Detection and mapping vegetation cover based on the Spectral Angle Mapper algorithm using NOAA AVHRR data

Satellite data, taken from the National Oceanic and Atmospheric Administration (NOAA) have been proposed and used for the detection and the cartography of vegetation cover in North Africa. The data used were acquired at the Analysis and Application of Radiation Laboratory (LAAR) from the Advanced Very High Resolution Radiometer (AVHRR) sensor of 1 km spatial resolution. The Spectral Angle Mapper Algorithm (SAM) is used for the classification of many studies using high resolution satellite data. In the present paper, we propose to apply the SAM algorithm to the moderate resolution of the NOAA AVHRR sensor data for classifying the vegetation cover. This study allows also exploiting other classification methods for the low resolution. First, the normalized difference vegetation index (NDVI) is extracted from two channels 1 and 2 of the AVHRR sensor. In order to obtain an initial density representation of vegetal formation distribution, a methodology, based on the combination between the threshold method and the decision tree, is used. This combination is carried out due to the lack of accurate data related to the thresholds that delimit each class. In a second time, and based on spectral behavior, a vegetation cover map is developed using SAM algorithm. Finally, with the use of low resolution satellite images (NOAA AVHRR) and with only two channels, it is possible to identify the most dominant species in North Africa such as: forests of the Liege oaks, other forests, cereal’s cultivation, steppes and bar soil.     

改进暗通道遥感影像去雾方法及效果分析

遥感影像的预处理工作是遥感数据应用的基础。去除云雾是影像预处理工作的重要组成部分。针对遥感影像雾霾浓度分布不均匀的问题,提出一种改进的暗通道遥感影像去雾方法。以"高分一号"(GF-1)卫星为例,根据影像灰度图中的灰度值对影像雾霾浓度区域进行划分,对每个区域中暗原色值的获取方式进行改进,使用导向滤波优化大气传输率,以归一化植被指数(NDVI)为基础,设计用于评价影像去雾质量的定量指标。结果表明:所提出的方法能明显去除雾霾干扰,有效改善卫星影像数据的视觉效果,增强影像细节。该方法去雾处理后的遥感数据能应用于定量遥感,提高遥感影像的可用性及有效性。     

GRACE products and land surface models for estimating the changes in key water storage components in the Nile River Basin

The Nile River Basin (NRB) is facing extreme demand for its water resources due to an alarming increase in population and the changing climate. The NRB is not compatible with ground-based in-situ observations owing to its large basin area size and limited hydrological data access from basin countries. Thus, it lends itself to remotely sensed approaches with high spatial resolution and extended temporal coverage. The Gravity Recovery and Climate Experiment (GRACE) avails a unique opportunity to investigate the changes in key components of terrestrial water storage (TWS). GRACE TWS solutions have specific tuning parameters and processing strategies that result in regionally specific variations and error patterns. We explored the TWS time series spatiotemporal changes, trends, uncertainties, and signal-to-noise ratio among different GRACE TWS data. We had also investigated the key terrestrial water storage components such as surface water, soil moisture, and groundwater storage changes. The results show that GRACE spherical harmonic solutions' uncertainty is higher than the mass concentration (mascon) over the NRB, and the Center for Space Research-mascons had the best performance. The evapotranspiration correlation (R² = 0.85) has the highest correlation with GRACE’s TWS, whereas the normalized difference vegetation index (R² = 0.82) has the second highest correlation. Notably, significant long-term (2003–2017) negative groundwater and soil moisture trends demonstrate a potential depletion of the NRB. Despite an increase in precipitation and the TWS time series, the rate of decline increased rapidly after 2008, thereby indicating the possibility of human-induced change (e.g. for irrigation purposes). Therefore, the results of this study provide a guide for future studies related to hydro-climatic change over the NRB and similar basins.     

Preparing for space: Increasing technical readiness of low-cost high-performance remote sensing using high-altitude ballooning

Two widely available, small size, weight and power camera systems were flown above 97 % of Earth’s atmosphere and showed utility in single filter vegetation and soil analysis in a space analogue environment. The experiment was conducted as a low-cost verification and test analogue to flying on vastly more expensive low Earth orbit missions. Normalised Difference Vegetation Index (NDVI) was used as the metric by which performance was analysed for ground calibration testing, low and near-space altitude remote sensing. Ground calibration testing with a laboratory-grade spectrometer revealed that both cameras were able to return consistent NDVI results, and high-altitude balloon flight allowed similar data capture from an environment similar to space. Although compressed captured imagery had been processed using gamma correction and pre-image processing, these were able to be corrected provided that access to radiometrically-calibrated data was available. The two hobbyist cameras were shown to return scientifically useful results, demonstrating performance, and additionally their utility for citizen science applications in the near-space environment.     

Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data

Land surface temperature (LST) is an important factor in global change studies, heat balance and as control for climate change. A comparative study of LST over parts of the Singhbhum Shear Zone in India was undertaken using various emissivity and temperature retrieval algorithms applied on visible and near infrared (VNIR), and thermal infrared (TIR) bands of high resolution Landsat-7 ETM+ imagery. LST results obtained from satellite data of October 26, 2001 and November 2, 2001 through various algorithms were validated with ground measurements collected during satellite overpass. In addition, LST products of MODIS and ASTER were compared with Landsat-7 ETM+ and ground truth data to explore the possibility of using multi-sensor approach in LST monitoring. An image-based dark object subtraction (DOS3) algorithm, which is yet to be tested for LST retrieval, was applied on VNIR bands to obtain atmospheric corrected surface reflectance images. Normalized difference vegetation index (NDVI) was estimated from VNIR reflectance image. Various surface emissivity retrieval algorithms based on NDVI and vegetation proportion were applied to ascertain emissivities of the various land cover categories in the study area in the spectral range of 10.4–12.5 μm. A minimum emissivity value of about 0.95 was observed over the reflective rock body with a maximum of about 0.99 over dense forest. A strong correlation was established between Landsat ETM+ reflectance band 3 and emissivity. Single channel based algorithms were adopted for surface radiance and brightness temperature. Finally, emissivity correction was applied on ‘brightness temperature’ to obtain LST. Estimated LST values obtained from various algorithms were compared with field ground measurements for different land cover categories. LST values obtained after using Valor’s emissivity and single channel equations were best correlated with ground truth temperature. Minimum LST is observed over dense forest as about 26 °C and maximum LST is observed over rock body of about 38 °C. The estimated LST showed that rock bodies, bare soils and built-up areas exhibit higher surface temperatures, while water bodies, agricultural croplands and dense vegetations have lower surface temperatures during the daytime. The accuracy of the estimated LST was within ±2 °C. LST comparison of ASTER and MODIS with Landsat has a maximum difference of 2 °C. Strong correlation was found between LST and spectral radiance of band 6 of Landsat-7 ETM+. Result corroborates the fact that surface temperatures over land use/land cover types are greatly influenced by the amount of vegetation present.     

Integrated drought monitoring index: A tool to monitor agricultural drought by using time-series datasets of space-based earth observation satellites

In this study, integrated drought monitoring index (IDMI) was proposed as a tool to assess and monitor the spatio-temporal dynamics of agricultural drought during the northeast monsoon season for the period from 2000 to 2016 in Tamil Nadu state, south-eastern part of Indian peninsula. The IDMI is characterized as the principal component of precipitation condition index (PCI), soil moisture condition index (SMCI), temperature condition index (TCI), and vegetation condition index (VCI) derived from time-series satellite observations of climate hazards group infra-red precipitation with stations (CHIRPS), European space agency climate change initiative (ESA-CCI) and moderate resolution imaging spectroradiometer (MODIS). The study shows that in the year 2016, about 44.4 and 17.8% of Tamil Nadu state was under extreme and severe drought conditions, respectively. Sensitivity analysis of the study shows that PCI is the most influential parameter to IDMI, followed by VCI and TCI. The validation of IDMI with 3-month standardized precipitation index (SPI) by using Pearson correlation test shows a strong positive correlation between IDMI and 3-month SPI with correlation coefficient (r) value of 0.73 and 0.77 for the wet (2005) and dry year (2016), respectively. The study clearly demonstrates the potential of IDMI derived from time-series datasets of earth observation satellites as a tool in assessment and monitoring of spatio-temporal dynamics of agricultural drought. The proposed IDMI could be effectively used as a reliable tool to monitor agricultural drought and develop its mitigation strategies to minimise the adverse effects of drought on agriculture, water resources, and livelihoods of the people.     

A study of forest vegetation dynamics in the south of the Krasnoyarskii Krai in spring

Remote sensing applications have greatly enhanced ability to monitor and manage in the areas of forestry. Accurate measurements of regional and global scale vegetation dynamics (phenology) are required to improve models and understanding of inter-annual variability in terrestrial ecosystem carbon exchange and climate–biosphere interactions. Study of vegetation phenology is required for understanding of variability in ecosystem. In this paper, monitoring of vegetation dynamics using time series of satellite data is presented. Vegetation variability (vegetation rate) in different topoclimatic areas is investigated. Original software using IDL interactive language for processing of satellite long-term data series was developed. To investigate growth dynamics vegetation rate inferred from remote sensing was used. All estimations based on annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Vegetation rate for Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) was calculated using MODIS data. The time series covers spring seasons of each of 9 years, from 2000 to 2008. Comparison of EVI and NDVI derived growth rates has shown that NDVI derived rates reveal spatial structure better. Using long-term data of vegetation rates variance was estimated that helps to reveal areas with anomalous growth rate. Such estimation shows sensitivity degree of different areas to different topoclimatic conditions. Woods of heights depend on spatial topoclimatic variability unlike woods of lowlands. Principal components analysis shows vegetation with different rate conditions. Also it reveals vegetation of same type in areas with different conditions. It was demonstrated that using of methods for estimating the dynamic state of vegetation based on remote sensing data enables successful monitoring of vegetation phenology.     

Monitoring of rice cropping intensity in the upper Mekong Delta,Vietnam using time-series MODIS data

Information on rice growing areas is important for policymakers to devise agricultural plans. This research explores the monitoring of rice cropping intensity in the upper Mekong Delta, Vietnam (from 2001 to 2007) using time-series MODIS NDVI 250-m data. Data processing includes three steps: (1) noise is filtered from the time-series NDVI data using empirical mode decomposition (EMD); (2) endmembers are extracted from the filtered time-series data and trained in a linear mixture model (LMM) for classification of rice cropping systems; and (3) classification results are verified by comparing them with the ground-truth and statistical data. The results indicate that EMD is a good filter for noise removal from the time-series data. The classification results confirm the validity of LMM, giving an overall accuracy of 90.1% and a Kappa coefficient of 0.7. The lowest producer and user accuracies were associated with single crop rain-fed rice class due to the mixed pixel problems. A strong yearly correlation at the district level was revealed in the MODIS-derived areas (R² ? 0.9). Investigation of interannual changes in rice cropping intensity from 2001 to 2007 showed a remarkable conversion from double to triple crop irrigated rice from 2001 to 2003, especially in the Thoai Son and Phu Tan districts. A big conversion from triple crop rice back to double crop rice cultivation was also observed in Phu Tan from 2005 to 2006. These changes were verified by visual interpretation of Landsat images and examination of NDVI profiles.     

A simplified data assimilation method for reconstructing time-series MODIS NDVI data

The Normalized Difference Vegetation Index (NDVI) is an important vegetation index, widely applied in research on global environmental and climatic change. However, noise induced by cloud contamination and atmospheric variability impedes the analysis and application of NDVI data. In this work, a simplified data assimilation method is proposed to reconstruct high-quality time-series MODIS NDVI data. We extracted 16-Day L3 Global 1 km SIN Grid NDVI data sets for western China from MODIS vegetation index (VI) products (MOD13A2) for the period 2003–2006. NDVI data in the first three years (2003–2005) were used to generate the background field of NDVI based on a simple three-point smoothing technique, which captures annual features of vegetation change. NDVI data for 2006 were used to test our method. For every time step, the quality assurance (QA) flags of the MODIS VI products were adopted to empirically determine the weight between the background field and NDVI observations. Ultimately, more reliable NDVI data can be produced. The results indicate that the newly developed method is robust and effective in reconstructing high-quality MODIS NDVI time-series.     

Comparison of ground reflectance measurement with satellite derived atmospherically corrected reflectance: A case study over semi-arid landscape

Atmospheric corrections to satellite data are important for comparing multitemporal data sets over tropical regions with variable aerosol loading. In this study, we evaluated the potential of 6S radiative transfer model for atmospheric corrections of IRS-P6 AWiFS satellite data sets, in a semi-arid landscape. Ground measurements of surface reflectance representing different land use/land cover categories were conducted to relate IRS-P6 AWiFS top of atmospheric reflectance. The 6S radiative transfer model was calibrated for local conditions using ground measurements for aerosol optical depth, water vapor and ozone with a sun photometer. Surface reflectance retrieved from 6S code was compared with top of atmosphere (TOA) reflectance and ground based spectroradiometer measurements. Accurate parameterization of the 6S model using measurements of aerosol optical depth, water vapor and ozone plays an important role while comparing ground and satellite derived reflectance measurements.