Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery

A forest fire started on August 8th, 2016 in several places on Madeira Island causing damage and casualties. As of August 10th the local media had reported the death of three people, over 200 people injured, over 950 habitants evacuated, and 50 houses damaged. This study presents the preliminary results of the assessment of several spectral indices to evaluate the burn severity of Madeira fires during August 2016. These spectral indices were calculated using the new European satellite Sentinel-2A launched in June 2015. The study confirmed the advantages of several spectral indices such as Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Burn Ratio (NBR) and Normalized Difference Vegetation Index (NDVIreXn) using red-edge spectral bands to assess the post-fire conditions. Results showed high correlation between NDVI, GNDVI, NBR and NDVIre1n spectral indices and the analysis performed by Copernicus Emergency Management Service (EMSR175), considered as the reference truth. Regarding the red-edge spectral indices, the NDVIre1n (using band B5, 705 nm) presented better results compared with B6 (740 nm) and B7 (783 nm) bands. These preliminary results allow us to assume that Sentinel-2 will be a valuable tool for post-fire monitoring. In the future, the two twin Sentinel-2 satellites will offer global coverage of the Madeira Archipelago every five days, therefore allowing the simultaneous study of the evolution of the burnt area and reforestation information with high spatial (up to 10 m) and temporal resolution (5 days).     

Pre-harvest wheat yield prediction using agromet-spectral-trend-yield models for hoshiarpur and rupnagar districts of punjab

Wheat yield prediction using different agrometeorological indices, spectral index (NDVI, Normalized Difference Vegetation Index) and trend predicted yield (TPY) were developed in Hoshiarpur and Rupnagar districts of Punjab. On the basis of examination of Correlation Coefficients (R), Standard Error of Estimate (SEOE) and Relative Deviation (RD) values resulted from different agromet models, the best agromet subset were selected as Minimum Temperature (T_min), Maximum Temperature (T_max) and accumulated Heliothermal Units (HTU) in case of Hoshiarpur district and Minimum Temperature (T--_min), accumulated Temperature Difference (TD) and accumulated Pan Evaporation (E) for Rupnagar district at reproductive stage (2^nd week of March) of wheat. It was found that Agromet-Spectral-Trend-Yield model could explain 96 % (SEOE = 87 kg/ha) and 91 % (SEOE = 146 kg/ha) of wheat yield variations for Hoshiarpur and Rupnagar districts, respectively.     

Assessment of crop growth parameters of wheat under stress condition through ground based spectral data

A field experiment was conducted on wheat crop during rabi seasons of 1995–96, 1996–97 and 1997–98 to study the spectral response of wheat crop (between 490 to 1080 nm) under water and nutrient stress condition. An indigenously developed ground truth radiometer having narrow band in visible and near infrared region (490 – 1080 nm) was used. Vegetation indices derived using different band combinations and related to crop growth parameters. The near infrared spectral region of 710 – 1025 nm was found most important for monitoring stress condition. Relationship has been developed between crop growth parameters and vegetation indices. Leaf Area Index (LAI) and chlorophyll could be predicted by knowing different reflectance ratios at milking stage of crop with R² value of 0.78 and 0.89, respectively. Dry biomass (DBM), Plant Water Content (PWC) and grain yield are also significantly related with reflectance ratios at flowering stage of crop with R² value of 0.90, 0.98 and 0.74, respectively.     

Yield Estimation Model and Water Productivity of Wheat Crop (Triticum aestivum) in an Irrigation Command Using Remote Sensing and GIS

Crop yield estimation has an important role on economy development and its accuracy and speed influence yield price and helps in deciding the excess or deficit production conditions. The water productivity evaluates the irrigation command through water use efficiency (WUE). Remote sensing (RS) and geographical information system (GIS) techniques were used for crop yield and water productivity estimation of wheat crop (Triticum aestivum) grown in Tarafeni South Main Canal (TSMC) irrigation command of West Bengal State in India. One IRS P6 image and four wide field sensor (WiFS) images for different months of winter season were used to determine the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) for area under wheat crop. The temporally and spatially distributed spectral growth profile and AREASUM of NDVI (A_NDVI) and SAVI (A_SAVI) with time after sowing of wheat crop were developed and correlated with actual crop yield of wheat (Y_act). The developed relationships between A_SAVI and Y_act resulted high correlation in comparison to that of A_NDVI. Using the developed model the RS based wheat yield (Y_RS) predicted from A_SAVI varied on entire TSMC irrigation command from 22.67 to 33.13 q ha⁻¹ respectively, which gave an average yield of 26.50 q ha⁻¹. The RS generated yield based water use efficiency (WUE_YRS) for water supplied from canal of TSMC irrigation command was found to be 6.69 kg ha⁻¹ mm⁻¹.     

Evaluating and comparing Sentinel 2A and Landsat-8 Operational Land Imager (OLI) spectral indices for estimating fire severity in a Mediterranean pine ecosystem of Greece

The main purpose of this study is to explore the relationship between three field-based fire severity indices (Composite Burn Index-CBI, Geometrically structure CBI, weighted CBI) and spectral indices derived from Sentinel 2A and Landsat-8 OLI imagery on a recent large fire in Thasos, Greece. We employed remotely sensed indices previously used from the remote sensing fire community (Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), differenced NDVI, differenced NBR, relative differenced NBR, Relativized Burn Ratio) and seven Sentinel 2A-specific indices considering the availability of spectral information recorded in the red-edge spectral region. The statistical correlation indicated a slightly stronger relationship between the differenced NBR and the GeoCBI for both Sentinel 2A (r = 0.872) and Landsat-8 OLI (r = 0.845) imagery. Predictive local thresholds of dNBR values showed slightly higher classification accuracy for Sentinel 2A (73.33%) than Landsat-8 OLI (71.11%), suggesting the adequacy of Sentinel 2A for forest fire severity assessment and mapping in Mediterranean pine ecosystems. The evaluation of the classification thresholds calculated in this study over other fires with similar pre-fire conditions could contribute in the operational mapping and reconstruction of the historical patterns of fire severity over the Eastern Mediterranean region.     

基于植被指数模型的淡水湖泊湿地景观信息提取

高邮湖湿地是江苏省重要湿地之一,对生态、环境控制、调节气候和保护生物多样性具有重要意义。采用2007年的LandsatTM影像作为遥感信息源,选择影像的光谱特征和比值植被指数(RVI)、差值植被指数(DVI)、归一化植被指数(NDVI)、归一化差异绿度指数(NDGI)、土壤调节植被指数(SAVI)和最佳土壤调节植被指数(OSAVI)6种植被指数做了光谱特征分析,从而确定出最佳指数模型,并基于决策树方法,实现研究区景观信息的遥感分类。研究结果表明,决策树分类法易于综合多种特征进行遥感影像分类,植被指数参与到决策树分类中能够提高分类的总体精度,其总体精度达到79.58%,Kappa系数为0.721 0,分类结果理想且人工参与灵活。     

Relationship of Hyperspectral Reflectance Indices with Leaf N and P Concentration,Dry Matter Accumulation and Grain Yield of Wheat

A field experiment was conducted on wheat during rabi season of year 2010–2011 and 2011–2012 at IARI, New Delhi to study the reflectance response of wheat to the nutrient omissions and identify the appropriate indices for assessing the nutrient deficiencies. Treatments comprised omission of N, P, K, S and Zn, 50% omission of N, P, and K, absolute control and optimum dose of nutrition (150–26.4–50–15–3 kg/ha N–P–K–S–Zn). The R² were significant and higher for the hyperspectral indices than the broad band vegetation indices. GMI-I, RI-2 dB and RI-3d, GNDVI, VOGa, VOGb, VOGc, ND₇₀₅, PRI, PSNDc and REIP had higher R² (>0.61) for the leaf N concentration. The hyperspectral indices having highly significant correlation with leaf P concentration were PSSRc, GMI-1, ZM, RI-half, VOGa, VOGb, VOGc, mSR and REIP. Among the indices analysed PSSRc, GMI-I, VOGa, RI-2 dB, RI-3 dB, GNDVI, VOGb, VOGc and ND₇₀₅ had almost a similar degree of relationship with DM accumulation with R² values ranging from 0.70 to 0.73. However, REIP displayed a higher degree of relationship with leaf N concentration, drymatter accumulation and grain yield as indicated by R² of 0.85, 0.81 and 0.95 (P = ≤0.01), respectively. It can be concluded from the study that among the hyperspectral indices REIP had a highly significant relationship with leaf N concentration, DM accumulation and grain yield. However, for leaf P concentration several hyperspectral indices viz PSSRc, GMI-1, ZM, RI-half, VOGa, VOGb, VOGc, mSR had though significant but almost similar R² values.     

SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity

Fires are a problematic and recurrent issue in Mediterranean ecosystems. Accurate discrimination between burn severity levels is essential for the rehabilitation planning of burned areas. Sentinel-2A MultiSpectral Instrument (MSI) record data in three red-edge wavelengths, spectral domain especially useful on agriculture and vegetation applications. Our objective is to find out whether Sentinel-2A MSI red-edge wavelengths are suitable for burn severity discrimination. As study area, we used the 2015 Sierra Gata wildfire (Spain) that burned approximately 80 km². A Copernicus Emergency Management Service (EMS)-grading map with four burn severity levels was considered as reference truth. Cox and Snell, Nagelkerke and McFadde pseudo-R² statistics obtained by Multinomial Logistic Regression showed the superiority of red-edge spectral indices (particularly, Modified Simple Ratio Red-edge, Chlorophyll Index Red-edge, Normalized Difference Vegetation Index Red-edge) over conventional spectral indices. Fisher's Least Significant Difference test confirmed that Sentinel-2A MSI red-edge spectral indices are adequate to discriminate four burn severity levels.     

Cotton Crop Discrimination Using Fuzzy Classification Approach

Crop growth information represented through temporal remote sensing data is of great importance for specific agriculture crop discrimination. In this paper, the effect of various indices was empirically investigated using temporal images for cotton crop discrimination. Five spectral indices SR (Simple Ratio), NDVI (Normalized Difference Vegetation index), TNDVI (Transformed Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index) and TVI (Triangular Vegetation Index) were investigated to identify cotton crop using temporal multi-spectral images. Data used for this study was AWIFS (coarser resolution) for soft classification and LISS-III (medium coarser) data for soft testing from Resourcesat-1 (IRS-P6) satellite. The mixed pixel (i.e. multiple classes within a single pixel) problem had been handled using soft computing techniques. Possibilistic fuzzy classification approach is used to handle mixed pixels for extracting single class of interest. The classification results with respect to various indices were compared in terms of image to image fuzzy overall classification accuracy. It was observed that temporal SAVI indices database with data set-2 outperformed other temporal indices database for cotton crop discrimination. Temporal SAVI indices database gave highest fuzzy overall accuracy of 93.12% with data set-2 in comparison to others.     

不同传感器的模拟植被指数对水稻叶面积指数的估测精度和敏感性分析

本研究利用水稻冠层高光谱数据,模拟NOAA-AVHRR,Terra-MODIS和Landsat-TM的可见光波段反射率数据,计算各传感器的多种植被指数(NDVI,RVI,EVI,GNDVI,GRVI和Red-edge RVI),比较植被指数模型对水稻LAI的估测精度,分析不同植被指数对LAI变化的敏感性.相对于红波段植被指数,红边比值植被指数(Red-edge RVI)和绿波段指数GRVI与LAI有更好的线性相关关系,而GNDVI和LAI呈现更好的对数相关关系.MODIS的Red-edge RVI指数不仅模型拟合的精度最高,还有独立数据验证的估测精度也最高,而且它的验证精度较拟合精度下降幅度最小;其次是绿波段构建的GNDVI和GRVI植被指数的估测精度,再次是NDVI和EVI的估测精度,而RVI的估测精度最差.敏感性分析发现,13个植被指数对水稻LAI的估测能力都随着LAI的增加而下降,但归一化类植被指数和比值类植被指数对LAI变化反应的差异明显,归一化类植被指数在LAI较低时(LAI<1.5)对LAI变化的反应开始非常敏感,但迅速下降,而比值类植被指数在LAI较低时,明显小于归一化类植被指数,之后随着LAI的增大(LAI>1.5)比值类植被指数对LAI的变化敏感性,则明显高于归一化类植被指数.Red-edge RVI和绿波段指数GRVI和LAI不仅表现了很好的线性相关关系,而且在LAI大于2.9左右保持较高的敏感性.     

Spectral models for estimation of chlorophyll content, growth and yield of wheat crop

Field experiments were conducted during 1998–99 and 1999–2000 at research farm of the Department of Agricultural Meteorology, CCS Haryana Agricultural University, Hisar. Five wheat cultivars: WH 542, PBW 343, UP 2338, Raj 3765 and Sonak were sown on 25^th November, 10^th and 25^th December with four nitrogen levels viz., no nitrogen. 50, 100 and 150% of recommended dose. Leaf area index, dry matter at anthesis, final dry biomass and grain yield were recorded in all the treatments. Chlorophyll and wax contents of wheat leaves were estimated at different growth stages. Multiband spectral reflectance was measured using hand-held radiometer. Spectral indices such as simple ratio, normalized difference, transformed vegetation index, perpendicular vegetation index and greenness index were computed using the multiband spectral data. Values of all the spectral indices were maximum in 25 November sown crop with maximum dose of nitrogen (180 kg N ha^-1). PBW 343 showed higher values of all the spectral indices in comparison with other cultivars. The spectral indices recorded during maximum leaf area index stage were correlated with crop parameters. Using stepwise regression, empirical models for chlorophyll, leaf area index, dry biomass and yield prediction were developed. The ’R²’ values of these models ranged between 0.87 and 0.95.     

Fuzzy Based Approach for Moist Deciduous Forest Identification Using MODIS Temporal Data

Fuzzy based soft classification have been used immensely for handling the mixed pixel and hence to extract the single class of interest. The present research attempts to extract the moist deciduous forest from MODIS temporal data using the Possibilistic c-Means (PCM) soft classification approach. Temporal MODIS (7 dates) data were used to identify moist deciduous forest and temporal AWiFS (7 dates) data were used as reference data for testing. The Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Transformed Normalized Difference Vegetation Index (TNDVI) were used to generate the temporal vegetation indices for both the MODIS and the AWiFS datasets. It was observed from the research that the MODIS temporal NDVI data set1, which contain the minimum number of images and avoids the temporal images corresponding to the highest frequency stages of onset of greenness (OG) and end of senescence (ES) activity of moist deciduous forest have been found most suitable data set for identification of moist deciduous forest with the maximum fuzzy overall accuracy of 96.731 %.     

Understanding the multi-seasonal spectral and biophysical characteristics of reedbed habitats in the UK

Reedbed in the UK has been classified as priority habitat for most regional Biodiversity Partnerships. However, critical information pertaining to the quality and spatial coverage of reedbed is currently lacking. This paper presents the results of a project conducted in collaboration with the Cumbria Wildlife Trust and Environment Agency aimed at monitoring and understanding variations in the spectral reflectance and biophysical properties of reedbed canopies across Leighton Moss Nature Reserve in Lancashire, northwest England. Throughout the seasonal phenological cycle of the reedbed habitats in the study area, optimal spectral indices required for quantifying its biophysical properties were determined using field spectroscopy and ground-based measurements. Results of the experiment showed that the narrow-band-derived Difference Vegetation Index (DVI) and Renormalised Difference Vegetation Index (RDVI), with the correlation coefficient R² of 0.77 and 0.72, respectively, provided the most accurate estimates of the leaf area index for the reedbed canopies.     

Discrimination and classification of mangrove forests using EO-1 Hyperion data: a case study of Indian Sundarbans

In remote sensing the identification accuracy of mangroves is greatly influenced by terrestrial vegetation. This paper deals with the use of specific vegetation indices for extracting mangrove forests using Earth Observing-1 Hyperion image over a portion of Indian Sundarbans, followed by classification of mangroves into floristic composition classes. Five vegetation indices (three new and two published), namely Mangrove Probability Vegetation Index, Normalized Difference Wetland Vegetation Index, Shortwave Infrared Absorption Index, Normalized Difference Infrared Index and Atmospherically Corrected Vegetation Index were used in decision tree algorithm to develop the mangrove mask. Then, three full-pixel classifiers, namely Minimum Distance, Spectral Angle Mapper and Support Vector Machine (SVM) were evaluated on the data within the mask. SVM performed better than the other two classifiers with an overall precision of 99.08%. The methodology presented here may be applied in different mangrove areas for producing community zonation maps at finer levels.     

Isolating type-specific phenologies through spectral unmixing of satellite time series

Vegetation phenology is commonly studied using time series of multi-spectral vegetation indices derived from satellite imagery. Differences in reflectance among land-cover and/or plant functional types are obscured by sub-pixel mixing, and so phenological analyses have typically sought to maximize the compositional purity of input satellite data by increasing spatial resolution. We present an alternative method to mitigate this ‘mixed-pixel problem’ and extract the phenological behavior of individual land-cover types inferentially, by inverting the linear mixture model traditionally used for sub-pixel land-cover mapping. Parameterized using genetic algorithms, the method takes advantage of the discriminating capacity of calibrated surface reflectance measurements in red, near infrared, and short-wave infrared wavelengths, as well as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index. In simulation, the unmixing procedure reproduced the reflectances and phenological signals of grass, crop, and deciduous forests with high fidelity (RMSE?相似文献   

Cotton yield prediction through spectral parameters

To predict the crop yield from spectral parameters, a field experiment was conducted on cotton crop during 1997-98 Kharif season on a sandy loam soil at the Punjab Agricultural Unjversity, Ludhiana. India. Spectral reflectance and agronomic measurements were made for cotton species (American and Desi cotton), sown on two dates (May 1 and May 29) under five nitrogen levels (0, 40, 80, 120 and 160 kg/ha). Regression analysis showed that growth variables had poor correlation with seed cotton yield for all three models, however, yield attributes were significantly and highly correlated for second degree model with seed cotton yield. The integrated Radiance Ratio (RR) and Normalized Difference Vegetation Index (NDVI) measured over time were significantly correlated quadratically with seed cotton yield on three time segment periods viz., 81–110, 111–140 and 141–200 DAS, but highest correlation values were obtained during 81–110 DAS, In American cotton, the highest correlation coefficient for RR and NDVI were 0.91 and 0.81, respectively; whereas for Desi cotton these values were 0.88 and 0.84, respectively.     

Analysis of spectral characteristics among different sensors by use of simulated RS images

This research, by use of RS image-simulating method, simulated apparent reflectance images at sensor level and ground-reflectance images of SPOT-HRV, CBERS-CCD Landsat-TM and NOAA14-AVHRR's corresponding bands. These images were used to analyze sensor's differences caused by spectral sensitivity and atmospheric impacts. The differences were analyzed on Normalized Difference Vegetation Index(NDVI). The results showed that the differences of sensors' spectral characteristics cause changes of their NDVI and reflectance. When multiple sensors' data are applied to digital analysis, the error should be taken into account. Atmospheric effect makes NDVI smaller, and atmospheric correction has the tendency of increasing NDVI values. The reflectance and their NDVIs of different sensors can be used to analyze the differences among sensor's features. The spectral analysis method based on RS simulated images can provide a new way to design the spectral characteristics of new sensors.     

基于指数分析法的西安市土地利用变化及驱动力研究

基于2000和2007年2期TM遥感影像,利用指数分析法,分别提取出归一化差异建筑指数(NDBI)、修正归一化差异水体指数(MNDWI)和归一化差异植被指数(NDVI)3种指数模型,分别代表西安市的3种最主要的土地利用类型--建筑用地、水体和植被.采用神经网络分类器进行监督分类,借助ERDAS Imagine 9.0、ENVI、ArcGIS 9.2和Matlab等软件平台,计算出西安市土地利用类型的动态转移矩阵,构建了土地利用变化动态度指数模型,定量分析西安市土地利用的时空变化.依据研究区土地利用变化的结果分析,变化的驱动力因子主要是人口增长、经济增长和政策变动.     

Investigating the vegetation and agricultural responses to El Nino/Southern Oscillation using AVHRR data

This study explores the possible linkages of El Nino/Southern Oscillation (ENSO) with vegetation and rainfall patterns, vegetation activity and food grain yields, in arid and semi-arid regions of western India. A sequence of 20-year (1981–2000) monthly maximum Normalized Difference Vegetation Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) and monthly rainfall from 160 stations were examined to study the seasonal patterns and their relation to ENSO activity. In addition, a direct (ENSO-crop yield) linkage and an intermediate (ENSO-NDVI) linkage of agricultural responses to ENSO were also investigated. The results indicate below-normal seasonal NDVI and rainfall associated with El Nino (warm) events, except during 1997, while positive anomalies occur during La Nina (cold) events. Sea surface temperature (SST) anomalies from NINO 3 region (5°N–5°S; 150°W–90°W), as an indicator of ENSO were significantly correlated with NDVI anomalies, rainfall anomalies and yield anomalies but the Southern Oscillation Index (SOI) was significantly related to NDVI anomalies only. NDVI anomaly patterns correspond to rainfall variability including that associated with ENSO activity. The observed strong intermediate linkage between yield anomalies and NDVI anomaly signal (r = 0.609) indicates that NDVI is an ideal index for understanding and analysing agricultural response to ENSO climate teleconnections.     

Historical forest biomass dynamics modelled with Landsat spectral trajectories

Estimation of forest aboveground biomass (AGB) is informative of the role of forest ecosystems in local and global carbon budgets. There is a need to retrospectively estimate biomass in order to establish a historical baseline and enable reporting of change. In this research, we used temporal spectral trajectories to inform on forest successional development status in support of modelling and mapping of historic AGB for Mediterranean pines in central Spain. AGB generated with ground plot data from the Spanish National Forest Inventory (NFI), representing two collection periods (1990 and 2000), are linked with static and dynamic spectral data as captured by Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors over a 25 year period (1984–2009). The importance of forest structural complexity on the relationship between AGB and spectral vegetation indices is revealed by the analysis of wavelet transforms. Two-dimensional (2D) wavelet transforms support the identification of spectral trajectory patterns of forest stands that in turn, are associated with traits of individual NFI plots, using a flexible algorithm sensitive to capturing time series similarity. Single-date spectral indices, temporal trajectories, and temporal derivatives associated with succession are used as input variables to non-parametric decision trees for modelling, estimation, and mapping of AGB and carbon sinks over the entire study area. Results indicate that patterns of change found in Normalized Difference Vegetation Index (NDVI) values are associated and relate well to classes of forest AGB. The Tasseled Cap Angle (TCA) index was found to be strongly related with forest density, although the related patterns of change had little relation with variability in historic AGB. By scaling biomass models through small (∼2.5 ha) spatial objects defined by spectral homogeneity, the AGB dynamics in the period 1990–2000 are mapped (70% accuracy when validated with plot values of change), revealing an increase of 18% in AGB irregularly distributed over 814 km² of pines. The accumulation of C calculated in AGB was on average 0.65 t ha⁻¹ y⁻¹, equivalent to a fixation of 2.38 t ha⁻¹ y⁻¹ of carbon dioxide.