Spectral analysis of charcoal on soils: implicationsfor wildland fire severity mapping methods

2010 ◽  
Vol 19 (7) ◽  
pp. 976 ◽  
Author(s):  
Alistair M. S. Smith ◽  
Jan U. H. Eitel ◽  
Andrew T. Hudak
Keyword(s):  
Spectral Analysis ◽  
Soil Type ◽  
Large Scale ◽  
Vegetation Index ◽  
Wildland Fire ◽  
Fire Severity ◽  
Mapping Method ◽  
Normalised Difference Vegetation Index ◽  
National Programs

Recent studies in the Western United States have supported climate scenarios that predict a higher occurrence of large and severe wildfires. Knowledge of the severity is important to infer long-term biogeochemical, ecological, and societal impacts, but understanding the sensitivity of any severity mapping method to variations in soil type and increasing charcoal (char) cover is essential before widespread adoption. Through repeated spectral analysis of increasing charcoal quantities on six representative soils, we found that addition of charcoal to each soil resulted in linear spectral mixing. We found that performance of the Normalised Burn Ratio was highly sensitive to soil type, whereas the Normalised Difference Vegetation Index was relatively insensitive. Our conclusions have potential implications for national programs that seek to monitor long-term trends in wildfire severity and underscore the need to collect accurate soils information when evaluating large-scale wildland fires.

scholarly journals Identifying Ecosystem Function Shifts in Africa Using Breakpoint Analysis of Long-Term NDVI and RUE Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1894
Author(s):  
Thomas P. Higginbottom ◽  
Elias Symeonakis
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Vegetation Index ◽  
Change Point Analysis ◽  
Annual Rainfall ◽  
Small Scale ◽  
Growth Season ◽  
Continental Scale ◽  
Normalised Difference Vegetation Index

Time-series of vegetation greenness data, derived from Earth-observation imagery, have become a key source of information for studying large-scale environmental change. The ever increasing length of such series allows for a range of indicators to be derived and for increasingly complex analyses to be applied. This study presents an analysis of trends in vegetation productivity—measured using the Global Inventory Monitoring and Modelling System third generation (GIMMS3g) Normalised Difference Vegetation Index (NDVI) data—for African savannahs, over the 1982–2015 period. Two annual metrics were derived from the 34 year dataset: the monthly, smoothed NDVI (the aggregated growth season NDVI) and the associated Rain Use Efficiency (growth season NDVI divided by annual rainfall). These indicators were then used in a BFAST-based change-point analysis, allowing the direction of change over time to change and the detection of one major break in the time-series. We also analysed the role of land cover type and climate zone as associations of the observed changes. Both methods agree that vegetation greening was pervasive across African savannahs, although RUE displayed less significant changes than NDVI. Monotonically increasing trends were the most common trend type for both indicators. The continental scale of the greening may suggest global processes as key drivers, such as carbon fertilization. That NDVI trends were more dynamic than RUE suggests that a large component of vegetation trends is driven by precipitation variability. Areas of negative trends were conspicuous by their minimalism. However, some patterns were apparent. In the southern Sahel and West Africa, declining NDVI and RUE overlapped with intensive population and agricultural regions. Dynamic trend reversals, in RUE and NDVI, located in Angola, Zambia and Tanzania, coincide with areas where a long-term trend of forest degradation and agricultural expansion has recently given way to increases in woody biomass. Meanwhile in southern Africa, monotonic increases in RUE with varying NDVI trend types may be indicative of shrub encroachment. However, all these processes are small-scale relative to the GIMMS NDVI data, and reconciling these conflicting drivers is not a trivial task. Our study highlights the importance of considering multiple options when undertaking trend analyses, as different inputs and methods can reveal divergent patterns.

scholarly journals Satellite-Observed Effects from Ozone Pollution and Climate Change on Growing-Season Vegetation Activity over China during 1982–2020

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1390
Author(s):  
Zhaosheng Wang
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Large Scale ◽  
Vegetation Index ◽  
Growing Season ◽  
Ozone Pollution ◽  
Vegetation Growth ◽  
Vegetation Activity ◽  
The Impact

Remote sensing vegetation index data contain important information about the effects of ozone pollution, climate change and other factors on vegetation growth. However, the absence of long-term observational data on surface ozone pollution and neglected air pollution-induced effects on vegetation growth have made it difficult to conduct in-depth studies on the long-term, large-scale ozone pollution effects on vegetation health. In this study, a multiple linear regression model was developed, based on normalized difference vegetation index (NDVI) data, ozone mass mixing ratio (OMR) data at 1000 hPa, and temperature (T), precipitation (P) and surface net radiation (SSR) data during 1982–2020 to quantitatively assess the impact of ozone pollution and climate change on vegetation growth in China on growing season. The OMR data showed an increasing trend in 99.9% of regions in China over the last 39 years, and both NDVI values showed increasing trends on a spatial basis with different ozone pollution levels. Additionally, the significant correlations between NDVI and OMR, temperature and SSR indicate that vegetation activity is closely related to ozone pollution and climate change. Ozone pollution affected 12.5% of NDVI, and climate change affected 26.7% of NDVI. Furthermore, the effects from ozone pollution and climate change on forest, shrub, grass and crop vegetation were evaluated. Notably, the impact of ozone pollution on vegetation growth was 0.47 times that of climate change, indicating that the impact of ozone pollution on vegetation growth cannot be ignored. This study not only deepens the understanding of the effects of ozone pollution and climate change on vegetation growth but also provides a research framework for the large-scale monitoring of air pollution on vegetation health using remote sensing vegetation data.

scholarly journals Crop loss identification at field parcel scale using satellite remote sensing and machine learning

2021 ◽  
Author(s):  
Santosh Hiremath ◽  
Samantha Wittke ◽  
Taru Palosuo ◽  
Jere Kaivosoja ◽  
Fulu Tao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Satellite Images ◽  
Vegetation Index ◽  
Reference Data ◽  
Insurance Companies ◽  
Crop Loss ◽  
Agricultural Field ◽  
Normalised Difference Vegetation Index ◽  
The Hard Problem

Identifying crop loss at field parcel scale using satellite images is challenging: first, crop loss is caused by many factors during the growing season; second, reliable reference data about crop loss are lacking; third, there are many ways to define crop loss. This study investigates the feasibility of using satellite images to train machine learning (ML) models to classify agricultural field parcels into those with and without crop loss. The reference data for this study was provided by Finnish Food Authority (FFA) containing crop loss information of approximately 1.4 million field parcels in Finland covering about 3.5 million ha from 2000 to 2015. This reference data was combined with Normalised Difference Vegetation Index (NDVI) derived from Landsat 7 images, in which more than 80\% of the possible data are missing. Despite the hard problem with extremely noisy data, among the four ML models we tested, random forest (with mean imputation and missing value indicators) achieved the average AUC (area under the ROC curve) of  [[EQUATION]] over all 16 years with the range [0.602, 0.795] in identifying new crop-loss fields based on reference fields of the same year. To our knowledge, this is one of the first large scale benchmark study of using machine learning for crop loss classification at field parcel scale. The classification setting and trained models have numerous potential applications, for example, allowing government agencies or insurance companies to verify crop-loss claims by farmers and realise efficient agricultural monitoring.

INFRASTRUCTURE POLICY OF THE CASPIAN STATES: DEVELOPMENT STRATEGY AND GEOPOLITICAL AIMS

2021 ◽  
Vol 22 (2) ◽  
pp. 068-077
Author(s):  
Sergey ZHILTSOV
Keyword(s):  
Development Strategy ◽  
Large Scale ◽  
Legal Status ◽  
Regional Cooperation ◽  
Negotiation Process ◽  
Infrastructure Development ◽  
The Caspian Sea ◽  
National Programs ◽  
Port Infrastructure

In recent years, the Caspian states have been paying increasingly more attention to port infrastructure development. The construction of new facilities and the reconstruction of existing ports received a new impetus. The Caspian states increase their investments in this sphere every year. They have developed national programs that aim to increase the volume of cargo passing through the Caspian states’ seaports. The development of port infrastructure, augmented by new railways, was deliberated by the Caspian states in the context of solving geopolitical problems. The advancement of the negotiation process on the Convention on the Legal Status of the Caspian Sea brought the solution of economic issues to the fore, along with the development of transport and related infrastructure. The signing of this document in August 2018 only raised the interest of the regional states in trade and economic cooperation. Accordingly, one of the key tasks that the Caspian states were facing was port infrastructure development. The construction of new ports was believed to foster achievement of long-term goals. In addition to economic development, first and foremost, of coastal territories, regional countries sought to reinforce their positions in global trade flows. Besides, the facilities constructed by the Caspian countries in recent years have been integrated in large-scale infrastructure projects, which are being actively promoted by non-regional states. China, the EU and Turkey have a stake in their implementation, and the Caspian infrastructure served as a part of regional transportation projects. The adoption of documents related to the development of transport in the Caspian region by the Caspian states reflected the importance of infrastructure. They formulated long-term tasks and outlined the spheres of cooperation with their regional neighbors. The expansion of regional cooperation by the Caspian countries is accompanied by the intensified struggle for the flow of goods. The Caspian states are growing increasingly competitive in the transportation sphere. The struggle for container traffic volumes and hydrocarbon resources is pushing the Caspian states to apply various financial and administrative mechanisms in order to attract cargo.

Mapping prescribed fire severity in south-east Australian eucalypt forests using modelling and satellite imagery: a case study

2017 ◽  
Vol 26 (6) ◽  
pp. 491 ◽  
Author(s):  
John Loschiavo ◽  
Brett Cirulis ◽  
Yingxin Zuo ◽  
Bronwyn A. Hradsky ◽  
Julian Di Stefano
Keyword(s):  
Satellite Imagery ◽  
Prescribed Fire ◽  
Vegetation Index ◽  
Fire Severity ◽  
Kappa Statistic ◽  
Fire Spread ◽  
Management Tool ◽  
Eucalypt Forest ◽  
Normalised Difference Vegetation Index ◽  
Eucalypt Forests

Accurate fire severity maps are fundamental to the management of flammable landscapes. Severity mapping methods have been developed and tested for wildfire, but need further refinement for prescribed fire. We evaluated the accuracy of two severity mapping methods for a low-intensity, patchy prescribed fire in a south-eastern Australian eucalypt forest: (1) the Normalised Difference Vegetation Index (NDVI) derived from RapidEye satellite imagery, and (2) PHOENIX RapidFire, a fire-spread simulation model. We used each method to generate a fire severity map (four-category: unburnt, low, moderate and severe), and then validated the maps against field-based data. We used error matrices and the Kappa statistic to assess mapping accuracy. Overall, the satellite-based map was more accurate (75%; Kappa±95% confidence interval 0.54±0.06) than the modelled map (67%; Kappa 0.40±0.06). Both methods overestimated the area of unburnt forest; however, the satellite-based map better represented moderately burnt areas. Satellite- and model-based methods both provide viable approaches for mapping prescribed fire severity, but refinements could further improve map accuracy. Appropriate severity mapping methods are essential given the increasing use of prescribed fire as a forest management tool.

The post-fire measurement of fire severity and intensity in the Christmas 2001 Sydney wildfires

2004 ◽  
Vol 13 (2) ◽  
pp. 227 ◽  
Author(s):  
Chris J. Chafer ◽  
Mark Noonan ◽  
Eloys Macnaught
Keyword(s):  
Vegetation Index ◽  
Fire Severity ◽  
Energy Levels ◽  
Satellite Image ◽  
Visual Assessment ◽  
Fuel Load ◽  
Fire Intensity ◽  
Simple Method ◽  
Affected Area ◽  
Normalised Difference Vegetation Index

Using pre- and post-fire satellite imagery from SPOT2, we examined the fire severity and intensity of the Christmas 2001 wildfires in the greater Sydney Basin, Australia. We computed a Normalised Difference Vegetation Index (NDVI) from the two satellite images captured before (November 2001) and after (January 2002) the wildfires, then subtracted the later from the former to produce a difference image (NDVIdiff) which was subsequently classified into six fire severity classes (unburnt, low, moderate, high, very high and extreme severity). We then tested the fire severity classification on 342 sample sites within the 225 000ha fire affected area using a qualitative visual assessment guide. We found that the NDVIdiff classification produced an accuracy of at least 88% (K hat = 0.86), with the greatest discrepancy being between the low and moderate classification. Knowledge of rate of spread over some of the affected area, coupled with a complete knowledge of fuel loads, was used to retrospectively model fire intensity, which in areas of extreme fire intensity, produced heat energy levels exceeding 70 000 kW m–1. Importantly, we found no positive effect of topography on fire severity, in fact finding an inverse relationship between slope and fire severity and no effect due to aspect. Further analysis showed that flat to moderate slopes less than 18° across all aspects suffered the greatest vegetal destruction, and there was no relationship between north-westerly aspects and fire severity. We also introduce a relatively simple method for estimating fuel load biomass using a combination of satellite image and rapid field assessment. We found 79% accuracy for this method based on 125 sample sites. It is postulated that this type of analysis can greatly improve our understanding of the spatial impact of fire, how natural areas within the fire ground were impacted, and how remote sensing and GIS technologies can be efficiently used in fire management planning and post-fire analysis.

scholarly journals Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach

2020 ◽  
Vol 17 (4) ◽  
pp. 1033-1061
Author(s):  
Christopher Krich ◽  
Jakob Runge ◽  
Diego G. Miralles ◽  
Mirco Migliavacca ◽  
Oscar Perez-Priego ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Vegetation Index ◽  
Terrestrial Ecosystems ◽  
Causal Discovery ◽  
Mediterranean Ecosystem ◽  
Climate Data ◽  
Normalised Difference Vegetation Index ◽  
Causal Networks ◽  
Causal Graphs

Abstract. The dynamics of biochemical processes in terrestrial ecosystems are tightly coupled to local meteorological conditions. Understanding these interactions is an essential prerequisite for predicting, e.g. the response of the terrestrial carbon cycle to climate change. However, many empirical studies in this field rely on correlative approaches and only very few studies apply causal discovery methods. Here we explore the potential for a recently proposed causal graph discovery algorithm to reconstruct the causal dependency structure underlying biosphere–atmosphere interactions. Using artificial time series with known dependencies that mimic real-world biosphere–atmosphere interactions we address the influence of non-stationarities, i.e. periodicity and heteroscedasticity, on the estimation of causal networks. We then investigate the interpretability of the method in two case studies. Firstly, we analyse three replicated eddy covariance datasets from a Mediterranean ecosystem. Secondly, we explore global Normalised Difference Vegetation Index time series (GIMMS 3g), along with gridded climate data to study large-scale climatic drivers of vegetation greenness. We compare the retrieved causal graphs to simple cross-correlation-based approaches to test whether causal graphs are considerably more informative. Overall, the results confirm the capacity of the causal discovery method to extract time-lagged linear dependencies under realistic settings. For example, we find a complete decoupling of the net ecosystem exchange from meteorological variability during summer in the Mediterranean ecosystem. However, cautious interpretations are needed, as the violation of the method's assumptions due to non-stationarities increases the likelihood to detect false links. Overall, estimating directed biosphere–atmosphere networks helps unravel complex multidirectional process interactions. Other than classical correlative approaches, our findings are constrained to a few meaningful sets of relations, which can be powerful insights for the evaluation of terrestrial ecosystem models.

scholarly journals A New Application of the Disturbance Index for Fire Severity in Coastal Dunes

2021 ◽  
Vol 13 (23) ◽  
pp. 4739
Author(s):  
Marcio D. DaSilva ◽  
David Bruce ◽  
Patrick A. Hesp ◽  
Graziela Miot da Silva
Keyword(s):  
Vegetation Index ◽  
Fire Severity ◽  
Coastal Dune ◽  
Burn Severity ◽  
Soil Conditions ◽  
Initiation Mechanism ◽  
Disturbance Index ◽  
Dune Systems ◽  
Normalised Difference Vegetation Index ◽  
Vegetation Loss

Fires are a disturbance that can lead to short term dune destabilisation and have been suggested to be an initiation mechanism of a transgressive dune phase when paired with changing climatic conditions. Fire severity is one potential factor that could explain subsequent coastal dune destabilisations, but contemporary evidence of destabilisation following fire is lacking. In addition, the suitability of conventional satellite Earth Observation methods to detect the impacts of fire and the relative fire severity in coastal dune environments is in question. Widely applied satellite-derived burn indices (Normalised Burn Index and Normalised Difference Vegetation Index) have been suggested to underestimate the effects of fire in heterogenous landscapes or areas with sparse vegetation cover. This work assesses burn severity from high resolution aerial and Sentinel 2 satellite imagery following the 2019/2020 Black Summer fires on Kangaroo Island in South Australia, to assess the efficacy of commonly used satellite indices, and validate a new method for assessing fire severity in coastal dune systems. The results presented here show that the widely applied burn indices derived from NBR differentially assess vegetation loss and fire severity when compared in discrete soil groups across a landscape that experienced a very high severity fire. A new application of the Tasselled Cap Transformation (TCT) and Disturbance Index (DI) is presented. The differenced Disturbance Index (dDI) improves the estimation of burn severity, relative vegetation loss, and minimises the effects of differing soil conditions in the highly heterogenous landscape of Kangaroo Island. Results suggest that this new application of TCT is better suited to diverse environments like Mediterranean and semi-arid coastal regions than existing indices and can be used to better assess the effects of fire and potential remobilisation of coastal dune systems.

scholarly journals Effects of Content of Soil Rock Fragments on Soil Erodibility in China

2022 ◽  
Vol 19 (2) ◽  
pp. 648
Author(s):  
Miaomiao Yang ◽  
Keli Zhang ◽  
Chenlu Huang ◽  
Qinke Yang
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Soil Type ◽  
Soil Profile ◽  
Redundancy Analysis ◽  
Vegetation Index ◽  
Vegetation Coverage ◽  
Soil Erodibility ◽  
Rock Fragments ◽  
Normalised Difference Vegetation Index

Soil erosion is serious in China—the soil in plateau and mountain areas contain a large of rock fragments, and their content and distribution have an important influence on soil erosion. However, there are still no complete results for calculating soil erodibility factor (K) that have corrected rock fragments in China. In this paper, the data available on rock fragments in the soil profile (RFP); rock fragments on the surface of the soil (RFS); and environmental factors such as elevation, terrain relief, slope, vegetation coverage (characterised by normalised difference vegetation index, NDVI), land use, precipitation, temperature, and soil type were used to explore the effects of content of soil rock fragments on calculating of K in China. The correlation analysis, typical sampling area analysis, and redundancy analysis were applied to analyse the effects of content of soil rock fragments on calculating of K and its relationship with environment factors. The results showed that (1) The rock fragments in the soil profile (RFP) increased K. The rock fragments on the surface (RFS) of the soil reduced K. The effect of both RFP and RFS reduced K. (2) The effect of rock fragments on K was most affected by elevation, followed by terrain relief, NDVI, slope, soil type, temperature, and precipitation, but had little correlation with land use. (3) The result of redundancy analysis showed elevation to be the main predominant factor of the effect of rock fragments on K. This study fully considered the effect of rock fragments on calculating of K and carried out a quantitative analysis of the factors affecting the effect of rock fragments on K, so as to provide necessary scientific basis for estimating K and evaluating soil erosion status in China more accurately.

scholarly journals Analyzing Future Rainfall Variations Over Southern Malay Peninsula Based On CORDEX-SEA Dataset

2021 ◽  
Author(s):  
Xiaosheng Qin ◽  
Chao Dai ◽  
Lilingjun Liu
Keyword(s):  
Large Scale ◽  
Southwest Monsoon ◽  
Mapping Method ◽  
Extreme Condition ◽  
Annual Rainfall ◽  
Study Region ◽  
Rainfall Characteristics ◽  
Long Run ◽  
Far Future

Abstract Gridded rainfall datasets based on various data sources and techniques have emerged to help describe the spatiotemporal features of rainfall patterns over large areas and have gained popularity in many regional/global climatic analyses. This study explored future variations of rainfall characteristics over peninsula Malaysia and Singapore region based on rainfall indices of PRCPTOT, Rx1day, Rx5day, R95pTOT, R1mm, and R20mm, under 9 CORDEX-SEA RCM datasets with RCP8.5 emission scenario. A monthly quantile delta mapping method (MQDM) was adopted for bias-correction of the RCM modelled data. It was indicated that all the studied rainfall indices have long-term variations both temporally and spatially. Generally, the further the future, the higher the variability and uncertainty of indices. For the study region, the relative increments of the medians from RCM models averaged over all climatic zones in the far future are 40.3%, 25.9%, and 4.7% for Rx1day, Rx5day and R95pTOT, respectively. The annual rainfall amount (PRCPTOT) in the long run would likely increase mainly in the northeast coastal zone and drop in most of other areas over the peninsula, with the median being -5.9% averaged over all zones. The frequency of wet days (R1mm) would generally drop over the whole peninsula, with the median averaged over all zones being -6.8% in the far future. The frequency of heavy rains (R20mm) would overall decrease (by -3.4% in average in the far future) but might still notably increase in the northeast zone (NE) at both annual and southwest monsoon. The extreme condition implied from various RCM models would be more alarming. The study result would be useful in revealing the essential spatiotemporal variations of rainfall over the peninsula from short- to long-term futures and supporting large-scale flood risk assessment and adaptation planning.

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