect, topographic wetness index (TWI), slope length (SL), and curvature, were generated from Sentinel 2A images and the ALOS 12-m digital elevation model (DEM), respectively. For image segmentation, the ESP2 tool was used to obtain three optimal scale factors. On using object pureness index (OPI), object matching index (OMI), and object fitness index (OFI), the accuracy of each scale in image segmentation was evaluated. The scale parameter of 45 with OFI of 0.94, which is a combination of OPI and OMI indices, proved to be the optimal scale parameter for image segmentation. Furthermore, segmented objects based on scale 45 were overlaid with 70% and 30% of a prepared gully inventory map to select the ML models’ training and testing objects, respectively. The quantitative accuracy assessment methods of Precision, Recall, and an F1 measure were used to evaluate the model’s performance. Integration of GEOBIA with the stacking model using a scale of 45 resulted in the highest accuracy in detection of gully networks with an F1 measure value of 0.89. Here, we conclude that the adoption of optimal scale object definition in the GEOBIA and application of the ensemble stacking of ML models resulted in higher accuracy in the detection of gully networks.

Is temperature still the most limiting factor for growth in northern boreal forests?

The Holocene ◽  
2021 ◽  
pp. 095968362110116
Author(s):  
Jeroen DM Schreel
Keyword(s):  
Tree Ring ◽  
Boreal Forests ◽  
Ring Width ◽  
Temperature Fluctuations ◽  
Limiting Factor ◽  
Tree Ring Width ◽  
Climate Reconstructions ◽  
Latewood Density

Over the last few decades – at a range of northern sites – changes in tree-ring width and latewood density have not followed mean summertime temperature fluctuations. This discrepancy sharply contrasts an earlier correlation between those variables. As the origin of this inconsistency has not been fully deciphered, questions have emerged regarding the use of tree-ring width and latewood density as a proxy in dendrochronological climate reconstructions. I suggest that temperature is no longer the most limiting factor in certain boreal areas, which might explain the observed divergence.

Effect of forest harvesting and slash piling on microbial biomass and respiration in Newfoundland boreal forests

2007 ◽  
Vol 87 (4) ◽  
pp. 455-458 ◽  
Author(s):  
Martin T Moroni ◽  
Paul Q Carter ◽  
Dean W Strickland ◽  
Franz Makeschin ◽  
Don-Roger Parkinson ◽  
...  
Keyword(s):  
Microbial Biomass ◽  
Key Words ◽  
Boreal Forests ◽  
Fungal Biomass ◽  
Forest Harvesting ◽  
Organic Layer ◽  
Total Microbial Biomass ◽  
Organic Layers ◽  
Respiration Rates ◽  
Fumigation Extraction

Clearcutting Newfoundland boreal forests significantly reduced organic layer fungal and total microbial biomass in clearcut areas with and without slash cover, compared with forested plots. However, aerobically incubated respiration rates were highest in organic layers from clearcut areas under slash, intermediate under forests, and lowest from clearcut areas without slash. Key words: Carbon, ergosterol, fumigation–extraction, fungal biomass, harvest slash, nitrogen

scholarly journals Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models

2021 ◽  
Vol 10 (5) ◽  
pp. 315
Author(s):  
Hilal Ahmad ◽  
Chen Ningsheng ◽  
Mahfuzur Rahman ◽  
Md Monirul Islam ◽  
Hamid Reza Pourghasemi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Debris Flows ◽  
Regional Scale ◽  
Indus Basin ◽  
Annual Rainfall ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Susceptibility Maps ◽  
Landslides And Debris Flows

The China–Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damage every year. This study assessed geohazards (landslides and debris flows) and developed susceptibility maps by considering four standalone machine-learning and statistical approaches, namely, Logistic Regression (LR), Shannon Entropy (SE), Weights-of-Evidence (WoE), and Frequency Ratio (FR) models. To this end, geohazard inventories were prepared using remote sensing techniques with field observations and historical hazard datasets. The spatial relationship of thirteen conditioning factors, namely, slope (degree), distance to faults, geology, elevation, distance to rivers, slope aspect, distance to road, annual mean rainfall, normalized difference vegetation index, profile curvature, stream power index, topographic wetness index, and land cover, with hazard distribution was analyzed. The results showed that faults, slope angles, elevation, lithology, land cover, and mean annual rainfall play a key role in controlling the spatial distribution of geohazards in the study area. The final susceptibility maps were validated against ground truth points and by plotting Area Under the Receiver Operating Characteristic (AUROC) curves. According to the AUROC curves, the success rates of the LR, WoE, FR, and SE models were 85.30%, 76.00, 74.60%, and 71.40%, and their prediction rates were 83.10%, 75.00%, 73.50%, and 70.10%, respectively; these values show higher performance of LR over the other three models. Furthermore, 11.19%, 9.24%, 10.18%, 39.14%, and 30.25% of the areas corresponded to classes of very-high, high, moderate, low, and very-low susceptibility, respectively. The developed geohazard susceptibility map can be used by relevant government officials for the smooth implementation of the CPEC project at the regional scale.

Testing PRISMA hyperspectral satellite imagery in predicting soil carbon content based on synthetized LUCAS spectral data

2021 ◽  
Author(s):  
Zsófia Adrienn Kovács ◽  
János Mészáros ◽  
Mátyás Árvai ◽  
Annamária Laborczi ◽  
Gábor Szatmári ◽  
...  
Keyword(s):  
Land Use ◽  
Spectral Data ◽  
Hyperspectral Imagery ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Soil Carbon Content ◽  
Space Agency ◽  
Italian Space Agency ◽  
Agricultural Areas ◽  
Imagery Data

&lt;p&gt;The estimation of the soil organic carbon (SOC) content plays an important role for carbon sequestration in the context of climate change and soil degradation. Reflectance spectroscopy has proven to be promising technique for SOC quantification in the laboratory and increasingly from air and spaceborne platforms, where hyperspectral imagery provides great potential for mapping SOC on larger scales.&lt;/p&gt;&lt;p&gt;The PRISMA (PRecursore IperSpettrale della Missione Applicativa) is an earth-observation satellite with a medium spatial resolution hyperspectral radiometer onboard, developed and maintained by the Italian Space Agency.&lt;/p&gt;&lt;p&gt;The Pan-European Land Use/ Land Cover Area Frame Survey (LUCAS) topsoil database contains soil physical, chemical and spectral data for most European countries. Based on the LUCAS points located in Hungary, a synthetized spectral dataset was created and matched to the spectral characteristic of PRISMA sensor, later used for building up machine learning based models (random forest, artificial neural network). SOC levels for the sample area was predicted using generated models and mainly PRISMA imagery.&lt;/p&gt;&lt;p&gt;Our sample imagery data was generated from five consecutive, cloud-free PRISMA images covering 4500 km&lt;sup&gt;2&lt;/sup&gt; in the central part of the Great Plain in Hungary, which is one of the most important agricultural areas of the country, used mainly for crops on arable lands. The images were recorded in 2020 February when most croplands are not covered by vegetation therefore our tests were implemented on bare soils.&lt;/p&gt;&lt;p&gt;We tested the prediction accuracy of hyperspectral imagery data supplemented by various environmental datasets as additional predictor variables in four scenarios: (i) using solely hyperspectral imagery data (ii) spectral imagery data, elevation and its derived parameters (e.g. slope, aspect, topographic wetness index etc.) (iii) spectral imagery data and land-use information and (iv) all aforementioned data in fusion.&lt;/p&gt;&lt;p&gt;For validation two types of datasets were used: (i) measured data at the observation sites of the Hungarian Soil Information and Monitoring System and (ii) the recently compiled national SOC maps., which provides a suitable and formerly tested spatial representation of the carbon stock of the Hungarian soils.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Acknowledgment:&lt;/strong&gt; Our research was supported by the Cooperative Doctoral Programme for Doctoral Scholarships (1015642) and by the OTKA thematic research projects K-131820 and K-124290 of the Hungarian National Research, Development and Innovation Office and by the Scholarship of Human Resource Supporter (NTP-NFT&amp;#214;-20-B-0022). Our project carried out using PRISMA Products, &amp;#169; of the Italian Space Agency (ASI), delivered under an ASI License to use.&lt;/p&gt;

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