scholarly journals Exploring the Potential of C-Band SAR in Contributing to Burn Severity Mapping in Tropical Savanna

2019 ◽  
Vol 12 (1) ◽  
pp. 49 ◽  
Author(s):  
Marius B. Philipp ◽  
Shaun R. Levick
Keyword(s):  
Satellite Imagery ◽  
Laser Scanning ◽  
Structural Changes ◽  
Fire Regime ◽  
Spatial Scales ◽  
Landscape Management ◽  
Carbon Accounting ◽  
Optical Data ◽  
Burn Severity ◽  
Sentinel 2

The ability to map burn severity and to understand how it varies as a function of time of year and return frequency is an important tool for landscape management and carbon accounting in tropical savannas. Different indices based on optical satellite imagery are typically used for mapping fire scars and for estimating burn severity. However, cloud cover is a major limitation for analyses using optical data over tropical landscapes. To address this pitfall, we explored the suitability of C-band Synthetic Aperture Radar (SAR) data for detecting vegetation response to fire, using experimental fires in northern Australia. Pre- and post-fire results from Sentinel-1 C-band backscatter intensity data were compared to those of optical satellite imagery and were corroborated against structural changes on the ground that we documented through terrestrial laser scanning (TLS). Sentinel-1 C-band backscatter (VH) proved sensitive to the structural changes imparted by fire and was correlated with the Normalised Burn Ratio (NBR) derived from Sentinel-2 optical data. Our results suggest that C-band SAR holds potential to inform the mapping of burn severity in savannas, but further research is required over larger spatial scales and across a broader spectrum of fire regime conditions before automated products can be developed. Combining both Sentinel-1 SAR and Sentinel-2 multi-spectral data will likely yield the best results for mapping burn severity under a range of weather conditions.

scholarly journals Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem

2017 ◽  
Vol 191 ◽  
pp. 95-109 ◽  
Author(s):  
Ran Meng ◽  
Jin Wu ◽  
Kathy L. Schwager ◽  
Feng Zhao ◽  
Philip E. Dennison ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
High Spatial Resolution ◽  
Spatial Scales ◽  
Burn Severity ◽  
Pine Barrens

scholarly journals Competition and Burn Severity Determine Post-Fire Sapling Recovery in a Nationally Protected Boreal Forest of China: An Analysis from Very High-Resolution Satellite Imagery

2019 ◽  
Vol 11 (6) ◽  
pp. 603 ◽  
Author(s):  
Lei Fang ◽  
Ellen Crocker ◽  
Jian Yang ◽  
Yan Yan ◽  
Yuanzheng Yang ◽  
...  
Keyword(s):  
High Resolution ◽  
Boreal Forest ◽  
Satellite Imagery ◽  
Fire Regime ◽  
Landsat Imagery ◽  
Burn Severity ◽  
Area Index ◽  
Legacy Effect ◽  
Very High ◽  
Tree Sapling

Anticipating how boreal forest landscapes will change in response to changing fire regime requires disentangling the effects of various spatial controls on the recovery process of tree saplings. Spatially explicit monitoring of post-fire vegetation recovery through moderate resolution Landsat imagery is a popular technique but is filled with ambiguous information due to mixed pixel effects. On the other hand, very-high resolution (VHR) satellite imagery accurately measures crown size of tree saplings but has gained little attention and its utility for estimating leaf area index (LAI, m2/m2) and tree sapling abundance (TSA, seedlings/ha) in post-fire landscape remains untested. We compared the explanatory power of 30 m Landsat satellite imagery with 0.5-m WorldView-2 VHR imagery for LAI and TSA based on field sampling data, and subsequently mapped the distribution of LAI and TSA based on the most predictive relationships. A random forest (RF) model was applied to assess the relative importance and causal mechanisms of spatial controls on tree sapling recovery. The results showed that pixel percentage of canopy trees (PPCT) derived from VHR imagery outperform all Landsat-derived spectral indices for explaining variance of LAI (R2VHR = 0.676 vs. R2Landsat = 0.427) and TSA (R2VHR = 0.508 vs. R2Landsat = 0.499). The RF model explained an average of 55.5% (SD = 3.0%, MSE = 0.382, N = 50) of the variation of estimated LAI. Understory vegetation coverage (competition) and post-fire surviving mature trees (seed sources) were the most important spatial controls for LAI recovery, followed by burn severity (legacy effect), topographic factors (environmental filter) and nearest distance to unburned area (edge effect). These analyses allow us to conclude that in our study area, mitigating wildfire severity and size may increase forest resilience to wildfire damage. Given the easily-damaged seed banks and relatively short seed dispersal distance of coniferous trees, reasonable human help to natural recovery of coniferous forests is necessary for severe burns with a large patch size, particularly in certain areas. Our research shows the VHR WorldView-2 imagery better resolves key characteristics of forest landscapes like LAI and TSA than Landsat imagery, providing a valuable tool for land managers and researchers alike.

Fire regimes and post-fire evolution of burned areas in selected plant biomes of the planet studying the phenology of the landscape with time series of satellite images

2021 ◽  
Author(s):  
Nikos Koutsias ◽  
Anastasia Karamitsou ◽  
Foula Nioti ◽  
Frank Coutelieris
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Fire History ◽  
Fire Regime ◽  
Fire Regimes ◽  
Burned Areas ◽  
History Of ◽  
Sentinel 2

<p>Plant biomes and climatic zones are characterized by a specific type of fire regime which can be determined from the history of fires in the area and it is a synergy mainly of the climatic conditions and the functional characteristics of the types of vegetation. They correspond also to specific phenology types, a feature that can be useful for various applications related to vegetation monitoring, especially when remote sensing methods are used. Both the assessment of fire regime from the reconstruction of fire history and the monitoring of post-fire evolution of the burned areas can be studied with satellite remote sensing based on satellite time series images. The free availability of (i) Landsat satellite imagery by US Geological Survey (USGS, (ii) Sentinel-2 satellite imagery by ESA and (iii) MODIS satellite imagery by NASA / USGS allow low-cost data acquisition and processing (eg 1984-present) which otherwise would require very high costs. The purpose of this work is to determine the fire regime as well as the patterns of post-fire evolution of burned areas in selected vegetation/climate zones for the entire planet by studying the phenology of the landscape with time series of satellite images. More specifically, the three research questions we are negotiating are: (i) the reconstruction of the history of fires in the period 1984-2017 and the determination of fire regimes with Landsat and Sentinel-2 satellite data , (ii) the assessment of pre-fire phenological pattern of vegetation and (iii) the monitoring and comparative evaluation of post-fire evolution patterns of the burned areas.</p><p><strong>Acknowledgements</strong></p><p>This research has been co-financed by the Operational Program "Human Resources Development, Education and Lifelong Learning" and is co-financed by the European Union (European Social Fund) and Greek national funds.</p><p> </p>

Biogeomorphology from Space: Using optical satellite imagery time series for analyzing the dynamic interaction of vegetation and hydromorphology along the Naryn River, Kyrgyzstan

2020 ◽  
Author(s):  
Florian Betz ◽  
Magdalena Lauermann ◽  
Gregory Egger ◽  
Bernd Cyffka
Keyword(s):  
Time Series ◽  
Satellite Imagery ◽  
Large Scale ◽  
Spatial Scales ◽  
Landsat Imagery ◽  
Natural State ◽  
Vegetation Development ◽  
Statistical Trends ◽  
River Corridors ◽  
Sentinel 2

<p>Under natural conditions, the structure and development of river corridors is controlled by an interplay of hydrological, geomorphological and ecological processes. Over the past decade, the concept of biogeomorphology has become increasingly popular to describe and analyze the manifold feedback mechanisms within river systems leading to an increasing number of studies. However, the majority of this work focuses either on conceptual development or on investigations on the scales of single geomorphic units or study reaches. Only very few studies enlarge the spatial scale to entire river corridors or networks despite the fact that recent frameworks emphasize these scales to be relevant for river research and management. A recent trend in remote sensing of terrestrial ecosystem is the use of dense imagery time series to assess trends and disturbances of vegetation development. In this study, we transfer this idea to the analysis of biogeomorphological interactions within fluvial environments on large spatial scales. We take the Naryn River in Kyrgyzstan as an example for demonstrating our satellite time series approach to biogeomorphological analysis of river corridors. The Naryn is still in a natural state on an entire flow length of more than 600 km with full longitudinal and lateral connectivity. Along the central part of the catchment, the Naryn is a highly dynamic braided river system shaped by the annual summer floods of a glacial discharge regime. This makes this river ideal to study large scale biogeomorphological dynamics. In our study, we follow the well-established concept of biogeomorphological succession suggested by Dov Corenblit and his colleagues. We mapped the different succession phases in the field and used the results to derive spectral-temporal indices of the succession phases. These indices are on the one hand used for a supervised classification based on Sentinel-2 imagery. On the other hand, we use Sentinel-2 as well as the longer term Landsat imagery time series to analyze the data for statistical trends and changepoints and evaluate this regarding biogeomorphological succession and disturbance events. The results reveal that dense time series of optical satellite imagery are well suited data sources to derive indicators of biogeomorphological interactions on large spatial scales. Especially when using the recently available Sentinel-2 imagery, such indicators have the potential to analyze biogeomorphological dynamics of entire river corridors or networks in a spatially and temporally continuous way at a reasonable spatial resolution.</p>

scholarly journals Comparison of Capability of SAR and Optical Data in Mapping Forest above Ground Biomass Based on Machine Learning

2020 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Negar Tavasoli ◽  
Hossein Arefi
Keyword(s):  
Machine Learning ◽  
Carbon Stock ◽  
Vegetation Indices ◽  
Forest Biomass ◽  
Principal Component ◽  
Optical Data ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Texture Characteristics ◽  
Sentinel 2

Assessment of forest above ground biomass (AGB) is critical for managing forest and understanding the role of forest as source of carbon fluxes. Recently, satellite remote sensing products offer the chance to map forest biomass and carbon stock. The present study focuses on comparing the potential use of combination of ALOSPALSAR and Sentinel-1 SAR data, with Sentinel-2 optical data to estimate above ground biomass and carbon stock using Genetic-Random forest machine learning (GA-RF) algorithm. Polarimetric decompositions, texture characteristics and backscatter coefficients of ALOSPALSAR and Sentinel-1, and vegetation indices, tasseled cap, texture parameters and principal component analysis (PCA) of Sentinel-2 based on measured AGB samples were used to estimate biomass. The overall coefficient (R2) of AGB modelling using combination of ALOSPALSAR and Sentinel-1 data, and Sentinel-2 data were respectively 0.70 and 0.62. The result showed that Combining ALOSPALSAR and Sentinel-1 data to predict AGB by using GA-RF model performed better than Sentinel-2 data.

scholarly journals Terrestrial Laser Scanning for Vegetation Analyses with a Special Focus on Savannas

2021 ◽  
Vol 13 (3) ◽  
pp. 507
Author(s):  
Tasiyiwa Priscilla Muumbe ◽  
Jussi Baade ◽  
Jenia Singh ◽  
Christiane Schmullius ◽  
Christian Thau
Keyword(s):  
Remote Sensing ◽  
Vegetation Structure ◽  
Laser Scanning ◽  
Spatial Scales ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Parameter Extraction ◽  
Airborne Lidar ◽  
Special Focus ◽  
Vegetation Parameter

Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome.

scholarly journals Effects on Lignin Redistribution in Eucalyptus globulus Fibres Pre-Treated by Steam Explosion: A Microscale Study to Cellulose Accessibility

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 507
Author(s):  
Eduardo Troncoso-Ortega ◽  
Rosario del P. Castillo ◽  
Pablo Reyes-Contreras ◽  
Patricia Castaño-Rivera ◽  
Regis Teixeira Mendonça ◽  
...  
Keyword(s):  
Enzymatic Hydrolysis ◽  
Eucalyptus Globulus ◽  
Steam Explosion ◽  
Laser Scanning ◽  
Structural Changes ◽  
Micro Particles ◽  
Cellulose Accessibility ◽  
Ft Ir ◽  
Enzymatic Accessibility ◽  
Physical And Chemical

The objective of this study was to investigate structural changes and lignin redistribution in Eucalyptus globulus pre-treated by steam explosion under different degrees of severity (S0), in order to evaluate their effect on cellulose accessibility by enzymatic hydrolysis. Approximately 87.7% to 98.5% of original glucans were retained in the pre-treated material. Glucose yields after the enzymatic hydrolysis of pre-treated material improved from 19.4% to 85.1% when S0 was increased from 8.53 to 10.42. One of the main reasons for the increase in glucose yield was the redistribution of lignin as micro-particles were deposited on the surface and interior of the fibre cell wall. This information was confirmed by laser scanning confocal fluorescence and FT-IR imaging; these microscopic techniques show changes in the physical and chemical characteristics of pre-treated fibres. In addition, the results allowed the construction of an explanatory model for microscale understanding of the enzymatic accessibility mechanism in the pre-treated lignocellulose.

scholarly journals Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Johannes Schumacher ◽  
Marius Hauglin ◽  
Rasmus Astrup ◽  
Johannes Breidenbach
Keyword(s):  
Forest Inventory ◽  
Regression Models ◽  
Laser Scanning ◽  
Site Index ◽  
National Forest Inventory ◽  
Airborne Laser Scanning ◽  
National Forest ◽  
Practical Applications ◽  
Airborne Laser ◽  
Sentinel 2

Abstract Background The age of forest stands is critical information for forest management and conservation, for example for growth modelling, timing of management activities and harvesting, or decisions about protection areas. However, area-wide information about forest stand age often does not exist. In this study, we developed regression models for large-scale area-wide prediction of age in Norwegian forests. For model development we used more than 4800 plots of the Norwegian National Forest Inventory (NFI) distributed over Norway between latitudes 58° and 65° N in an 18.2 Mha study area. Predictor variables were based on airborne laser scanning (ALS), Sentinel-2, and existing public map data. We performed model validation on an independent data set consisting of 63 spruce stands with known age. Results The best modelling strategy was to fit independent linear regression models to each observed site index (SI) level and using a SI prediction map in the application of the models. The most important predictor variable was an upper percentile of the ALS heights, and root mean squared errors (RMSEs) ranged between 3 and 31 years (6% to 26%) for SI-specific models, and 21 years (25%) on average. Mean deviance (MD) ranged between − 1 and 3 years. The models improved with increasing SI and the RMSEs were largest for low SI stands older than 100 years. Using a mapped SI, which is required for practical applications, RMSE and MD on plot level ranged from 19 to 56 years (29% to 53%), and 5 to 37 years (5% to 31%), respectively. For the validation stands, the RMSE and MD were 12 (22%) and 2 years (3%), respectively. Conclusions Tree height estimated from airborne laser scanning and predicted site index were the most important variables in the models describing age. Overall, we obtained good results, especially for stands with high SI. The models could be considered for practical applications, although we see considerable potential for improvements if better SI maps were available.

scholarly journals Estimating Aboveground Biomass Using Sentinel-2 MSI Data and Ensemble Algorithms for Grassland in the Shengjin Lake Wetland, China

2021 ◽  
Vol 13 (8) ◽  
pp. 1595
Author(s):  
Chunhua Li ◽  
Lizhi Zhou ◽  
Wenbin Xu
Keyword(s):  
Aboveground Biomass ◽  
Carbon Accounting ◽  
Dynamic Monitoring ◽  
Gradient Boosting ◽  
Biomass Estimation ◽  
Floodplain Wetlands ◽  
Red Edge ◽  
Extreme Gradient Boosting ◽  
Ensemble Algorithms ◽  
Sentinel 2

Wetland vegetation aboveground biomass (AGB) directly indicates wetland ecosystem health and is critical for water purification, carbon cycle, and biodiversity conservation. Accurate AGB estimation is essential for the monitoring and supervision of ecosystems, especially in seasonal floodplain wetlands. This paper explored the capability of spectral and texture features from the Sentinel-2 Multispectral Instrument (MSI) for modeling grassland AGB using random forest (RF) and extreme gradient boosting (XGBoost) algorithms in Shengjin Lake wetland (a Ramsar site). We use five-fold cross-validation to verify the model effectiveness. The results indicated that the RF and XGBoost models had a robust and efficient performance (with root mean square error (RMSE) of 126.571 g·m−2 and R2 of 0.844 for RF, RMSE of 112.425 g·m−2 and R2 of 0.869 for XGBoost), and the XGBoost models, by contrast, performed better. Both traditional and red-edge vegetation indices (VIs) obtained satisfactory results of AGB estimation (RMSE = 127.936 g·m−2, RMSE = 125.879 g·m−2 in XGBoost models, respectively), with the red-edge VIs contributed more to the AGB models. Moreover, we selected eight gray-level co-occurrence matrix (GLCM) textures calculated by four processing window sizes using the mean value of four offsets, and further analyzed the results of three analysis sets. Textures derived from traditional and red-edge bands using a 7 × 7 window size performed better in biomass estimation. This finding suggested that textures derived from the traditional bands were as important as the red-edge bands. The introduction of textures moderately improved the accuracy of modeling AGB, whereas the use of textures alo ne was not satisfactory. This research demonstrated that using the Sentinel-2 MSI and the two ensemble algorithms is an effective method for long-term dynamic monitoring and assessment of grass AGB in seasonal floodplain wetlands, which can support sustainable management and carbon accounting of wetland ecosystems.

scholarly journals A low-cost technique to measure bank erosion proces ses along middle-size river reaches

2018 ◽  
Author(s):  
Gonzalo Duró ◽  
Alessandra Crosato ◽  
Maarten G. Kleinhans ◽  
Wim S. J. Uijttewaal
Keyword(s):  
Laser Scanning ◽  
Spatial Scales ◽  
Low Cost ◽  
Bank Erosion ◽  
Water Levels ◽  
Small Scale ◽  
Observation Distance ◽  
River Bank ◽  
Erosion Processes ◽  
Rtk Gps

Abstract. Diverse methods are currently available to measure river bank erosion at broad-ranging temporal and spatial scales. Yet, no technique provides low-cost and high-resolution to survey small-scale bank processes along a river reach. We investigate the capabilities of Structure-from-Motion photogrammetry applied with imagery from an Unmanned Aerial Vehicle (UAV) to describe the evolution of riverbank profiles in middle-size rivers. The bank erosion cycle is used as a reference to assess the applicability of different techniques. We surveyed 1.2 km of a restored bank of the Meuse River eight times within a year, combining different photograph perspectives and overlaps to identify an efficient UAV flight to monitor banks. The accuracy of the Digital Surface Models (DSMs) was evaluated compared with RTK GPS points and an Airborne Laser Scanning (ALS) of the whole reach. An oblique perspective with eight photo overlaps was sufficient to achieve the highest relative precision to observation distance of ~1:1400, with 10 cm error range. A complementary nadiral view increased coverage behind bank toe vegetation. The DSM and ALS had comparable accuracies except on banks, where the latter overestimates elevations. Sequential DSMs captured signatures of the erosion cycle such as mass failures, slump-block deposition, and bank undermining. Although this technique requires low water levels and banks without dense vegetation, it is a low-cost method to survey reach-scale riverbanks in sufficient resolution to quantify bank retreat and identify morphological features of the bank failure and erosion processes.

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