Winter wheat identification by integrating spectral and temporal information derived from multi-resolution remote sensing data

Timely crop acreage and distribution information are the basic data which drive many agriculture related applications.For identifying crop types based on remote sensing,methods using only a single image type have significant limitations.Current research that integrates fine and coarser spatial resolution images,using techniques such as unmixing methods,regression models,and others,usually results in coarse resolution abundance without sufficient detail within pixels,and limited attention has been paid to the spatial relationship between the pixels from these two kinds of images.Here we propose a new solution to identify winter wheat by integrating spectral and temporal information derived from multi-resolution remote sensing data and determine the spatial distribution of sub-pixels within the coarse resolution pixels.Firstly,the membership of pixels which belong to winter wheat is calculated using a 25-m resolution resampled Landsat Thematic Mapper(TM)image based on the Bayesian equation.Then,the winter wheat abundance(acreage fraction in a pixel)is assessed by using a multiple regression model based on the unique temporal change features from moderate resolution imaging spectroradiometer(MODIS)time series data.Finally,winter wheat is identified by the proposed Abundance-Membership(AM)model based on the spatial relationship between the two types of pixels.Specifically,winter wheat is identified by comparing the spatially corresponding 10×10 membership pixels of each abundance pixel.In other words,this method takes advantage of the relative size of membership in a local space,rather than the absolute size in the entire study area.This method is tested in the major agricultural area of Yiluo Basin,China,and the results show that acreage accuracy(Aa)is 93.01%and sampling accuracy(As)is 91.40%.Confusion matrix shows that overall accuracy(OA)is 91.4%and the kappa coefficient(Kappa)is 0.755.These values are significantly improved compared to the traditional Maximum Likelihood classification(MLC)and Random Forest classification(RFC)which rely on spectral features.The results demonstrate that the identification accuracy can be improved by integrating spectral and temporal information.Since the identification of winter wheat is performed in the space corresponding to each MODIS pixel,the influence of differences of environmental conditions is greatly reduced.This advantage allows the proposed method to be effectively applied in other places.

Characterizing urban expansion of Korla City and its spatial-temporal patterns using remote sensing and GIS methods

Cities provide spatial contexts for populations and economic activities. Determining the spatial-temporal patterns of urban expansion is of particular significance for regional sustainable development. To achieve a better understanding of the spatial-temporal patterns of urban expansion of Korla City, we explore the urban expansion characteristics of Korla City over the period 1995-2015 by employing Landsat TM/ETM＋ images of 1995, 2000, 2005, 2010, and 2015. Urban land use types were classified using the supervised classification method in ENVI 4.5. Urban expansion indices, such as expansion area, expansion proportion, expansion speed, expansion intensity, compactness, and fractal dimension, were calculated. The spatial-temporal patterns and evolution process of the urban expansion （e.g., urban gravity center and its direction of movement） were then quantitatively analyzed. The results indicated that, over the past 25 years, the area and proportion of urban land increased substantially with an average annual growth rate of 15.18%. Farmland and unused land were lost greatly due to the urban expansion. This result might be attributable to the rapid population growth and the dramatic economic development in this area. The city extended to the southeast, and the urban gravity center shifted to the southeast as well by about 2118 m. The degree of urban compactness tended to decrease and the fractal dimension index tended to increase, indicating that the spatial pattern of Korla City was becoming loose, complex, and unstable. This study could provide a scientific reference for the studies on urban expansion of oasis cities in arid land.

Research on the Optimal Vegetation Cover for Remote Sensing Assessment of Soil Erosion Risk Using the Temporal Matching Relationship between Rainfall and Vegetation

Vegetation cover derived from remote sensing image is widely used for soil erosion risk assessment, but there is no clear guideline to select the most appropriate temporal satellite data. It is common practice that satellite data during growing season are randomly selected and used in soil erosion risk assessment. However, the effectiveness of vegetation in protecting the soil is quite different even if it is the same growing season since vegetation covers change as they grow. This article aims to provide a method of choosing optimal vegetation cover for studying soil erosion risk using remote sensing, that is, the vegetation cover in the most appropriate temporal period. Based on the temporal relationship of the two most active impact factors, rainfall and vegetation, an index of RV is developed and used to indicate the relative erosion risk during the year. The results show that annual variation of rainfall is significant, and vegetation is relatively stable, resulting in their matching relationship is different in each year. The correlation coefficient reaches 0.89 between RV and real sediment transport during the period when rainfall can cause soil erosion. In other words, RV is a good indicator of soil erosion. Therefore, there is a good correlation between RV maximum and the optimal vegetation cover, which can help facilitate erosion research in the future, showing good potential for successful application in other places.

Remote Sensing Derived Phenological Metrics to Assess the Spatio-Temporal Growth Variability in Cropping Fields

Precision Agriculture (PA) recognizes and manages intra-field spatial variability to increase profitability and reduced environmental impact. Site Specific Crop Management (SSCM), a form of PA, subdivides a cropping field into uniformly manageable zones, based on quantitative measurement of yield limiting factors. In Mediterranean environments, the spatial and temporal yield variability of rain-fed cropping system is strongly influenced by the spatial variability of Plant Available Water-holding Capacity (PAWC) and its strong interaction with temporally variable seasonal rainfall. The successful adoption of SSCM depends on the understanding of both spatial and temporal variabilities in cropping fields. Remote sensing phenological metrics provide information about the biophysical growth conditions of crops across fields. In this paper, we examine the potential of phenological metrics to assess the spatial and temporal crop yield variability across a wheat cropping field at Minnipa, South Australia. The Minnipa field was classified into three management zones using prolonged observations including soil assessment and multiple year yield data. The main analytical steps followed in this study were: calculation of the phenological metrics using time series NDVI data from Moderate Resolution Imaging Spectroscope (MODIS) for 15 years (2001-2015);producing spatial trend and temporal variability maps of phenological metrics;and finally, assessment of association between the spatial patterns and temporal variability of the metrics with management zones of the cropping field. The spatial trend of the seasonal peak NDVI metric showed significant association with the management zone pattern. In terms of temporal variability, Time-integrated NDVI (TINDVI) showed higher variability in the “good” zone compared with the “poor” zone. This indicates that the magnitude of the seasonal peak is more sensitive to soil related factors across the field, whereas TINDVI is more sensitive to seasonal variability. The interpretation of the association between phenological metrics and the management zone site conditions was discussed in relation to soil-climate interaction. The results demonstrate the potential of the phenological metrics to assess the spatial and temporal variability across cropping fields and to understand the soil-climate interaction. The approach presented in this paper provides a pathway to utilize phenological metrics for precision agricultural management application.

Cloud removal of remote sensing image based on multi-output support vector regression

Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-scale decomposition of the area of thin cloud cover on remote sensing images. Through enhancing coefficients of high frequency and suppressing coefficients of low frequency, the thin cloud is removed. For thick cloud cover, if the areas of thick cloud cover on multi-source or multi-temporal remote sensing images do not overlap, the multi-output support vector regression learning method is used to remove this kind of thick clouds. If the thick cloud cover areas overlap, by using the multi-output learning of the surrounding areas to predict the surface features of the overlapped thick cloud cover areas, this kind of thick cloud is removed. Experimental results show that the proposed cloud removal method can effectively solve the problems of the cloud overlapping and radiation difference among multi-source images. The cloud removal image is clear and smooth.

Accuracy Analysis on the Automatic Registration of Multi-Source Remote Sensing Images Based on the Software of ERDAS Imagine

The automatic registration of multi-source remote sensing images (RSI) is a research hotspot of remote sensing image preprocessing currently. A special automatic image registration module named the Image Autosync has been embedded into the ERDAS IMAGINE software of version 9.0 and above. The registration accuracies of the module verified for the remote sensing images obtained from different platforms or their different spatial resolution. Four tested registration experiments are discussed in this article to analyze the accuracy differences based on the remote sensing data which have different spatial resolution. The impact factors inducing the differences of registration accuracy are also analyzed.

Retrieval study of lake water depth by using multi-spectral remote sensing in Bangong Co Lake

This paper reviewed studies on remote sensing of water depth retrieval. Four water depth retrieval models （single-band, dou- ble-ratio-band, multi-band, and BP network models） were evaluated using TM image and water data from Bangong Co Lake, which is located in China＇s Tibet Autonomous Region and Indian Kashmir. Tested by independent data, comparison of these four models demonstrates that BP network model performed better than the multi-band model, with the single-band model performing the worst. To sum up, this study demonstrates that first, BP network model performed better than the traditional model; second, precise atmospheric correction and radiation study, affected by different water level sand sediment, could improve the precision of water depth retrieval.

Suitable Land Assessment for Rice Crop in Burkina Faso Using GIS, Remote Sensing and Multi Criteria Analysis

In Burkina Faso, rice consumption is currently common and increasing in both, rural and urban areas. Although several efforts have been made by the state to develop land for rice cultivation, the population’s demand is still greater than the supply. Nevertheless, the country has great potential for rice cultivation. This study aims to analyze the suitability of land for rice cropping in the province of Nahouri. Field and satellite data were collected to map the rice crops developed areas. Furthermore, morphopedological, topographic, land use/cover, and accessibility to land data were collected and integrated into a Geographic Information System (GIS) based on simple Multi-Criteria Analysis (MCA). Land suitability for rice crop was performed. The results show that the total suitable area for rice cultivation in the province is 106,373 ha (28% of the province) where the most suitable areas cover 32,614 ha and the suitable areas cover 73,759 ha. Already 3144 ha of the province area had been developed for rice cultivation with 815 ha and 845 ha of most suitable and suitable areas respectively whereas more than 45% of the developed lands were not fit for the rice crop suitable land.

Red Tide Information Extraction Based on Multi-source Remote Sensing Data in Haizhou Bay

[Objective] The aim was to extract red tide information in Haizhou Bay on the basis of multi-source remote sensing data.[Method] Red tide in Haizhou Bay was studied based on multi-source remote sensing data,such as IRS-P6 data on October 8,2005,Landsat 5-TM data on May 20,2006,MODIS 1B data on October 6,2006 and HY-1B second-grade data on April 22,2009,which were firstly preprocessed through geometric correction,atmospheric correction,image resizing and so on.At the same time,the synchronous environment monitoring data of red tide water were acquired.Then,band ratio method,chlorophyll-a concentration method and secondary filtering method were adopted to extract red tide information.[Result] On October 8,2005,the area of red tide was about 20.0 km2 in Haizhou Bay.There was no red tide in Haizhou bay on May 20,2006.On October 6,2006,large areas of red tide occurred in Haizhou bay,with area of 436.5 km2.On April 22,2009,red tide scattered in Haizhou bay,and its area was about 10.8 km2.[Conclusion] The research would provide technical ideas for the environmental monitoring department of Lianyungang to implement red tide forecast and warning effectively.

Dynamics of Soil Erosion in Xingguo County, China, Determined Using Remote Sensing and GIS

The spatial and temporal dynamics of soil erosion in Xingguo County, Jiangxi Province, China were studied using multi-period remote sensing images and GIS. The results indicated that the soil erosion status of the region has been improving, particularly since the 1980s, with the erosion rate showing an evident decline over the past 30 years. The improvement showed not only in the decline of eroded soil area, but also with the reduction in the extent of erosion. The extent of erosion mainly changed by one level, and the change primarily occurred with the severely or moderately eroded soil types. However, in general, soil erosion was still an overriding problem in the region with some areas becoming more serious, especially those with large quantities of granite.

Grassland degradation in Northern Tibet based on remote sensing data

This study selected vegetation cover as the main evaluation index, calculated the grassland degradation index （GDI） and established the remote sensing monitoring and evaluation system for grassland degradation in Northern Tibet, according to the National Standard （GB19377-2003）, based on the remote sensing data such as NDVI data derived from NOAA/AVHRR with a spatial resolution of 8 km of 1981-2000, from SPOT/VGT with a spatial resolution of 1 km of 2001 and from MODIS with a spatial resolution of 0.25 km of 2002-2004 respectively in this area, in combination with the actual condition of grassland degradation. The grassland degradation processes and their responses to climate change during 1981-2004 were discussed and analyzed in this paper. The result indicated that grassland degradation in Northern Tibet is very serious, and the mean value of GDI in recent 20 years is 2.54 which belongs to the serious degradation grade. From 1981 to 2004, the GDI fluctuated distinctly with great interannual variations in the proportion of degradation degree and GDI but the general tendency turned to severe-grade during this period with the grassland degradation grade changed from light degraded to serious degraded in Northern Tibet. The extremely serious degraded and serious degraded grassland occupied 1.7% and 8.0% of the study area, the moderate and light degraded grassland accounted for 13.2% and 27.9% respectively, and un-degraded grassland occupied 49.2% of the total grassland area in 2004. The grassland degradation was serious, especially in the conjunctive area of Naqu, Biru and Jiali counties, the headstream of the Yangtze River lying in the Galadandong snow mountain and glaciers, the area along the Qinghai-Tibet highway and railway, and areas around the Tanggula and Nianqingtanggula snow mountains and glaciers. So the snow mountains and glaciers as well as their adjacent areas in Northern Tibet were sensitive to climate change and the areas along the vital communication line with frequent human activities experienced relatively serious grassland degradation.

Geological mapping and mining prospecting in the Aouli inlier(Eastern Meseta,Morocco)based on remote sensing and geographic information systems(GIS)

Landsat 8 Oli,ASTER,and Sentinel 2A satellite images processing was used to map geological formations,lineaments and hydrothermal alteration minerals in the Aouli inlier,as a case study to illustrate the application of digital images processing and Geographic Information System(GIS)in geological mapping and mining prospecting.Principal Component Analysis(PCA)applied to the Landsat images allowed good lithological discrimination and contributed to the updating of available geological maps.The Automatic lineament extraction from Sentinel images revealed the main tectonic structures affecting Aouli inlier.The ratio bands(b5+b7)/b6 and the false color composite(b4/b6,b2/b1,b3/b2)allowed the hydrothermal alteration minerals mapping from Aster images.Combined with available geological data and field observations,the satellite derived data were integrated and analyzed in a GIS software to establish mining prospecting guides.The results showed that the anomaly zones are intimately linked to NNE-SSW and NW-SE oriented faults and to highly fractured areas developing argillic and Fe rich alterations.Verified via field survey,this approach was successfully applied to the Aouli inlier to rapidly target potential areas to be explored in the tactical phase.This provides a model for future prospecting efforts for similar mineral deposits in other areas.

Framework of SAGI Agriculture Remote Sensing and Its Perspectives in Supporting National Food Security

Remote sensing, in particular satellite imagery, has been widely used to map cropland, analyze cropping systems, monitor crop changes, and estimate yield and production. However, although satellite imagery is useful within large scale agriculture applications （such as on a national or provincial scale）, it may not supply sufifcient information with adequate resolution, accurate geo-referencing, and specialized biological parameters for use in relation to the rapid developments being made in modern agriculture. Information that is more sophisticated and accurate is required to support reliable decision-making, thereby guaranteeing agricultural sustainability and national food security. To achieve this, strong integration of information is needed from multi-sources, multi-sensors, and multi-scales. In this paper, we propose a new framework of satellite, aerial, and ground-integrated （SAGI） agricultural remote sensing for use in comprehensive agricultural monitoring, modeling, and management. The prototypes of SAGI agriculture remote sensing are ifrst described, followed by a discussion of the key techniques used in joint data processing, image sequence registration and data assimilation. Finally, the possible applications of the SAGI system in supporting national food security are discussed.

Improving remote sensing-based net primary production estimation in the grazed land with defoliation formulation model

Remote sensing(RS) technologies provide robust techniques for quantifying net primary productivity(NPP) which is a key component of ecosystem production management. Applying RS, the confounding effects of carbon consumed by livestock grazing were neglected by previous studies, which created uncertainties and underestimation of NPP for the grazed lands. The grasslands in Xinjiang were selected as a case study to improve the RS based NPP estimation. A defoliation formulation model(DFM) based on RS is developed to evaluate the extent of underestimated NPP between 1982 and 2011. The estimates were then used to examine the spatiotemporal patterns of the calculated NPP. Results show that average annual underestimated NPP was 55.74 gC·m^(-2)yr^(-1) over the time period understudied, accounting for 29.06% of the total NPP for the Xinjiang grasslands. The spatial distribution of underestimated NPP is related to both grazing intensity and time. Data for the Xinjiang grasslands show that the average annual NPP was 179.41 gC·m^(-2)yr^(-1), the annual NPP with an increasing trend was observed at a rate of 1.04 gC·m^(-2)yr^(-1) between 1982 and 2011. The spatial distribution of NPP reveals distinct variations from high to low encompassing the geolocations of the Tianshan Mountains, northern and southern Xinjiang Province and corresponding with mid-mountain meadow, typical grassland, desert grassland, alpine meadow, and saline meadow grassland types. This study contributes to improving RS-based NPP estimations for grazed land and provides a more accurate data to support the scientific management of fragile grassland ecosystems in Xinjiang.

A two-scale approach for estimating forest aboveground biomass with optical remote sensing images in a subtropical forest of Nepal

Forests account for 80%of the total carbon exchange between the atmosphere and terrestrial ecosystems.Thus,to better manage our responses to global warming,it is important to monitor and assess forest aboveground carbon and forest aboveground biomass(FAGB).Different levels of detail are needed to estimate FAGB at local,regional and national scales.Multi-scale remote sensing analysis from high,medium and coarse spatial resolution data,along with field sampling,is one approach often used.However,the methods developed are still time consuming,expensive,and inconvenient for systematic monitoring,especially for developing countries,as they require vast numbers of field samples for upscaling.Here,we recommend a convenient two-scale approach to estimate FAGB that was tested in our study sites.The study was conducted in the Chitwan district of Nepal using GeoEye-1(0.5 m),Landsat(30 m)and Google Earth very high resolution(GEVHR)Quickbird(0.65 m)images.For the local scale(Kayerkhola watershed),tree crowns of the area were delineated by the object-based image analysis technique on GeoEye images.An overall accuracy of 83%was obtained in the delineation of tree canopy cover(TCC)per plot.A TCC vs.FAGB model was developed based on the TCC estimations from GeoEye and FAGB measurements from field sample plots.A coefficient of determination(R2)of 0.76 was obtained in the modelling,and a value of 0.83 was obtained in the validation of the model.To upscale FAGB to the entire district,open source GEVHR images were used as virtual field plots.We delineated their TCC values and then calculated FAGB based on a TCC versus FAGB model.Using the multivariate adaptive regression splines machine learning algorithm,we developed a model from the relationship between the FAGB of GEVHR virtual plots with predictor parameters from Landsat 8 bands and vegetation indices.The model was then used to extrapolate FAGB to the entire district.This approach considerably reduced the need for field data and commercial very high resolution imagery while achieving two-scale forest information and FAGB estimates at high resolution(30 m)and accuracy(R2=0.76 and 0.7)with minimal error(RMSE=64 and 38 tons ha-1)at local and regional scales.This methodology is a promising technique for cost-effective FAGB and carbon estimations and can be replicated with limited resources and time.The method is especially applicable for developing countries that have low budgets for carbon estimations,and it is also applicable to the Reducing Emissions from Deforestation and Forest Degradation(REDD?)monitoring reporting and verification processes.

Quantitative Application Study on Remote Sensing of Suspended Sediment

Quantitative application on remote sensing of suspended sediment is an important aspect of the engineering application of remote sensing study. In this paper, the Xiamen Bay is chosen as the study area. Eleven different phases of the remote sensing data are selected to establish a quantitative remote sensing model to map suspended sediment by using remote sensing images and the quasi-synchronous measured sediment data. Based on empirical statistics developed are the conversion models between instantaneous suspended sediment concentration and tidally-averaged suspended sediment concentration as well as the conversion models between surface layer suspended sediment concentration and＂ the depth-averaged suspended sediment concentration. On this basis, the quantitative application integrated model on remote sensing of suspended sediment is developed. By using this model as well as multi-temporal remote sensing images, multi-year averaged suspended sediment concentration of the Xiamen Bay are predicted. The comparison between model prediction and observed data shows that the multi-year averaged suspended sediment concentration of studied sites as well as the concentration difference of neighboring sites can be well predicted by the remote sensing model with an error rate of 21.61% or less, which can satisfy the engineering requirements of channel deposition calculation.

Towards a Remote Sensing and GIS-Based Technique to Study Population and Urban Growth: A Case Study of Multan

In recent decades, the migration rates of the large cities of Punjab have been risen up to a considerable level due to the lack of employment opportunities as well as lack of facilities in the rural areas of the province. It has caused an unprecedented and unplanned urbanization across the urban areas of the province. This study has been undertaken to perform fractal analysis about the sprawl in rapidly growing city. GIS and remote sensing data have been used in this study as an emerging technology which is cost effective as well as accurate at the same time. Landsat images have been taken for the study and the sprawl has been calculated with the analysis of the data of each decade for past more than 40 years. It has been observed that the built up area is 47.8 to 141.12 Sq. Km whereas the pattern of urban settlement has been classified as clustered and linear, following the roads network. The temporal population growth also seconded these results. The population growth rate and population density increase, are based on the pixel based extraction of the data from satellite imagery for the period of 2000 to 2014, which is taken as a decision support tool. In 2000, the population of the district increased from 2,071,694 (1981 census) to 2,939,907 and then in 2013, it became 4,384,191 at a rate to 2.93% upturn per annum. Moreover, the study also reveals the extent of the growth of other land uses as well which may be taken as a reference that in an agricultural country like Pakistan, the natural resources are being wasted (by urbanization of the fertile land). There must be some master planning to avoid such things in the other cities as well.

Solid Waste Dumping Site Selection Using GIS and Remote Sensing for Kajiado County, Kenya

Solid waste dumping is a hectic problem in urban and developing areas due to shortage of land for the purpose. The main objective of this study was to select potential areas for suitable solid waste dumping for Kajiado County, Kenya. Eight input map layers including DEM （digital elevation model）, topography, urban settlement, roads, wetlands, rivers, forests and protected areas were prepared and MCDA （Multi Criteria Decision Analysis Methods） were implemented in a GIS （geographic information systems） environment. GIS, RS （remote sensing） and MDCA are powerful tools which can effectively be applied during the planning phase of solid waste management in order to avoid adverse catastrophes in future. The final suitability map was prepared by weighted overlay analyses and leveled as the most suitable, moderate suitable, less suitable and unsuitable areas. The area of each suitability level was calculated using spatial statistics. Polygons representing the most suitable sites were further analyzed in terms of area perimeter ratio in order to investigate the most suitable areas in terms of shape regularity. The leading four polygons considered were marked A, B, C, D respectively in the final map. This study showed that suitable areas for solid waste landfills were limited and scattered in the study area.

ART Based Reliable Method for Prediction of Agricultural Land Changes Using Remote Sensing

This paper focuses on prediction of change in agricultural lands by using ART2 algorithm. The existing method used ENVI and ARCGIS software to predict the changes in land, which showed less accuracy due to human errors. To overcome this user friendly GUI based ART2 algorithm has been developed in java to obtain more accuracy in prediction of changes in land. The input is satellite temporal images of the years 1990 and 2014. By using the ART2 algorithm, the input images of the years 1990 and 2014 are classified, where the features are identified to form cluster. The clustered image is given as input and pixel to pixel comparison method in ART2 is implemented in java, for detecting the changes in agricultural lands. The comparison results of ENVI and ARCGIS and GUI based ART2 with in situ data show that the prediction of changes in agricultural land is more accurate in the case of GUI based ART2 implementation.

Study of Forest Cover Change Dynamics between 2000 and 2015 in the Ikongo District of Madagascar Using Multi-Temporal Landsat Satellite Images

Satellite images are considered reliable data that preserve land cover information. In the field of remote sensing, these images allow relevant analyses of changes in space over time through the use of computer tools. In this study, we have applied the “discriminant” change detection algorithm. In this, we have verified its effectiveness in multi-temporal studies. Also, we have determined the change in forest dynamics in the Ikongo district of Madagascar between 2000 and 2015. During the treatments, we have used the Landsat TM satellite images for the years 2000, 2005 and 2010 as well as ETM+ for 2015. Thus, analyses carried out have allowed us to note that between 2000-2005, 1.4% of natural forest disappeared. And, between 2005-2010, forests degradation was 1.8%. Also, between 2010-2015, about 0.5% of the natural forest conserved in 2010 disappeared. Furthermore, we have found that the discriminant algorithm is considerably efficient in terms of monitoring the dynamics of forest cover change.