Using UAV Multispectral Images for Classification of Forest Burn Severity—A Case Study of the 2019 Gangneung Forest Fire

Unmanned aerial vehicle (UAV)-based remote sensing has limitations in acquiring images before a forest fire, although burn severity can be analyzed by comparing images before and after a fire. Determining the burned surface area is a challenging class in the analysis of burn area severity because it looks unburned in images from aircraft or satellites. This study analyzes the availability of multispectral UAV images that can be used to classify burn severity, including the burned surface class. RedEdge multispectral UAV image was acquired after a forest fire, which was then processed into a mosaic reflectance image. Hundreds of samples were collected for each burn severity class, and they were used as training and validation samples for classification. Maximum likelihood (MLH), spectral angle mapper (SAM), and thresholding of a normalized difference vegetation index (NDVI) were used as classifiers. In the results, all classifiers showed high overall accuracy. The classifiers also showed high accuracy for classification of the burned surface, even though there was some confusion among spectrally similar classes, unburned pine, and unburned deciduous. Therefore, multispectral UAV images can be used to analyze burn severity after a forest fire. Additionally, NDVI thresholding can also be an easy and accurate method, although thresholds should be generalized in the future.

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Análise multitemporal da regeneração natural da candeia após ocorrência de incêndio florestal

Revista Verde de Agroecologia e Desenvolvimento Sustentável ◽

10.18378/rvads.v12i2.4719 ◽

2017 ◽

Vol 12 (2) ◽

pp. 192

Author(s):

Ivy Mayara Sanches de Oliveira ◽

Alex Donizeti Sales ◽

Eduarda Martiniano de Oliveira Silveira ◽

Fausto Weimar Acerbi Júnior ◽

José Marcio De Mello

Keyword(s):

Forest Fire ◽

Natural Regeneration ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Minas Gerais ◽

Landsat Tm ◽

Fire Occurrence ◽

Regeneration Pattern ◽

Serra Do Cipó ◽

Before And After

Os sensores de satélites têm a capacidade de fornecer informações sobre regiões afetadas pela atividade do fogo, sendo uma ferramenta eficiente para a detecção e quantificação destas áreas. Objetivou-se avaliar o comportamento da regeneração natural da candeia Eremanthus incanus (Less.) Less, após a ocorrência de incêndio florestal por meio do índice de vegetação da diferença normalizada (NDVI) de forma a identificar a capacidade de resiliência da espécie. O incêndio ocorreu em 1999, ao lado do Parque Nacional da Serra do Cipó no município de Morro do Pilar, Minas Gerais. Foi selecionada uma série temporal de quatro imagens adquiridas entre os anos de 1999 a 2005 do satélite Landsat (TM e ETM+). Foram geradas as imagens NDVI e em seguida foram obtidos seus valores de reflectância nas diferentes datas para analisar o comportamento espectral das áreas em regeneração. Posteriormente esses parâmetros foram utilizados para analisar as alterações na cobertura vegetal. Ao comparar os valores de NDVI antes e pós-incêndio, observou-se que num período de 6 anos a candeia apresenta valores de reflectância próximos àqueles encontrados antes do incêndio, o que sugere que a cobertura vegetal está num estágio similar à antes da ocorrência do fogo. O índice aplicado mostrou-se eficiente na análise da capacidade de resiliência da espécie após o fogo. Multitemporal analysis of natural regeneration of Candeia after occurrence of forest fire<pre>Abstract: Satellite sensors have the ability to provide information on areas affected by fire activity, being an efficient tool for detection and quantification of these areas. The aim of this study was to analyze the natural regeneration pattern of the Eremanthus incanus (Less.) Less, after the occurrence of forest fire, using the normalized difference vegetation index (NDVI) in order to identify its resilience capacity. The fire occurred in 1999, next to the Serra do Cipó National Park in Morro do Pilar city, Minas Gerais. A time series of four Landsat (TM e ETM</pre>+<pre>) images acquired between the years 1999-2005 were selected. The NDVI images were generated and their reflectance values were obtained at the different dates to analyze the spectral pattern of regenerating areas. Later, these parameters were used to analyze the vegetation cover changes. Comparing the NDVI values before and after the fire, it was observed that, over a period of 6 years the reflectance values were close to those found before the fire, which suggests that the vegetal cover is at a similar stage before the fire occurrence. The applied index proved to be efficient in the analysis of the species capacity of resilience after the fire occurrence.</pre>

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Riverscape classification by using machine learning in combination with satellite and UAV images

10.5194/egusphere-egu2020-9428 ◽

2020 ◽

Author(s):

Hitoshi Miyamoto ◽

Takuya Sato ◽

Akito Momose ◽

Shuji Iwami

Keyword(s):

Machine Learning ◽

Satellite Images ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Training Data ◽

Low Resolution ◽

Critical Areas ◽

Aerial Vehicle ◽

Uav Images

This presentation&#160;examined&#160;a new method for classifying riverine land covers by using the machine learning technique applied to both the satellite and UAV&#160;(Unmanned Aerial Vehicle) images in a Kurobe River channel.&#160; The method used Random Forests (RF) for the classification with RGBs and NDVIs (Normalized Difference Vegetation Index) of the images in combination.&#160; In the process, the high-resolution UAV images made it possible to create accurate training data for the land cover classification of the low-resolution satellite images.&#160; The results indicated that the combination of the high- and low-resolution images in the machine learning could effectively detect waters, gravel/sand beds, trees, and grasses from the satellite images with a certain degree of accuracy.&#160; In contrast, the usage of only low-resolution satellite images failed to detect the vegetation difference between trees and grasses.&#160; These results could actively support the effectiveness of the present machine learning method in the combination of satellite and UAV images to grasp the most critical areas in riparian vegetation management.

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Urban Fire Severity and Vegetation Dynamics in Southern California

Remote Sensing ◽

10.3390/rs13010019 ◽

2020 ◽

Vol 13 (1) ◽

pp. 19

Author(s):

Lauren E. H. Mathews ◽

Alicia M. Kinoshita

Keyword(s):

Vegetation Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Current Knowledge ◽

Fire Severity ◽

Satellite Image ◽

Burn Severity ◽

Native Vegetation ◽

Riparian Areas ◽

The Impact

A combination of satellite image indices and in-field observations was used to investigate the impact of fuel conditions, fire behavior, and vegetation regrowth patterns, altered by invasive riparian vegetation. Satellite image metrics, differenced normalized burn severity (dNBR) and differenced normalized difference vegetation index (dNDVI), were approximated for non-native, riparian, or upland vegetation for traditional timeframes (0-, 1-, and 3-years) after eleven urban fires across a spectrum of invasive vegetation cover. Larger burn severity and loss of green canopy (NDVI) was detected for riparian areas compared to the uplands. The presence of invasive vegetation affected the distribution of burn severity and canopy loss detected within each fire. Fires with native vegetation cover had a higher severity and resulted in larger immediate loss of canopy than fires with substantial amounts of non-native vegetation. The lower burn severity observed 1–3 years after the fires with non-native vegetation suggests a rapid regrowth of non-native grasses, resulting in a smaller measured canopy loss relative to native vegetation immediately after fire. This observed fire pattern favors the life cycle and perpetuation of many opportunistic grasses within urban riparian areas. This research builds upon our current knowledge of wildfire recovery processes and highlights the unique challenges of remotely assessing vegetation biophysical status within urban Mediterranean riverine systems.

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Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation

Scientific Reports ◽

10.1038/s41598-021-82797-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Zhou Tang ◽

Atit Parajuli ◽

Chunpeng James Chen ◽

Yang Hu ◽

Samuel Revolinski ◽

...

Keyword(s):

Prediction Model ◽

High Throughput ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Selection Process ◽

Forage Legume ◽

Breeding Programs ◽

High Throughput Phenotyping ◽

Fully Automatic ◽

Uav Images

AbstractAlfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50–70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green–Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.

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The Use of Satellite Information (MODIS/Aqua) for Phenological and Classification Analysis of Plant Communities

Forests ◽

10.3390/f10070561 ◽

2019 ◽

Vol 10 (7) ◽

pp. 561 ◽

Cited By ~ 3

Author(s):

Yulia Ivanova ◽

Anton Kovalev ◽

Oleg Yakubailik ◽

Vlad Soukhovolsky

Keyword(s):

Time Series ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Plant Communities ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Weather Conditions ◽

Linear Discriminant ◽

Canonical Correlations

Vegetation indices derived from remote sensing measurements are commonly used to describe and monitor vegetation. However, the same plant community can have a different NDVI (normalized difference vegetation index) depending on weather conditions, and this complicates classification of plant communities. The present study develops methods of classifying the types of plant communities based on long-term NDVI data (MODIS/Aqua). The number of variables is reduced by introducing two integrated parameters of the NDVI seasonal series, facilitating classification of the meadow, steppe, and forest plant communities in Siberia using linear discriminant analysis. The quality of classification conducted by using the markers characterizing NDVI dynamics during 2003–2017 varies between 94% (forest and steppe) and 68% (meadow and forest). In addition to determining phenological markers, canonical correlations have been calculated between the time series of the proposed markers and the time series of monthly average air temperatures. Based on this, each pixel with a definite plant composition can be characterized by only four values of canonical correlation coefficients over the entire period analyzed. By using canonical correlations between NDVI and weather parameters and employing linear discriminant analysis, one can obtain a highly accurate classification of the study plant communities.

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Mapping Forest Fire Risk—A Case Study in Galicia (Spain)

Remote Sensing ◽

10.3390/rs12223705 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3705

Author(s):

Ana Novo ◽

Noelia Fariñas-Álvarez ◽

Joaquín Martínez-Sánchez ◽

Higinio González-Jorge ◽

José María Fernández-Alonso ◽

...

Keyword(s):

Forest Fire ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Fire Risk ◽

Fire Regimes ◽

Slope Aspect ◽

Fire Weather Index ◽

Novel Approach ◽

Fuel Model ◽

Forest Fire Risk

The optimization of forest management in roadsides is a necessary task in terms of wildfire prevention in order to mitigate their effects. Forest fire risk assessment identifies high-risk locations, while providing a decision-making support about vegetation management for firefighting. In this study, nine relevant parameters: elevation, slope, aspect, road distance, settlement distance, fuel model types, normalized difference vegetation index (NDVI), fire weather index (FWI), and historical fire regimes, were considered as indicators of the likelihood of a forest fire occurrence. The parameters were grouped in five categories: topography, vegetation, FWI, historical fire regimes, and anthropogenic issues. This paper presents a novel approach to forest fire risk mapping the classification of vegetation in fuel model types based on the analysis of light detection and ranging (LiDAR) was incorporated. The criteria weights that lead to fire risk were computed by the analytic hierarchy process (AHP) and applied to two datasets located in NW Spain. Results show that approximately 50% of the study area A and 65% of the study area B are characterized as a 3-moderate fire risk zone. The methodology presented in this study will allow road managers to determine appropriate vegetation measures with regards to fire risk. The automation of this methodology is transferable to other regions for forest prevention planning and fire mitigation.

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Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination

Mathematical Problems in Engineering ◽

10.1155/2017/6824051 ◽

2017 ◽

Vol 2017 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Long Zhao ◽

Pan Zhang ◽

Xiaoyi Ma ◽

Zhuokun Pan

Keyword(s):

Time Series ◽

Land Cover ◽

Information Extraction ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Classification And Regression Tree ◽

Support Vector ◽

Land Resource ◽

Spectral Angle Mapper ◽

Ndvi Time Series

A timely and accurate understanding of land cover change has great significance in management of area resources. To explore the application of a daily normalized difference vegetation index (NDVI) time series in land cover classification, the present study used HJ-1 data to derive a daily NDVI time series by pretreatment. Different classifiers were then applied to classify the daily NDVI time series. Finally, the daily NDVI time series were classified based on multiclassifier combination. The results indicate that support vector machine (SVM), spectral angle mapper, and classification and regression tree classifiers can be used to classify daily NDVI time series, with SVM providing the optimal classification. The classifiers of K-means and Mahalanobis distance are not suited for classification because of their classification accuracy and mechanism, respectively. This study proposes a method of dimensionality reduction based on the statistical features of daily NDVI time series for classification. The method can be applied to land resource information extraction. In addition, an improved multiclassifier combination is proposed. The classification results indicate that the improved multiclassifier combination is superior to different single classifier combinations, particularly regarding subclassifiers with greater differences.

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Mapeamento e Quantificação da Cobertura Vegetal no Agreste Central de Pernambuco Utilizando Técnicas de Empilhamento e o NDVI (Mapping and Quantification of Vegetation Cover from Central Agreste Region of Pernambuco State Using NDVI Technique)

Revista Brasileira de Geografia Física ◽

10.26848/rbgf.v3i3.232664 ◽

2011 ◽

Vol 3 (3) ◽

pp. 157

Author(s):

Daniel Rodrigues Lira ◽

Maria do Socorro Bezerra de Araújo ◽

Everardo Valadares De Sá Barretto Sampaio ◽

Hewerton Alves da Silva

Keyword(s):

Remote Sensing ◽

Vegetation Cover ◽

Satellite Images ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Regional Scale ◽

Digital Processing ◽

Sparse Vegetation ◽

New Sensors

O mapeamento e monitoramento da cobertura vegetal receberam consideráveis impulsos nas últimas décadas, com o advento do sensoriamento remoto, processamento digital de imagens e políticas de combate ao desmatamento, além dos avanços nas pesquisas e gerações de novos sensores orbitais e sua distribuição de forma mais acessível aos usuários, tornam as imagens de satélite um dos produtos do sensoriamento remoto mais utilizado para análises da cobertura vegetal das terras. Os índices de cobertura vegetal deste trabalho foram obtidos usando o NDVI - Normalized Difference Vegetation Index para o Agreste central de Pernambuco indicou 39,7% de vegetação densa, 13,6% de vegetação esparsa, 14,3% de vegetação rala e 10,5% de solo exposto. O NDVI apresentou uma caracterização satisfatória para a classificação do estado da vegetação do ano de 2007 para o Agreste Central pernambucano, porém ocorreu uma confusão com os índices de nuvens, sombras e solos exposto, necessitando de uma adaptação na técnica para um melhor aprimoramento da diferenciação desses elementos, constituindo numa recombinação de bandas após a elaboração e calculo do NDVI.Palavras-chave: Geoprocessamento; sensoriamento remoto; índice de vegetação. Mapping and Quantification of Vegetation Cover from Central Agreste Region of Pernambuco State Using NDVI Technique ABSTRACTIn recent decades, advanced techniques for mapping and monitoring vegetation cover have been developed with the advent of remote sensing. New tools for digital processing, the generation of new sensors and their orbital distribution more accessible have facilitated the acquisition and use of satellite images, making them one of the products of remote sensing more used for analysis of the vegetation cover. The aim of this study was to assess the vegetation cover from Central Agreste region of Pernambuco State, using satellite images TM / LANDSAT-5. The images were processed using the NDVI (Normalized Difference Vegetation Index) technique, generating indexes used for classification of vegetation in dense, sparse and scattered. There was a proportion of 39.7% of dense vegetation, 13.6% of sparse vegetation, 14.3% of scattered vegetation and 10.5% of exposed soil. NDVI technique has been used as a useful tool in the classification of vegetation on a regional scale, however, needs improvement to a more precise differentiation among levels of clouds, shadow, exposed soils and vegetation. Keywords: Geoprocessing, remote sensing, vegetation index

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Influence of the use and the land cover of the catchment in the water quality of the semiarid tropical reservoirs

Journal of Hyperspectral Remote Sensing ◽

10.29150/jhrs.v7.7.p389-398 ◽

2018 ◽

Vol 7 (7) ◽

pp. 389

Author(s):

Herika Cavalcante ◽

Patrícia Silva Cruz ◽

Leandro Gomes Viana ◽

Daniely De Lucena Silva ◽

José Etham De Lucena Barbosa

Keyword(s):

Water Quality ◽

Supervised Classification ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Landsat 8 ◽

Dissolved Phosphorus ◽

Sparse Vegetation ◽

Tropical Reservoirs

The aim of this study was to evaluate some parameters of water quality of semiarid reservoirs under different uses and occupation of the catchment’s soil. For this, the reservoirs Acauã and Boqueirão, belonging to the Paraíba do Norte river watershed and Middle and Upper course sub catchments, respectively, were studied. For this, water samples were collected in August, September and October 2016. From these samples, total and dissolved phosphorus, nitrate, nitrite, ammonia, chlorophyll, dissolved and suspended solids were analyzed. In addition, images of the Landsat 8 satellite were acquired for the calculation of the Normalized Difference Vegetation Index (NDVI), and for the supervised classification of the use and occupation of the sub catchments. Thus, it was observed that, in general, the Acauã reservoir presented values of phosphorus and nitrogen and solids larger than the Boqueirão reservoir, due to the greater urban area, even though it had a smaller total area of the basin. Both reservoirs presented low vegetation rates and high areas of sparse vegetation and exposed soil, increasing the propensity to soil erosion and the transport of nutrients from the basin to the reservoirs, making water quality worse or impossible.

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LANDSAT-BASED DETECTION AND SEVERITY ANALYSIS OF BURNED SUGARCANE PLOTS IN TARLAC, PHILIPPINES USING DIFFERENCED NORMALIZED BURN RATIO (dNBR)

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w1-173-2016 ◽

2016 ◽

Vol XLII-4/W1 ◽

pp. 173-179 ◽

Cited By ~ 2

Author(s):

A. B. Baloloy ◽

A. C. Blanco ◽

B. S. Gana ◽

R. C. Sta. Ana ◽

L. C. Olalia

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

The Philippines ◽

Local Economy ◽

Burn Severity ◽

Burned Area ◽

Landsat 8 ◽

Potential Yield ◽

Differenced Normalized Burn Ratio ◽

Normalized Burn Ratio

The Philippines has a booming sugarcane industry contributing about PHP 70 billion annually to the local economy through raw sugar, molasses and bioethanol production (SRA, 2012). Sugarcane planters adapt different farm practices in cultivating sugarcane, one of which is cane burning to eliminate unwanted plant material and facilitate easier harvest. Information on burned sugarcane extent is significant in yield estimation models to calculate total sugar lost during harvest. Pre-harvest burning can lessen sucrose by 2.7% - 5% of the potential yield (Gomez, et al 2006; Hiranyavasit, 2016). This study employs a method for detecting burn sugarcane area and determining burn severity through Differenced Normalized Burn Ratio (dNBR) using Landsat 8 Images acquired during the late milling season in Tarlac, Philippines. Total burned area was computed per burn severity based on pre-fire and post-fire images. Results show that 75.38% of the total sugarcane fields in Tarlac were burned with post-fire regrowth; 16.61% were recently burned; and only 8.01% were unburned. The monthly dNBR for February to March generated the largest area with low severity burn (1,436 ha) and high severity burn (31.14 ha) due to pre-harvest burning. Post-fire regrowth is highest in April to May when previously burned areas were already replanted with sugarcane. The maximum dNBR of the entire late milling season (February to May) recorded larger extent of areas with high and low post-fire regrowth compared to areas with low, moderate and high burn severity. Normalized Difference Vegetation Index (NDVI) was used to analyse vegetation dynamics between the burn severity classes. Significant positive correlation, rho = 0.99, was observed between dNBR and dNDVI at 5% level (p = 0.004). An accuracy of 89.03% was calculated for the Landsat-derived NBR validated using actual mill data for crop year 2015-2016.

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