Evy Index Research Articles

Principal component analysis and biophysical parameters in the assessment of soil salinity in the irrigated perimeter of Bahia, Brazil

Soil salinity is an environmental concern which can lead to land desertification. It is a common problem in the semiarid region of northeastern Brazil and its impacts extend from the environment to the economy. The objective of this study was to evaluate soil salinity in the dry period in an agricultural area of irrigated Maniçoba perimeter, in Juazeiro, State of Bahia, Brazil. The study was carried out in an agricultural area of the irrigated perimeter in Juazeiro of Bahia, Brazil, where 98 disturbed soil samples were collected in different areas, being 33 samples to obtain Electrical Conductivity (EC) (dS m−1) and regression models, and 65 samples to validate the models. Images from the Landsat-8 and Sentinel-2 satellites were used to obtain the biophysical parameters: NDVI, SAVI, EVI, and GDVI vegetation indices, SI-1, SI-2, SI-3, and IB salinity indices, albedo, temperature (TSUP), and real evapotranspiration (ETR). Descriptive statistics and principal component analysis (PCA) were applied to identify majors EC and biophysical parameters relationships, and select the best predictors to generate equations capable of estimating soil salinity across the irrigated perimeter. The EC showed a high standard deviation and coefficient of variation compared to the other variables, with higher concentrations observed in areas of bare soil, followed by natural vegetation and agricultural vegetation. TSUP and GDVI were the predictors most strongly related to EC, for images Landsat-8 and Sentinel-2 respectively, with R2 of 73% and 71%. The application of the generated models presented satisfactory validation for two distinct areas. The maps obtained were similar in terms of the distribution of soil saline levels, confirming what was observed in the field scale, with a greater level of detail for the map from Sentinel-2.

Analysis of the vegetation condition on the area of the closed Babina mine in 1989-2019 using multispectral satellite images

Technical and biological treatments, aiming at granting or restoring use/natural values of lands degraded by mining operations, are carried out already at the stage of mining works and continue after the end of exploitation. As a result of these activities, area of plant cover increases and condition of vegetation in the former mineral extraction is improved. However, monitoring of post-mining areas in various regions of the world indicates that in the sites of former exploitation local degradation of vegetation may occur, despite the completion of the reclamation process. Adverse changes in flora can be caused for example by water penetrating deep into the ground through the system of underground pavements or their collapsing. The purpose of this publication is to present the changes in the condition of plant cover within the Pustkôw mining field, one of the four fields of the closed lignite “Friendship of Nations - Babina Shaft” mine. The analysis of variation in the state of the plant cover health was carried out for the period of 1989-2019 based on NDVI, EVI and GNDVI spectral indices, developed using multispectral images of the Landsat TM/ETM+/OLI missions. The obtained results provide information about systematic improvement of the flora condition in the analysed region and indicate an increase in green areas. In 1989-2019, overgrowing of the shorelines of anthropogenic lakes and heaps was observed, as well as reservoir with a decreasing surface area due to vegetation succession was identified. In 2016, local degradation of the plant cover was also observed in the north-western part of the Pustków field. The obtained results prove the necessity of continuous monitoring of the flora health in the area of the analysed mine, but also in other post-mining areas.

Fractional vegetation cover estimation in southern African rangelands using spectral mixture analysis and Google Earth Engine

Grasslands are under continuous threat of conversion and subsequent degradation, which has a detrimental impact on grassland productivity and grazing capacity, affecting the livestock industry. Fractional vegetation cover as indicator of grassland condition and productivity has been extensively researched, however, existing approaches and products are limited with respect to accessibility, affordability, applicability, and transferability. This study evaluated the use of publicly available satellite imagery, spectral mixture analysis and cloud geoprocessing technologies for dynamic, continuous, and accurate estimation of FVC for sustainable management. A linear spectral mixture model was developed, calibrated, and implemented in Google Earth Engine using Sentinel-2 and Landsat 8 imagery. Model accuracy and spatial and temporal transferability were evaluated using existing benchmark products and field data. It was found that Sentinel-2 performed the best using a feature combination of the SWIR2 band and the NDVI, EVI, MSAVI2 and DBSI indices. Accuracies were further improved by dividing the woody and bare endmembers into subclasses. The approach proved both spatially and temporally transferable, thus this research provides a robust approach to FVC estimation using limited field data and open source remote sensing imagery. The combination of this research with further grassland productivity modelling could prove valuable for sustainable environmental and economical rangeland planning and management.

Transformation of the vegetation of the Tsasuchey steppe pine forest

The Tsasuchey pine forest is located at the South-Eastern Trans-Baikal near the Mongolian border. This territory is included in the State Nature Reserve of Federal Importance “Tsasuchey pine forest”. An increase in air temperatures and a low atmospheric precipitation in recent decades resulted in an increase in fire hazard and catastrophic fires, which nearly destroyed the pine forest. With the help of satellite images of the Terra/Modis spectroradiometer, we determined the burnt areas and vegetation indices NDVI and EVI for the territory of Tsasuchey pine forest. The research results showed that the fires are the key factor of vegetation transformation at the forest territory, and its recovering is closely connected with humidifying. During the last drought period the pine forest area was reduced from 43.5 to 2.5 thousand ha as the result of the catastrophic fires in 2000, 2003, and 2012. Correlation coefficients between investigated vegetation indexes on the territory of Tsasuchey pine forest and meteorological factors show the proved connection between precipitation and hydrothermal index (humidity factor), herewith, the EVI shows higher correlation coefficients with meteorological parameters than NDVI, which indicates its high informative value. The most direct relationship was revealed between the average and maximum monthly values of the EVI index and the total precipitation. In the dynamics of the average annual minimum values of EVI, there is, on average, a positive trend, which indicates an increase in the amount of vegetation on the territory of Tsasuchey pine forest during the warm period.

Propagation dynamics of abruptly autofocusing circular Airy Gaussian vortex beams in the fractional Schrödinger equation

We have investigated the propagation dynamics of the circular Airy Gaussian vortex beams (CAGVBs) in a (2+1)-dimesional optical system discribed by fractional nonlinear Schr\"odinger equation (FNSE). By combining fractional diffraction with nonlinear effects, the abruptly autofocusing effect becomes weaker, the radius of the focusing beams becomes bigger and the autofocusing length will be shorter with increase of fractional diffraction L\'evy index. It has been found that the abruptly autofocusing effect becomes weaker and the abruptly autofocusing length becomes longer if distribution factor of CAGVBs increases for fixing the L\'evy index. The roles of the input power and the topological charge in determining the autofocusing properties are also discussed. Then, we have found the CAGVBs with outward acceleration and shown the autodefocusing properties. Finally, the off-axis CAGVBs with positive vortex pairs in the FNSE optical system have shown interesting features during propagation.

Uji Akurasi Beberapa Indeks Vegetasi dalam Mengestimasi Kerapatan Hutan Mangrove dengan Citra Sentinel-2A di Taman Nasional Bali Barat

Vegetation Indices is one of the remote sensing parameters that can be used to estimate the mangrove forest density. The purpose of this study is to determine the vegetation index with the best accuracy to estimate the condition of mangrove density, as well as determine the spatial distribution of mangrove density in the TNBB area. This study uses Sentinel-2A satellite imagery data and five different vegetation indices, namely NDVI, NNIR, EVI, mRE-SR, and vegetation index developed in this study. The method of determining samples in the field uses stratified random and proportional sampling. Data collection of canopy density used hemispherical photography method, which is taking vertical photos with a 180o angle of view using a camera with a Fish Eye or Wide lens. Data analysis used in this study is regression analysis, coefficient of determination test, model validation test, and paired t test. From statistical tests conducted on several vegetation indices, the mRE-SR vegetation index value shows the best results on all the accuracy parameters tested. The R2 value was generated by the mRE-SR vegetation index from the relationship between mangrove density results from field measurements with the vegetation index value and the estimated density results shows that the highest values, namely 0.909 and 0.935. These results show that the mRE-SR vegetation index is the best vegetation index in explaining the variation of mangrove density in the field. The mRE-SR vegetation index also has the lowest deviation of the estimated value, with the resulting SE values in the two linear relationships of 1,592 and 0,999. In addition, the mRE-SR vegetation index has a P (T <= t) two-tail value greater than the significance level (0.05), the results means that two values of the tested variables are not significant different. The calculation results show that the total area of mangroves in TNBB is 409.21 ha. From the percentage of density obtained, the mangrove density class was only distributed in the medium and solid density classes.

Modification of the Land Surface Temperature – Vegetation Index Triangle Method for soil moisture condition estimation by using SYNOP reports

Estimation of soil moisture condition on a local or regional scale is one of the most important problems in vegetation conditions monitoring. One of the most suitable approach to do this over vegetated areas is the Land Surface Temperature – Vegetation Index (LST-VI) Triangle Method. This method estimates water content in soil which is available for plants in contrast to methods using satellite observations in the microwave band, and it is superior to pixel by pixel soil moisture methods using observations in the optical band which neglect statistical relationship between VI and LST controlling evapotranspiration on vegetated area. This study tested a methodological modification of the method based on assumption that evapotranspiration depends on the difference between the air temperature and the terrain surface temperature. The spatial distribution of Temperature-Vegetation Dryness Indices, which reflect soil moisture in the root zone was calculated for vast areas of Central Europe using the classic Triangle Method and its modified version. Both NDVI and EVI indices were used as input data in these calculations. The air temperature at 2 m height was taken from SYNOP reports, and the terrain surface temperature from MODIS data. In addition, Matlab scripts were written by the authors to allow convenient access to free SYNOP data. Our results indicate that proposed modification increases the accuracy of soil moisture estimation. This was confirmed by comparing classic and modified Temperature-Vegetation Dryness Indices values with rainfall data.

Hyperspectral imaging of symptoms induced by Rhizoctonia solani in sugar beet: comparison of input data and different machine learning algorithms

The fungal pathogen Rhizoctonia solani is one of the most important soil-borne diseases in sugar beet production worldwide. Root and crown rot caused by this fungus are traditionally recognized later in the cropping season by rating the above-ground symptoms like wilting and chlorosis on foliage, and dark brown lesions at the base of petioles. The present study was designed to evaluate noninvasive sensors and machine learning for measuring disease incidence and early detection. Eight-weeks-old plants were inoculated with the pathogen in two different concentrations and under controlled conditions. Hyperspectral images in the visible and near-infrared range from leaf were obtained in time-series. One hundred thirty and fifteen spectral features were selected in two levels by using the recursive feature elimination method (RFE) and a clustering approach. Subsequently, five types of machine-learning methods were employed to train four types of spectral data containing reflectance values, vegetation indices, selected variables of the RFE process and selected variables of an RFE-clustering process. The best classifier was obtained from a partial least squares modeling process and required a number of 15 spectral features, which include first and second derivatives of the wavelength spectrum as well as the Ctr3, EVI and PSSRa vegetation index. This investigation proves that under controlled conditions early detection of indirect symptoms caused by Rhizoctonia root rot in sugar-beet plants is possible. Scoring of disease incidence of Rhizoctonia root rot at 10 dai was 3 to 5 times higher with a machine-learning classifier in comparison with the human visual rating.

The tipping times in an Arctic sea ice system under influence of extreme events.

In light of the rapid recent retreat of Arctic sea ice, the extreme weather events triggering the variability in Arctic ice cover has drawn increasing attention. A non-Gaussian α-stable Lévy process is thought to be an appropriate model to describe such extreme events. The maximal likely trajectory, based on the nonlocal Fokker-Planck equation, is applied to a nonautonomous Arctic sea ice system under α-stable Lévy noise. Two types of tipping times, the early-warning tipping time and the disaster-happening tipping time, are used to predict the critical time for the maximal likely transition from a perennially ice-covered state to a seasonally ice-free one and from a seasonally ice-free state to a perennially ice-free one, respectively. We find that the increased intensity of extreme events results in shorter warning time for sea ice melting and that an enhanced greenhouse effect will intensify this influence, making the arrival of warning time significantly earlier. Meanwhile, for the enhanced greenhouse effect, we discover that increased intensity and frequency of extreme events will advance the disaster-happening tipping time, in which an ice-free state is maintained throughout the year in the Arctic Ocean. Finally, we identify values of the Lévy index α and the noise intensity ϵ in the αϵ-space that can trigger a transition between the Arctic sea ice state. These results provide an effective theoretical framework for studying Arctic sea ice variations under the influence of extreme events.

The Shape of the Correlation Function

The correlation function measured in ultrarelativistic nuclear collisions is non-Gaussian. By making use of models we discuss and assess how much various effects can influence its shape. In particular, we focus on the parametrisations expressed with the help of L\'evy-stable distributions. We show that the L\'evy index may deviate substantially from 2 due to non-critical effects such as non-spherical shape, resonance decays, event-by-event fluctuations and functional dependence on $Q_{inv}$ or similar.

Detection of Diseases in Oil Palm Plantations in the Brazilian Amazon through Orbital Image

The detection of diseases in oil palm crops in the Brazilian Amazon represents a great challenge for the management of this crop in Brazil. The plantations in the State of Pará provide inputs for the food, cosmetics, agro-energy and biofuel industries, supplying Brazilian markets. In recent years, several factors such as pests, diseases and climate have interfered in the development of oil palm in the region, generating the need to adopt new techniques to detect and monitor such issues. In this work, spectral enhancements were carried out by simple reflectance and vegetation indices for four plots cropped on Companhia Palmares da Amazônia (CPA) farm, owned by Agropalma S.A. company in the municipality of Acará, in the state of Pará. The results allowed the identification of expressive patterns minimum and maximum reflectances of the studied plots, correlating with occurrences of diseases. The EVI index showed an excellent correlation with the occurrence of diseases. However, the NDVI and SAVI indexes showed adequate adjustments with the occurrence of diseases in 2017. The areas corresponding to the L36 and H27 plots showed higher occurrences of diseases, based on the analysis of reflectance through vegetation indices. It is concluded that the reflectance enhancements, NDVI, SAVI and EVI obtained by orbital sensors are efficient in the detection of diseases in the plots. The results allowed the identification of diagnostic anomalies of stresses in the plots, either by disease or other factor, allowing the decision making in an adequate time, therefore avoiding large scale eradication in the extensive areas in commercial palm oil plantations in Brazil.

Long-term changes of surface albedo and vegetation indices in north of Iran

Surface albedo is a key parameter in earth energy budget and global climate change studies. In this aspect, variation in vegetation covers is one of the most critical issues affecting global energy balance. The present study is conducted to examine the relationship between changes in vegetation cover (by using NDVI and EVI indices) and changes in surface albedo. Using qualified MODIS data, mean annual albedo variations (during the growing season) and their relation with the changes in NDVI and EVI values in 6 climate zones are investigated for the period of 2004 to 2016. The results show that the lowest variations of albedo are observed in the region with SH-C-VW climate and the highest in the SA-C-W climate. With the exception of A-C-W and SA-C-W climates, the absolute value of the Pearson correlation coefficient between albedo and EVI is stronger than NDVI. Hot spots of temperature and precipitation are detected by employing daily measurements of 17 weather stations to find the relationship with the changes in surface albedo. According to the results, surface albedo has increased during the study period in majority of the weather sites. Generally, increase in surface albedo was evident for 61% of the study area, while decreases were detected in 30% and no significant changes in the remaining areas. Also, except for SH-C-VW climate, albedo has experienced an increasing trend in all climates from April to September. The major albedo declines are found in the Hyrcanian forests, where higher vegetation cover is confirmed by Iran Forests, Range and Watershed Management National Organization. Examining the hot spots in temperature and precipitation reveals that the reduction of albedo (due to the increase in vegetation cover) in the areas coincides with more rainfall in the region. In contrast, higher temperatures were observed for areas with the decreased vegetation cover.

MONITORING VEGETATION COVER CHANGE USING VEGETATION INDICES IN TANGBO RIVER, BARANGAY TANGBO, SAMBOAN

Abstract. Tangbo River is an important resource in Cebu’s southern town of Samboan for being the site of Aguinid Falls, a known tourist destination. Monitoring the changes in the river’s riparian vegetation is important since it has impacts on its ecological role of helping maintain biodiversity and river water quality. This study aims to detect vegetation index changes along the Tangbo River corridor using three vegetation indices: NDVI, EVI, NDMI, and Tasseled Cap indices, specifically for the years 1998, 2004, 2009, 2016, and 2019. It also aims to monitor the changes in NDVI and EVI values alongside tourism arrivals in Aguinid in 2018.Cloudless Landsat 5 (1998, 2004, 2009, and 2016) and Landsat 8 (2019) imagery were selected. Thirty reference points were plotted along the river with a 30-m distance between each point. Vegetation Indices (VI) and Tasseled Cap values were generated using data from these points and were compared for each selected year. NDVI and EVI values from the same reference points used in Landsat were generated from selected cloudless months of 2018 Planetscope imagery. Inbound tourist records were acquired from the tourism office of Samboan and the tourism arrivals for the year 2018 was then graphed with the Planetscope VI values for better visualization.Landsat imagery showed that there was a general upward trend in the vegetation indices from 1998 to 2019. Tasseled Cap Greenness and Wetness showed an increase in values from 1998–2019 while Tasseled Cap Brightness showed the opposite. Results from Planetscope data for the year 2018 showed that there was an inverse pattern between NDVI and tourism arrivals. Tourism arrivals peaked during the months of April and May based on annual records, while VI values dropped. On the other hand, both VI values peaked towards the last quarter of the year while tourist numbers dropped. This suggests that the pattern of VI values and tourism arrivals seemed to be influenced by seasonal changes rather than with each other. Findings from the study shows that further data collection is required to be able to establish a relationship between tourism and vegetation index values.

Space-time analysis of vegetation trends and drought occurrence in domain area of tropical forest.

The purpose of this study is to evaluate temporal trends in changes in vegetation patterns within the Sooretama Biological Reserve and its surroundings, located in Espirito Santo State, Brazil. The evaluation will be performed using the EVI and NDVI index of the MODIS sensor, the Mann-Kendall monotonic trend, Seasonal Trend Analysis methods, and monitoring drought events through the VCI drought index for the years 2007 through 2015. The tools utilized were the EVI and NDVI indexes of the MOD13Q1 product and LST from the MOD11A2 product. These indices were used in order to represent the dynamics of the study area biomass and then to analyze the drought occurrence using the index best-suited to the area of study, identified as VCI. The temporal trends in the data set were examined, pixel by pixel, by application of the Mann-Kendall monotonic technique, treating each pixel in space as a one-dimensional temporal series of 16-day cycles. To evaluate the seasonal trend, the analysis used the STA technique (Seasonal Trend Analysis) implemented in the ETM module. The characterization and spatial distribution of drought events were performed through the Vegetation Condition Index (VCI). The use of (a) images and seasonal curves produced by the monotonic trend of Mann-Kendall and (b) analysis of seasonal trends generated the response of the vegetation to climate variations. The VCI indicated a potential for drought occurrence analysis in regions and areas with different vegetation densities. So, the VCI can be used as a powerful tool to compose a comprehensive and early system alert of drought that can accompany the changes in spatial coverage of vegetation and severity of change. Lastly, the analysis of the data from the MODIS NDVI, EVI, and TST images indicated that the data is suitable to a space-time analysis of drought occurrences and vegetation trends.

ESTIMASI PRODUKSI JAGUNG (Zea Mays L.) DENGAN MENGGUNAKAN CITRA SENTINEL 2A DI SEBAGIAN WILAYAH KABUPATEN JENEPONTO PROVINSI SULAWESI SELATAN

Production is a real benchmark in successful crop management which is the most important output economically. Currently, corn production estimates are generally done by conventional means through field surveys. This conventional way requires a high cost and a long time. Appropriate agricultural management requires precise and accurate information or data to increase production and economic benefits. Sentinel 2A remote sensing satellite data is potential to be used in assessment of corn production estimation. The purpose of this research is to make land use mapping and corn production estimation by using spectral approach. Estimated data were obtained from Sentinel 2A image by mapping land use and modeling of vegetation index (NDVI, SAVI, MSAVI, TSAVI, EVI, and ARVI) then compared with data of corn production in the field. The result of data analysis shows land use mapping using Sentinel 2A image has 91% confidence level. Calculation of production estimation can show the accuracy of 74% with RMSE 0.69. The highest correlation is estimated production with EVI index model with regression correlation equal to 74% which shows strong correlation on both variables. Estimated production of corn in 2017 in Jeneponto Regency is 178,660,69 tons.

Real-time Prediction of Crop Yields from MODIS Relative Vegetation Health: A Continent-wide Analysis of Africa

Developing countries often have poor monitoring and reporting of weather and crop health, leading to slow responses to droughts and food shortages. Here, I develop satellite analysis methods and software tools to predict crop yields two to four months before the harvest. This method measures relative vegetation health based on pixel-level monthly anomalies of NDVI, EVI and NDWI indices. Because no crop mask, tuning, or subnational ground truth data are required, this method can be applied to any location, crop, or climate, making it ideal for African countries with small fields and poor ground observations. Testing began in Illinois where there is reliable county-level crop data. Correlations were computed between corn, soybean, and sorghum yields and monthly vegetation health anomalies for every county and year. A multivariate regression using every index and month (up to 1600 values) produced a correlation of 0.86 with corn, 0.74 for soybeans, and 0.65 for sorghum, all with p-values less than 10 − 6 . The high correlations in Illinois show that this model has good forecasting skill for crop yields. Next, the method was applied to every country in Africa for each country’s main crops. Crop production was then predicted for the 2018 harvest and compared to actual production values. Twenty percent of the predictions had less than 2% error, and 40% had less than 5% error. This method is unique because of its simplicity and versatility: it shows that a single user on a laptop computer can produce reasonable real-time estimates of crop yields across an entire continent.

Real-Time Prediction of Crop Yields From MODIS Relative Vegetation Health: A Continent-Wide Analysis of Africa

Developing countries often have poor monitoring and reporting of weather and crop health, leading to slow responses to droughts and food shortages. Here, I develop satellite analysis methods and software tools to predict crop yields two to four months before the harvest. This method measures relative vegetation health based on pixel-level monthly anomalies of NDVI, EVI and NDWI indices. Because no crop mask, tuning, or subnational ground truth data are required, this method can be applied to any location, crop, or climate, making it ideal for African countries with small fields and poor ground observations. Testing began in Illinois where there is reliable county-level crop data. Correlations were computed between corn, soybean, and sorghum yields and monthly vegetation health anomalies for every county and year. A multivariate regression using every index and month (up to 1600 values) produced a correlation of 0.86 with corn, 0.74 for soybeans, and 0.65 for sorghum, all with p-values less than 10 − 6 . The high correlations in Illinois show that this model has good forecasting skill for crop yields. Next, the method was applied to every country in Africa for each country’s main crops. Crop production was then predicted for the 2018 harvest and compared to actual production values. Twenty percent of the predictions had less than 2% error, and 40% had less than 5% error. This method is unique because of its simplicity and versatility: it shows that a single user on a laptop computer can produce reasonable real-time estimates of crop yields across an entire continent.

Correlations between spectral and biophysical data obtained in canola canopy cultivated in the subtropical region of Brazil

Abstract: The objective of this work was to identify the spectral bands, vegetation indices, and periods of the canola crop season in which the correlation between spectral data and biophysical indicators (total shoot dry matter and grain yield) is most significant. The experiment was carried out during the 2013 and 2014 crop seasons at Embrapa Trigo, in the state of Rio Grande do Sul, Brazil. A randomized complete block design was used, with four replicates, and the treatments consisted of five doses of nitrogen topdressing. Plant dry matter, grain yield, and phenology were measured. The canola spectral response was evaluated by measuring the canola canopy reflectance using a spectroradiometer, and, with this data, the SR, NDVI, EVI, SAVI, and GNDVI vegetation indices were determined. Pearson’s correlations between the spectral and biophysical variables of canola showed that the red (620 to 670 nm) and near-infrared (841 to 876 nm) bands were the best to estimate the dry matter. The vegetative period is the most indicated to obtain the most significant correlations for canola. All the used vegetation indices are adequate for estimating the dry matter and grain yield of canola.

Species Richness and Abundance of Cerambycidae (Coleoptera) in Huatulco, Oaxaca, Mexico; Relationships with Phenological Changes in the Tropical Dry Forest.

Cerambycidae have an important ecological role in initiating the degradation process of dead wood, but few studies have evaluated Cerambycidae community attributes in relation to ecosystem phenology. We surveyed the cerambicid fauna of the tropical dry forest in Huatulco, Oaxaca, Mexico, and explored the relationship of Cerambycidae species richness and abundance with phenological changes in vegetation. We applied three collecting methods of light traps, direct collection, and Malaise traps to survey Cerambycidae throughout 2005. To determine seasonal variations, we collected samples in the dry season month of February in the rainy season of May-July and August-September, and in the transition months of October and November through. We collected and identified 145 species, 88 genera, 37 tribes, and four subfamilies. The subfamily with the highest number of species was Cerambycinae (100 species), and the tribe with the highest number of genera and species was Elaphidiini with 13 genera and 33 species. The ICE non-parametric estimator determined an overall expected richness of 373 species, while the overall Shannon Diversity Index was 4.1. Both species richness and abundance varied seasonally, with the highest values recorded in the rainy season and the lowest in the dry season. Overall species abundance was not significantly correlated to monthly rainfall or EVI neither, only for "direct collecting" the EVI vs Richness and EVI vs Shannon Diversity Index were significantly correlated. We propose that the seemingly contradictory relationships between seasonal richness patterns of Cerambycidae and the greening/senescence of vegetation (EVI) may be explained by the seasonal availability of dead organic matter, flowers, or leafy vegetation that may be synchronized with the behavior of different cerambycid species.

Applying the Global Disturbance Index for Detecting Vegetation Changes in Lao Tropical Forests

Land cover change is a major challenge for many developing countries. Spatiotemporal information on this change is essential for monitoring global terrestrial ecosystem carbon, climate and biosphere exchange, and land use management. A combination of LST and the EVI indices in the global disturbance index (DI) has been proven to be useful for detecting and monitoring of changes in land covers at continental scales. However, this model has not been adequately applied or assessed in tropical regions. We aimed to demonstrate and evaluate the DI algorithm used to detect spatial change in land covers in Lao tropical forests. We used the land surface temperature and enhanced vegetation index of the Moderate Resolution Imaging Spectroradiometer time-series products from 2006-2012. We used two dates Google EarthTM images in 2006 and 2012 as ground truth data for accuracy assessment of the model. This research demonstrated that the DI was capable of detecting vegetation changes during seven-year periods with high overall accuracy; however, it showed low accuracy in detecting vegetation decrease.