Moderate Imaging Research Articles

POTENTIAL AREA ESTIMATION OF FISHING GROUND BASED ON THERMAL FRONT AND UPWELLING IN WEST SUMATERA WATERS IN EAST SEASON

This research was conducted in August to November 2019. It aims to determine the potential fishing ground based on thermal front and upwelling in the waters of West Sumatra. The survey method was applied in this research. The results showed that the distribution of thermal front and upwelling in the waters of West Sumatra experienced a change that was not too significant. Based on the results of sea surface temperature verification on the Aqua MODIS image with the temperature in the field obtained temperature differences of around ± 10C. The catch during the research shows that catches in the thermal front area can be used as a potential fishing ground because the catch in the thermal front area is more than the area outside the thermal front

The short life of the volcanic island New Late\u2019iki (Tonga) analyzed by multi-sensor remote sensing data

Satellite-based Earth observation plays a key role for monitoring volcanoes, especially those which are located in remote areas and which very often are not observed by a terrestrial monitoring network. In our study we jointly analyzed data from thermal (Moderate Resolution Imaging Spectrometer MODIS and Visible Infrared Imaging Radiometer Suite VIIRS), optical (Operational Land Imager and Multispectral Instrument) and synthetic aperture radar (SAR) (Sentinel-1 and TerraSAR-X) satellite sensors to investigate the mid-October 2019 surtseyan eruption at Late’iki Volcano, located on the Tonga Volcanic Arc. During the eruption, the remains of an older volcanic island formed in 1995 collapsed and a new volcanic island, called New Late’iki was formed. After the 12 days long lasting eruption, we observed a rapid change of the island’s shape and size, and an erosion of this newly formed volcanic island, which was reclaimed by the ocean two months after the eruption ceased. This fast erosion of New Late’iki Island is in strong contrast to the over 25 years long survival of the volcanic island formed in 1995.

Appraisal of Spatiotemporal Variations in Snow Cover Dynamics using Geospatial Techniques: A paradigm from Gilgit-Baltistan, Pakistan

Snow and glaciers in the mountain ranges of Hindu-Kush, Karakoram, and Himalayas (HKH), are supposed to be shrinking. Consequently, serious significances occur in respect of water accessibility for inhabitants of down streams. This investigation is an effort o identify the temporal variation in snow covers of Gilgit Baltistan (GB), northern Pakistan. For the calculation of the Normalized Difference Snow Index (NDSI) and estimation of snow-covered area (SCA), MODIS images for the period of 2000 to 2020 were used. Hunza, Astore, Gilgit, Diamir, Shyok, Ghanche, Skardu and Shigar are major sub-basins lying in GB with Hunza and Skardu being the mostly glaciated areas. Annual fluctuation in snow cover within GB was 10 to 80%. In accumulation season, from December to February, snow cover was almost 80% while in melting season, from July to September this area was reduced to 65-75%. The maximum and minimum Snow-Covered Area (SCA) detected in 2009 was almost 57687.85 km2 and 12083.40 km2 respectively. These results show fluctuations in SCA within the time duration of the last two decades.

The spatio-temporal soil moisture variation along the major tributaries of Zambezi River in the Mbire District, Zimbabwe

This study uses the Surface Energy Balance System (SEBS) and the TOPographic driven MODEL (TOPMODEL) in estimating soil moisture for Mbire district in Zimbabwe and validates these estimates with ground based gravimetric measurements baseded on fifty-four soil moisture sampling sites. Five atmospherically corrected MODIS images for the period 2013were processed and used to estimate evapotransiration. The result of the relative evaporation from the SEBS algorithm was multiplied by the average of the porosity and the field capacity to get the spatial and temporal soil moisture estimates. The TOPMODEL rainfall-runoff model whose land surface inputs were obtained from remote sensing was calibrated with runoff data for the period ranging 2008−2013. After successful calibration the model was used to to simulate spatial and temporal soil moisture estimates and compare with insitu-measured soil moisture from the fifty-two sampling sites. An upscaling procedure to improve the comparison between soil moisture retrieval point measurements, remote sensing and TOPMODEL outputs was accomplished by the use of geostatistical tools. Land suitability analysis for flood recession farming was performed using soil moisture maps, distance from stream network, vertical channel distance, and land use/cover datasets. The model performance indicators for TOPMODEL simulations show an acceptable match with measured discharge, (NSE = 0.765 and 0.812 and PBIAS= -6.04 % and -10.5 % for Manyame and Angwa sub catchments respectively). Results show that the SEBS approach show spatial and temporal soil moisture variability across the Mbire district simulated. There is a strong relationship (R2 = 0.796) between upscaled insitu-based soil moisture measurements and SEBS based techniques for the period of March to July 2013. The study further revealed that there is a fair relationship (R2 = 0.60) between upscaled ground soil moisture measurements and hydrological modelling (TOPMODEL) for the same period. TOPMODEL soil moisture increases towards the Angwa and Manyame river tributaries. Results also show that approximately 22800 ha of Mbire district has suitable to moderately suitable flood recession farming area. The study concludes that the use of geostatistical approaches, remote sensing and hydrologic models is promising in planning and management of soil moisture and water resources in data scarce and water limited environments.

Water Consumption Modeling by Coupling MODIS Images and Agrometeorological Data for Sugarcane Crops

Actual evapotranspiration (ETa) is the main component of crop water consumption. Sugarcane requires large volumes of water during its development and may induce changes in local and regional climate. Field measurements, besides their high cost, are insufficient to understand the ETa spatial variability—a gap that can be filled with remote sensing techniques, due to the spatial nature of satellite imaging and its low cost, which allows the construction of temporal, spatial series of estimates of energy and water balances components. The Simple Algorithm For Retrieving Evapotranspiration (SAFER) model was applied to sugarcane crop in the mesoregion of Bauru, Sao Paulo, Brazil. Surface agrometeorological data were used as inputs along with parameters obtained by remote sensing from January 2015 to June 2016. The highest values of daily ETa (> 5.0 mm d−1) were observed in the rainy season, during the sugarcane growing stage, indicating the phase of higher water demand, and the lowest values (< 3.0 mm d−1) were verified in the dry season, during the stages of emergence and maturation. Sugarcane crop was responsible for the consumption of 52% to 62% of the volume of precipitated water in the Bauru meso region. The results of the SAFER model using meteorological data from remote sensing (SAFER-RS) presented d1 = 0.72 in relation to results using surface weather data from the Brazilian National Institute of Meteorology-INMET (SAFER-INMET). The errors in the estimation of ETa by SAFER-RS may be due to errors in the meteorological data from the remote sensing used.

Subpixel Analysis of MODIS Imagery Time Series using Transfer Learning and Relative Calibration

Transfer learning reuses a pre-trained model on a new related problem, which can be useful for monitoring large areas such as the Amazon biome. A given object must have similar spectral characteristics in the data used for this type of analysis, which can be achieved using relative calibration techniques. In this article, we present a relative calibration process in multitemporal images and evaluate its impacts on a subpixel classification process. MODIS images from the Amazon region, collected between 2013 and 2017, were relatively calibrated using a 2012 image as reference and classified by transfer learning. Classifications of calibrated and uncalibrated images were compared with data from the PRODES project, focusing on forest areas. A great variation was observed in the spectral responses of the forest class, even in images of proximate dates and from the same sensor. These variations significantly impacted the land cover classifications in the subpixel, with cases of agreement between the uncalibrated data maps and PRODES of 0%. For calibrated data, the agreement values ​​were greater than 70%. The results indicate that the method used, although quite simple, is adequate and necessary for the subpixel classification of MODIS images by transfer learning.

Recognition of different yield potentials among rain-fed wheat fields before harvest using remote sensing

An algorithm was proposed to classify wheat fields into high-productive and low-productive classes using satellite images according to a threshold value for rain-fed wheat ETa in the anthesis stage. Since the classification process was based on the ETa values in the rain-fed wheat pixels, an algorithm was proposed to map wheat fields using the combination of MODIS and Landsat-8 images. The wheat area mapping method included two major processes in which the first one used a step by step elimination process of non-wheat pixels which did not follow the standard wheat vegetation index time series; the second one used supervised classification methods for detecting the rain-fed wheat pixels. The assessment of the wheat area map was performed statistically using surveyed wheat plots. After detecting the wheat parcels, the threshold value was determined using a frequency analysis on actual evapotranspiration values in the parcels. The rain-fed wheat ETa values estimated from the SEBAL algorithm and were compared to the results from the Eagleman-Affholder method and MOD16A2 products. To assess if the wheat fields in productivity classes have been categorized correctly, yield values in the two classes were compared with each other. The rain-fed wheat yield was estimated using the light use efficiency model and compared to provincial census data for accuracy assessment. Results showed that the overall accuracy, Kappa coefficient, and F1 score values for the 250-m resolution map from the MODIS images were 82, 0.61, and 0.71, respectively. In the same order, these statistics for the 30-m resolution map from the Landsat-8 images were 92, 0.62, and 0.77, respectively. Both the SEBAL and the Eagleman-Affholder methods closely estimated the average wheat ETa value in the anthesis stage equal to 2.4 mm/day. The mean of the rain-fed wheat yield value from the LUE model in 2015 was 10% lower than the census data. Relying on the low amount of the absolute error between the LUE model and the census data, the mean of the yield values in both high-productive and low-productive classes were compared with together. Results showed that the mean amount of yield in high-productive class was 33% (271 kg/ha) more than that of in low-productive class.

Remote Sensing Monitoring Method for Groundwater Level on Aeolian Desertification Area

Groundwater is one of the most important natural resources. Regional groundwater depth is an important parameter for understanding groundwater resources and maintaining sustainable development of water resources and environment. The middle reaches of the Tarim River in Xinjiang are the most primitive and well-preserved place in the world, which provides valuable resources in studying the response mechanism of surface vegetation to the groundwater level. The ecological environment of Tarim River Basin has been deteriorating, and Populuseuphratica forest has died, which is directly related to the decrease of water inflow and groundwater level around the Tarim River. To obtain the spatial distribution of the groundwater level, this study uses the MODIS satellite remote sensing image data and the remote sensing-mathematical-model of a fusion science research methods, based on the field investigation of the groundwater level, soil moisture, and other supporting information on Aeolian desertification area in the middle reaches of Tarim River in Xinjiang. Through the experimental equation of the soil moisture and groundwater level, a simple and effective remote sensing method was proposed. This method is used to evaluate the spatial distribution of groundwater level based on the MODIS image data when there is capillary supply in the soil. This model was field-proven on the desertification area in the middle reaches of Tarim River. The results indicate that the correlation coefficient between the inversion of groundwater depth and the measured groundwater level is 0.89, which are realistic with small errors. So it is feasible to monitor and assess the spatial distribution of groundwater table depth in desertification areas with a large groundwater depth of 6 m or less. This study is helpful to provide critical area for the ecological environment monitoring and restoration, and ultimately serve the sustainable development of water and environment.

FROM SPACE TO EARTH: PHYSICAL AND BIOLOGICAL IMPACTS OF GLACIER DYNAMICS IN THE MARINE SYSTEM BY MEANS OF REMOTE SENSING AT ALMIRANTAZGO BAY, ANTARCTICA

Abstract. To determine the biological and physical mechanisms between Lange glacier and its pro-glacier marine system located in Almirantazgo bay, King George Island, Antarctica, specific variables were determined through remote sensing approaches shown in this work. These preliminary results will allow relating the dynamics of both systems, the glacier, and the marine ecosystem. The information for the estimation of surface flux velocity of the glacier was derived through Radar satellite images (Sentinel-1) by means of offset tracking, the bathymetry was derived from a Multi-beam Echo Sounder, and the Digital Elevation Modell was obtained by means of a Remotely Piloted Aircraft; Finally, the biological parameters were derived from MODIS and OLCI images for the analysis of satellite data to have a first insight to the characteristics of the marine system. This information will help to build the first frame needed to study through remote sensing approaches, the mechanisms that govern the interface among “Lang Glacier” and the “Almirantazgo Bay” at King George Island, Antarctica.

Geomatics and EO Data to Support Wildlife Diseases Assessment at Landscape Level: A Pilot Experience to Map Infectious Keratoconjunctivitis in Chamois and Phenological Trends in Aosta Valley (NW Italy)

Geomatics and satellite remote sensing offer useful analysis tools for several technical-scientific fields. This work, with reference to a regional case of study, investigates remote sensing potentialities for describing relationships between environment and diseases affecting wildlife at landscape level in the light of climate change effects onto vegetation. Specifically, the infectious keratoconjunctivitis (IKC) of chamois (Rupicapra rupicapra L.) in Aosta Valley (NW Italy) was investigated at the regional level. IKC (Mycoplasma conjunctivae) is a contagious disease for domestic and wild ruminants (Caprinae and Ovinae). Two types of analysis were performed: one aimed at exploring by remotely sensed data phenological metrics (PMs) and evapotranspiration (ET) trends of vegetation in the area; one investigating the correlation between PMs and ET, versus IKC prevalence. The analysis was based on TERRA MODIS image time series ranging from 2000 to 2019. Ground data about IKC were available for a shorter time range: 2009–2019. Consequently, PMs and ET trend investigations were focused on the whole times range (2000–2019); conversely, correlation analysis was achieved with reference to the reduced 2009–2019 period. The whole study was based on freely available data from public archives. MODIS products, namely MOD13Q1 v.6 and MOD16A2, were used to derive PM and ET trends, respectively. Shuttle Radar Topography Mission (SRTM) Digital Terrain Model (DTM) was used to describe local topography; CORINE Land Cover map was adopted to describe land use classes. PMs and ET (as derivable from EO data) proved to significantly changed their values in the last 20 years, with a continuous progressive trend. As far as correlation analysis was concerned, ET and some PMs (specifically, End of Season (EOS) and Length of Season (LOS) proved significantly condition IKC prevalence. According to results, the proposed methodology can be retained as an effective tool for supporting public health and eco-pathological sectors. Specifically, it can be intended for a continuous monitoring of effects that climatic dynamics determine onto wild animals in the Alpine area, included diseases and zoonosis, moving future environmental management and planning towards the One Health perspective.

Changing Patterns of Lakes on The Southern Tibetan Plateau Based on Multi-Source Satellite Data

More than 1100 lakes covering an area greater than 4500 km2 are located on the Tibetan Plateau, and these lakes are important regulators of several large and famous rivers in Asia. The determination of hydrological changes that have occurred in these lakes can reflect climate change and supply scientific data to plateau environmental research. Data from high frequency (moderate-resolution imaging spectro-radiometer) MODIS images, altimetry, and the Hydroweb database collected during 2000–2015 were integrated in this study to delineate the detailed hydrological changes of 15 lakes in three basins—Inner Basin, Indus Basin, and Brahmaputra Basin—on the southern Tibetan Plateau. Seven of the 10 lakes in the Inner Basin presented increasing trends with various intensities, and the increasing rates in the area of four lakes (Nam Co, Selin Co, Zhari-namco, and Ngangze) were 1.62, 28.81, 2.27, and 3.70 km2/yr, respectively. The yearly increases in volume of the four lakes were 3.6, 9.44, 6, and 2.36 km3, respectively. A water balance equation was established for the four lakes based on lake volume changes to illustrate the contributions of precipitation, ground runoff, evaporation, and other factors. The results revealed that surface runoff was the major contributor to expansion, and lake surface evaporation was almost 2.76–3.86 times that of lake surface precipitation. The two lakes in Indus Basin, Rakshastal and Mapam Yumco, presented a slight retreat. As a representative of Brahmaputra Basin, Yamzho Yumco underwent a retreat of –3.49 km2/yr in area, –0.39 m/yr in level, and –0.19 km3/yr in volume. Decreasing precipitation, increasing evaporation, and the operation of a hydrological project were the main causes of its constant retreat.

A methodology to estimate forest fires burned areas and burn severity degrees using Sentinel-2 data. Application to the October 2017 fires in the Iberian Peninsula

A methodology to estimate the extent of areas affected by forest fires, as well as the burn severity levels using Sentinel 2 images (10 and 20 m) is proposed and applied to the fires occurred in October 2017 in Spain and Portugal. An extension larger than 250,000 ha and 4 burn severity levels (low, moderate, high and very high) have been obtained. The comparison with the European Forest Fire Information System (EFFIS), which uses MODIS images (250 m), shows that the methodology improves the area estimate by 10 % in commission area. In terms of burn severity levels, the Separability index (SI) and the Kappa statistic (k) show a high correlation between Sentinel-2 and EFFIS (SI values higher than one in all cases and k higher than 0.69, respectively).

Effects of sea ice on wave energy flux distribution in the Bohai Sea

Abstract The wave energy flux distribution in the Bohai Sea during wintertime is re-evaluated based on SWAN with the added sea-ice effect, which is derived from an ice-ocean coupled model, FVCOM. Simulated wave parameters and ice cover agree well with the measured wave data and the MODIS images of sea-ice cover, respectively. The results indicate that the decrease of wave energy flux is mainly caused by the formed ice which leads to the reduction of the effective wind fetch and transferred momentum from air to the ocean surface. The numerical results in a typical winter from 2011 to 2012 show that, due to the presence of sea ice, the average wave energy fluxes can be decreased by up to 80% in Liaodong Bay, while up to 50% in Bohai Bay and Laizhou Bay. Under the extreme winter-weather conditions, more pronounced reduction of wave energy flux caused by ice is observed. The findings also imply that in the design of wave energy converters (WECs) and their deployed locations in the Bohai Sea, even the likely ice-free areas in winter time, the effect of other ice-covered regions on the decrease of wave energy flux as a result of decrease in wind fetch should be considered.

Spatiotemporal analysis of land surface temperature using multi-temporal and multi-sensor image fusion techniques

Urban Heat Island (UHI) is a major challenge in urban environments that affects human activities. This phenomenon is caused and changed by various factors in urban environments, in which identification of such parameters requires fine-scale satellite images. This study, therefore, aimed at investigating spatiotemporal changes of UHIs as well as identification of important factors using remote sensing image fusion techniques in Rasht. The image dataset comprises of Landsat 5, 7, 8 and MODIS images from 2001 to 2018. After pre-processing, multi-temporal and multi-sensor image fusion techniques were used to retrieve Landsat 7 images as well as Landsat-like medium spatial resolution images. The effects of built-up areas and surface biophysical characteristics such as brightness, greenness and wetness were also examined on LST changes through time. The results showed that multi-temporal and multi-sensor image fusion methods provide appropriate accuracies and the multi-temporal fusion method performs better than the multi-sensor fusion method. The results also showed that retrieval of sensor products is of higher accuracy than that of the spectral bands. The spatiotemporal changes of UHIs were indicative of an increasing trend over time. Of the surface biophysical parameters, the normalized difference built-up index exhibited the highest correlation with changes of normalized LST.

A comparison of actual evapotranspiration estimates based on Remote Sensing approaches with a classical climate data driven method

The knowledge of actual evapotranspiration at farm level is a prerequisite for irrigation planning, farm management, to increase production and reduce water consumption. To accomplish this, comprehensive and accurate assessment methods should be applied. In order to evaluate accurately evapotranspiration processes we compared lysimeter evapotranspiration data with MODIS (Aqua and Terra satellites) and LANDSAT (SEBAL algorithm) satellite images as well as with the FAO Penman-Montith method. The findings indicate the low error rate, high correlation (1) and appropriateness of SEBAL in estimating actual evapotranspiration. The error values MAD, MSE and RMSE between lysimeter and the SEBAL algorithm were 0.59, 0.36 and 0.60 respectively. The second best performance was established for the FAO Penman-Montith method. The obtained error values MAD, MSE and RMSE between the lysimeter and FAO-Penman-Montith method are 0.91, 1.29 and 1.13, respectively.

Analysis of Variance and Correlation Study of Chlorophyll-a in North and South Coast of Lombok Island Using Aqua MODIS Image Data

Chlorophyll-a mapping is widely used in the coastal environment. The mapping results can be utilized as guidance for fishing activities. The presence of chlorophyll-a in phytoplankton is much investigated concerning the existence of fish as a source of livelihood for fishermen around the globe. One method extensively developed is the use of satellite imagery to map the chlorophyll-content in the coastal region from a small area to a global scale, such as Aqua MODIS image data. This study aims to monitor the distribution of chlorophyll-a on the north coast and south coast of Lombok island. Analysis of variance and parametric statistical tests with t-distribution was utilized to examine the correlation between the two types of chlorophyll-a distribution of the coast area, Lombok Island. The result shows that the distribution of chlorophyll-a concentrations on the north coast and the south of the coast is not the same abundance; the south coast of Lombok island has smaller variances, with the concentration distribution is relatively the same. It differs from the north part; the distribution shows less diversity. This result indicates that a reasonably wide interval wherein some coordinates has a relatively diffuse chlorophyll-a concentration with a sufficiently high distribution level. Analysis of variance result also shows that both the north and south coasts have different fertility levels for their coastal waters, which are characterized by the growth rate of chlorophyll-a on the surface water along the coastal region examined

The use of remote sensing techniques by Modis (MCD45A1) images using to identify and evaluate burned areas in the metropolitan region of Belo Horizonte - MG, Brazil

Estudos sobre a ocorrência de queimadas são muito importantes, visto que eles corroboram medidas de controle, prevenção e monitoramento destas. Incêndios florestais causam vários impactos, entre eles perda da biodiversidade e prejuízo à saúde humana, sendo um problema tanto na esfera ambiental quanto social e política. Uma forma de monitoramento desses incêndios é a utilização de técnicas de sensoriamento remoto. Assim, a proposta do presente trabalho foi a utilização de imagens Modis (MCD45A1) para identificação e avaliação de áreas queimadas na região metropolitana de Belo Horizonte, entre os anos de 2012 e 2016, validando-o com dados do sensor Landsat-8/OLI. Com o emprego dos dados Landsat, é possível delimitar com melhor precisão a dimensão da área queimada, mas exigindo-se conhecimento técnico operacional e corpo técnico especializado para tanto. Já o produto decorrente do Modis, é mais acessível e de fácil utilização, sendo de aplicação direta, pois já informa os focos de incêndio. Observou-se grande ocorrência de queimadas na região de estudo, principalmente na primavera e no inverno. Os resultados encontrados aqui poderão ser utilizados em estudos futuros para diminuir a incidência de queimadas na região e assim preservar a biodiversidade de biomas importantes nela presente.

Modeling large-scale biometeorological indices to monitor agricultural-growing areas: applications in the fruit circuit region, São Paulo, Brazil.

This paper aimed to support the rational crop expansion in agricultural-growing regions. MODIS satellite images are used together with gridded weather data to model biometeorological parameters at the Fruit Circuit region, state of São Paulo, Southeast Brazil. This region has experienced some cases of drought, while arising rainfall water excess in some periods, demanding large-scale water and energy balance studies to subsidize water resource policies. The SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm was applied together with the radiation-use efficiency (RUE) model for biometeorological index assessments. The highest latent heat fluxes (λE), above 8.0MJm-2d-1, at the end of the year, coincide with the progressive increases on both rainfall and global solar radiation (RG) levels, and drop to below 5.0MJm-2d-1 in the middle of the year, during the driest conditions. The same tendencies along the year are verified for sensible heat fluxes (H), for which mean pixel values are above 3.5MJm-2d-1 at the end of the year. On the one hand, the highest values for water productivity (WP), which is considered the ratio of actual evapotranspiration (ET) to biomass production (BIO), above 4.0kgm-3, are verified in April, period under increasing BIO and low ET rates. On the other hand, the lowest WP values (below 2.0kgm-3) occur between August and October, when BIO is low, and ET is high. Although the area featuring good WP levels under high precipitation (P), with rainfalls generally above ET, supplementary irrigation may benefit agriculture in some periods of the year. The results of the large-scale modeling showed applicability of the models for monitoring water and vegetation dynamics over 16-day timescale and at a 250-m spatial resolution in areas experiencing climate and land-use changes by combining climate data and MODIS images. Application of these tools enables to indicate the best options for expanding the agriculture activities, being of great potential for rational natural resources management, in regions under environmental vulnerability conditions.

Particle Size Parameters of Particulate Matter Suspended in Coastal Waters and Their Use as Indicators of Typhoon Influence

The power law particle size distribution (PSD) slope parameter is commonly used to characterize sediment fluxes, resuspension, aggregates, and settling rates in coastal and estuarine waters. However, particle size distribution metrics are also very useful for understanding sediment source and dynamic processes. In this study, a method was proposed to employ the particle size parameters commonly used in sedimentary geology (average particle size (ø), sorting, skewness, and kurtosis) as indicators of changes in sediment dynamic processes, and MODIS images were used to estimate these parameters. The particle size parameters were estimated using a Mie scattering model, Quasi-Analytical Algorithm (QAA) analysis algorithm, and least squares QR decomposition (LSQR) solution method based on the relationship between the power law distribution of the suspended particles and their optical scattering properties. The estimates were verified by field measurements in the Yellow Sea and Bohai Sea regions of China. This method provided good estimates of the average particle size (ø), sorting, and kurtosis. A greater number of wavebands (39) was associated with more accurate particle size distribution curves. Furthermore, the method was used to monitor changes in suspended particulate matter in the vicinity of the Heini Bay of China before and after the passage of a strong storm in August 2011. The particle size parameters represented the influence of a strong typhoon on the distribution of the near-shore sediment and, together with the PSD slope, comprehensively reflected the changes in the near-shore suspended particulate matter. This method not only established the relationship between remote sensing monitoring and the historical sediment record, it also extends the power law model to the application of sediment source and dynamic processes in coastal waters.

Predicting ecosystem shift in a Salt Lake by using remote sensing indicators and spatial statistics methods (case study: Lake Urmia basin)

The consequences of unsustainable human activities on the environment are often delayed, when it is too late to compensate. New approaches are based on the use of “spatial statistics” of leading indicators to measure the “critical slowing down” in a degraded ecosystem, when it is reaching to a tipping point. This research predicts the tipping points in the ecosystem of Lake Urmia Basin (LUB) based on spatial statistics. By Remote Sensing (RS) indicators, their effectiveness in assessing the state of the ecosystem was evaluated in a 16-years period (2002-2017). Seven spectral indicators (NDVI, NDWIv,NDWIw,NDSI,SRDI, NMDI and MVWR) were extracted from ten MODIS images. Ability of the indicators to identify critical point in time-series was investigated by five spatial statistic methods (Moran’s-I, Getis-Ord-Gi, Geary’s-C, variance, and skewness). The results showed that Moran’s-I is more successful in predicting the ecosystem tipping point(s) in comparison with other methods. In addition, the ability to predict ecosystem trends by the autocorrelation of MVWR is higher than other indicators. According to results, the tipping points of LUB occurred in the years of 2008 to 2010 and 2015. For further studies, it is recommended to use radar indicators for identifying tipping points of the similar vulnerable ecosystems.