Predicting post-fire canopy mortality in the boreal forest from dNBR derived from time series of Landsat data

2016 ◽  
Vol 25 (7) ◽  
pp. 762 ◽  
Author(s):  
Ignacio San-Miguel ◽  
David W. Andison ◽  
Nicholas C. Coops ◽  
Gregory J. M. Rickbeil
Keyword(s):  
Boreal Forest ◽  
Natural Disturbance ◽  
Ground Truth ◽  
Cost Effective ◽  
Burn Severity ◽  
Ancillary Data ◽  
Validation Data ◽  
Ground Truth Data ◽  
Forest Fire Management ◽  
Canadian Boreal Forest

Regulatory and certification agencies need historical fire pattern information across the Canadian boreal forest to support natural disturbance-based management. Landsat-derived spectral indices have been used extensively to map burn severity in North America. However, satellite-derived burn severity is difficult to define and quantify, and relies heavily on ground truth data for validation, which hinders fire pattern analysis over broad scales. It is therefore critical to translate burn severity estimates into more quantifiable measurements of post-fire conditions and to provide more cost-effective methods to derive validation data. We assessed the degree to which Landsat-derived indices and ancillary data can be used to classify canopy mortality for 10 fires in the boreal forest of Alberta and Saskatchewan, Canada. Models based on two and three mortality classes had overall accuracies of 91 and 72% respectively. The three-level classification has more utility for resource management, with improved accuracy at predicting unburned and complete canopy mortality classes (93 and 66%), but is relatively inaccurate for the partial mortality class (56%). The results presented here can be used to assess the suitability of different canopy mortality models for forest fire management goals, to help provide objective, consistent and cost-effective results to analyse historical fire patterns across the Canadian boreal forest.

scholarly journals Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

2021 ◽  
Vol 13 (10) ◽  
pp. 1966
Author(s):  
Christopher W Smith ◽  
Santosh K Panda ◽  
Uma S Bhatt ◽  
Franz J Meyer ◽  
Anushree Badola ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Ground Truth ◽  
Burn Severity ◽  
Classification Methods ◽  
Spectral Indices ◽  
Ground Truth Data ◽  
Burn Scar ◽  
Interior Alaska ◽  
Remote Sensing Methods ◽  
The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

scholarly journals POST-FIRE FORESTRY RECOVERY MONITORING USING HIGH-RESOLUTION MULTISPECTRAL IMAGERY FROM UNMANNED AERIAL VEHICLES

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 301-305 ◽  
Author(s):  
L. Pádua ◽  
T. Adão ◽  
N. Guimarães ◽  
A. Sousa ◽  
E. Peres ◽  
...  
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicles ◽  
Tree Height ◽  
Ground Truth ◽  
Cost Effective ◽  
Forest Disturbances ◽  
Ground Truth Data ◽  
High Resolution Data ◽  
Aerial Vehicles ◽  
Fire Scenarios

<p><strong>Abstract.</strong> In recent years unmanned aerial vehicles (UAVs) have been used in several applications and research studies related to environmental monitoring. The works performed have demonstrated the suitability of UAVs to be employed in different scenarios, taking advantage of its capacity to acquire high-resolution data from different sensing payloads, in a timely and flexible manner. In forestry ecosystems, UAVs can be used with accuracies comparable with traditional methods to retrieve different forest properties, to monitor forest disturbances and to support disaster monitoring in fire and post-fire scenarios. In this study an area recently affected by a wildfire was surveyed using two UAVs to acquire multi-spectral data and RGB imagery at different resolutions. By analysing the surveyed area, it was possible to detect trees, that were able to survive to the fire. By comparing the ground-truth data and the measurements estimated from the UAV-imagery, it was found a positive correlation between burned height and a high correlation for tree height. The mean NDVI value was extracted used to create a three classes map. Higher NDVI values were mostly located in trees that survived that were not/barely affected by the fire. The results achieved by this study reiterate the effectiveness of UAVs to be used as a timely, efficient and cost-effective data acquisition tool, helping for forestry management planning and for monitoring forest rehabilitation in post-fire scenarios.</p>

Using spatially explicit simulations to explore size distribution and spacing of regenerating areas produced by wildfires: recommendations for designing harvest agglomerations for the Canadian boreal forest

2007 ◽  
Vol 83 (1) ◽  
pp. 72-83 ◽  
Author(s):  
Annie Belleau ◽  
Yves Bergeron ◽  
Alain Leduc ◽  
Sylvie Gauthier ◽  
Andrew Fall
Keyword(s):  
Forest Management ◽  
Boreal Forest ◽  
Size Distribution ◽  
Temporal Variability ◽  
Fire Regime ◽  
Natural Disturbance ◽  
Fire Regimes ◽  
Spatially Explicit ◽  
Fire Size ◽  
Canadian Boreal Forest

It is now recognized that in the Canadian boreal forest, timber harvesting activities have replaced wildfires as the main stand-replacing disturbance. Differences in landscape patterns derived from these two sources of disturbance have, however, raised concerns that the way forest harvesting has been dispersed is potentially shifting patterns away from the natural range. In the context of natural disturbance-based management, we used a spatially explicit model designed to capture general fire regimes in order to quantify temporal variability associated with regenerating areas (burnt areas of 25 years or younger), and to develop strategic objectives for harvest agglomeration sizes and dispersion. We first evaluated temporal variability in the proportion of stands younger than 100 years (assumed to be even-aged stands) for various fire regimes (seven fire cycles: 50 to 400 years, and three mean fires sizes: 3000, 15 000 and 60 000 ha). Secondly, we quantified the size distribution and dispersion of regenerating areas for each fire regime. As expected by theoretical fire frequencies and size distributions, the importance of even-aged stands at the forest management unit level was found to decrease with longer fire cycles. However, the temporal variability associated with these proportions is shown to increase with mean fire size. It was also observed that the size distribution and dispersion of regenerating areas was primarily influenced by mean fire size. Based on these observations, natural disturbance-based management objectives were formulated, providing guidelines on harvest agglomeration size and dispersion. Key words: temporal variability, boreal forest, fire regime, forest management, age distribution, fire size distribution, clearcut agglomeration size distribution

scholarly journals From Village to Globe: A Dynamic Real-Time Map of African Fields Through PlantVillage

2021 ◽  
Vol 5 ◽  
Author(s):  
Annalyse Kehs ◽  
Peter McCloskey ◽  
John Chelal ◽  
Derek Morr ◽  
Stellah Amakove ◽  
...  
Keyword(s):  
Real Time ◽  
Ground Truth ◽  
Cost Effective ◽  
Quality Data ◽  
Learning Tools ◽  
High Quality ◽  
Ground Truth Data ◽  
Cost Effective Method ◽  
High Throughput Method ◽  
Major Bottleneck

A major bottleneck to the application of machine learning tools to satellite data of African farms is the lack of high-quality ground truth data. Here we describe a high throughput method using youth in Kenya that results in a cost-effective method for high-quality data in near real-time. This data is presented to the global community, as a public good and is linked to other data sources that will inform our understanding of crop stress, particularly in the context of climate change.

scholarly journals Potentiality and Limitations of Open Satellite Data for Flood Mapping

2018 ◽  
Author(s):  
Davide Notti ◽  
Daniele Giordan ◽  
Fabiana Calò ◽  
Antonio Pepe ◽  
Francesco Zucca ◽  
...  
Keyword(s):  
Satellite Data ◽  
Low Cost ◽  
Ground Truth ◽  
Flood Mapping ◽  
Ancillary Data ◽  
Ground Truth Data ◽  
Flood Peak ◽  
Sar Data ◽  
Flooded Areas ◽  
Flood Maps

Satellite remote sensing is a powerful tool to map flooded areas. In the last years, the availability of free satellite data sensibly increased in terms of type and frequency, allowing producing flood maps at low cost around the World. In this work, we propose a semi-automatic method for flood mapping, based only on free satellite images and open-source software. As case studies, we selected three flood events recently occurred in Spain and Italy. Multispectral satellite data acquired by MODIS, Proba-V, Landsat, Sentinel-2 and SAR data collected by Sentinel-1 were used to detect flooded areas using different methodologies (e.g., MNDWI; SAR backscattering variation; Supervised classification). Then, we improved and manually refined the automatic mapping using free ancillary data like DEM based water depth model and available ground truth data. For the areas affected by major floods, we also validated and compared the produced flood maps with official maps made by river authorities. We calculated flood detection performance (flood ratio) for the different datasets we used. The results show that it is necessary to take into account different factors for the choice of best satellite data, among these, the time of satellite pass with respect to the flood peak is the most important one. SAR data showed good results only for co-flood acquisitions, whereas multispectral images allowed detecting flooded areas also with the post-flood acquisition. With the support of ancillary data, it was possible to produce reliable geomorphological based flood maps in the study areas.

Calculating macroalgal height and biomass using bathymetric LiDAR and a comparison with surface area derived from satellite data in Nova Scotia, Canada

2020 ◽  
Vol 63 (1) ◽  
pp. 43-59 ◽  
Author(s):  
Tim Webster ◽  
Candace MacDonald ◽  
Kevin McGuigan ◽  
Nathan Crowell ◽  
Jean-Sebastien Lauzon-Guay ◽  
...  
Keyword(s):  
Nova Scotia ◽  
Surface Area ◽  
Survey Data ◽  
Satellite Data ◽  
High Tide ◽  
Field Measurements ◽  
Ground Truth ◽  
Cost Effective ◽  
Satellite System ◽  
Ground Truth Data

AbstractThe ability to map and monitor the macroalgal coastal resource is important to both the industry and the regulator. This study evaluates topo-bathymetric lidar (light detection and ranging) as a tool for estimating the surface area, height and biomass of Ascophyllum nodosum, an anchored and vertically suspended (floating) macroalga, and compares the surface area derived from lidar and WorldView-2 satellite imagery. Pixel-based Maximum Likelihood classification of low tide satellite data produced 2-dimensional maps of intertidal macroalgae with overall accuracy greater than 80%. Low tide and high tide topo-bathymetric lidar surveys were completed in southwestern Nova Scotia, Canada. Comparison of lidar-derived seabed elevations with ground-truth data collected using a survey grade global navigation satellite system (GNSS) indicated the low tide survey data have a positive bias of 15 cm, likely resulting from the seaweed being draped over the surface. The high tide survey data did not exhibit this bias, although the suspended canopy floating on the water surface reduced the seabed lidar point density. Validation of lidar-derived seaweed heights indicated a mean difference of 30 cm with a root mean square error of 62 cm. The modelled surface area of seaweed was 28% greater in the lidar model than the satellite model. The average lidar-derived biomass estimate was within one standard deviation of the mean biomass measured in the field. The lidar method tends to overestimate the biomass compared to field measurements that were spatially biased to the mid-intertidal level. This study demonstrates an innovative and cost-effective approach that uses a single high tide bathymetric lidar survey to map the height and biomass of dense macroalgae.

scholarly journals Soil Bacterial and Fungal Response to Wildfires in the Canadian Boreal Forest Across a Burn Severity Gradient

2019 ◽  
Author(s):  
Thea Whitman ◽  
Ellen Whitman ◽  
Jamie Woolet ◽  
Mike D Flannigan ◽  
Dan K Thompson ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Microbial Communities ◽  
Community Composition ◽  
Soil Microbial Communities ◽  
Total Carbon ◽  
Burn Severity ◽  
Ecosystem Functions ◽  
Moisture Regime ◽  
Soil Microbial ◽  
Canadian Boreal Forest

Global fire regimes are changing, with increases in wildfire frequency and severity expected for many North American forests over the next 100 years. Fires can result in dramatic changes to C stocks and can restructure plant and microbial communities, which can have long-lasting effects on ecosystem functions. We investigated wildfire effects on soil microbial communities (bacteria and fungi) in an extreme fire season in the northwestern Canadian boreal forest, using field surveys, remote sensing, and high-throughput amplicon sequencing. We found that fire occurrence, along with vegetation community, moisture regime, pH, total carbon, and soil texture are all significant predictors of soil microbial community composition. Communities become increasingly dissimilar with increasingly severe burns, and the burn severity index (an index of the fractional area of consumed organic soils and exposed mineral soils) best predicted total bacterial community composition, while burned/unburned was the best predictor for fungi. Globally abundant taxa were identified as significant positive fire responders, including the bacteria Massilia sp. (64x more abundant with fire) and Arthrobacter sp. (35x), and the fungi Penicillium sp. (22x) and Fusicladium sp. (12x) Bacterial and fungal co-occurrence network modules were characterized by fire responsiveness as well as pH and moisture regime. Building on the efforts of previous studies, our results identify specific fire-responsive microbial taxa and suggest that accounting for burn severity improves our understanding of their response to fires, with potentially important implications for ecosystem functions.

scholarly journals Knowledge-Based Classification of Grassland Ecosystem Based on Multi-Temporal WorldView-2 Data and FAO-LCCS Taxonomy

2020 ◽  
Vol 12 (9) ◽  
pp. 1447 ◽  
Author(s):  
Maria Adamo ◽  
Valeria Tomaselli ◽  
Cristina Tarantino ◽  
Saverio Vicario ◽  
Giuseppe Veronico ◽  
...  
Keyword(s):  
Agricultural Practices ◽  
Ground Truth ◽  
Multiple Scale ◽  
Grassland Ecosystem ◽  
Local Conservation ◽  
Ancillary Data ◽  
Data Set ◽  
Ground Truth Data ◽  
Natural Grasslands ◽  
Multi Temporal

Grassland ecosystems can provide a variety of services for humans, such as carbon storage, food production, crop pollination and pest regulation. However, grasslands are today one of the most endangered ecosystems due to land use change, agricultural intensification, land abandonment as well as climate change. The present study explores the performance of a knowledge-driven GEOgraphic-Object—based Image Analysis (GEOBIA) learning scheme to classify Very High Resolution (VHR) images for natural grassland ecosystem mapping. The classification was applied to a Natura 2000 protected area in Southern Italy. The Food and Agricultural Organization Land Cover Classification System (FAO-LCCS) hierarchical scheme was instantiated in the learning phase of the algorithm. Four multi-temporal WorldView-2 (WV-2) images were classified by combining plant phenology and agricultural practices rules with prior-image spectral knowledge. Drawing on this knowledge, spectral bands and entropy features from one single date (Post Peak of Biomass) were firstly used for multiple-scale image segmentation into Small Objects (SO) and Large Objects (LO). Thereafter, SO were labelled by considering spectral and context-sensitive features from the whole multi-seasonal data set available together with ancillary data. Lastly, the labelled SO were overlaid to LO segments and, in turn, the latter were labelled by adopting FAO-LCCS criteria about the SOs presence dominance in each LO. Ground reference samples were used only for validating the SO and LO output maps. The knowledge driven GEOBIA classifier for SO classification obtained an OA value of 97.35% with an error of 0.04. For LO classification the value was 75.09% with an error of 0.70. At SO scale, grasslands ecosystem was classified with 92.6%, 99.9% and 96.1% of User’s, Producer’s Accuracy and F1-score, respectively. The findings reported indicate that the knowledge-driven approach not only can be applied for (semi)natural grasslands ecosystem mapping in vast and not accessible areas but can also reduce the costs of ground truth data acquisition. The approach used may provide different level of details (small and large objects in the scene) but also indicates how to design and validate local conservation policies.

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