Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data

The aim of this paper is to assess the accuracy of an object-oriented classification of polarimetric Synthetic Aperture Radar (PolSAR) data to map and monitor crops using 19 RADARSAT-2 fine beam polarimetric (FQ) images of an agricultural area in North-eastern Ontario, Canada. Polarimetric images and field data were acquired during the 2011 and 2012 growing seasons. The classification and field data collection focused on the main crop types grown in the region, which include: wheat, oat, soybean, canola and forage. The polarimetric parameters were extracted with PolSAR analysis using both the Cloude–Pottier and Freeman–Durden decompositions. The object-oriented classification, with a single date of PolSAR data, was able to classify all five crop types with an accuracy of 95% and Kappa of 0.93; a 6% improvement in comparison with linear-polarization only classification. However, the time of acquisition is crucial. The larger biomass crops of canola and soybean were most accurately mapped, whereas the identification of oat and wheat were more variable. The multi-temporal data using the Cloude–Pottier decomposition parameters provided the best classification accuracy compared to the linear polarizations and the Freeman–Durden decomposition parameters. In general, the object-oriented classifications were able to accurately map crop types by reducing the noise inherent in the SAR data. Furthermore, using the crop classification maps we were able to monitor crop growth stage based on a trend analysis of the radar response. Based on field data from canola crops, there was a strong relationship between the phenological growth stage based on the BBCH scale, and the HV backscatter and entropy.     

The improvement of an object-oriented classification using multi-temporal MODIS EVI satellite data

Abstract

This paper investigates the contribution of multi-temporal enhanced vegetation index (EVI) data to the improvement of object-based classification accuracy using multi-spectral moderate resolution imaging spectral-radiometer (MODIS) imagery. In object-oriented classification, similar pixels are firstly grouped together and then classified; the produced result does not suffer the speckled appearance and closer to human vision. EVI data are from the MODIS sensor aboard Terra spacecraft. 69 EVI data (scenes) were collected during the period of three years (2001–2003) in a mountainous vegetated area. These data sets were used to study the phenology of the land cover types. Different land cover types show distinct fluctuations over time in EVI values and this information might be used to improve object-oriented land cover classification. Two experiments were carried out: one was only with single date MODIS multispectral data, and the other one including also the 69 EVI images. Eight classes were distinguished: temperate forest, tropical dry forest, grassland, irrigated agriculture, rain-fed agriculture, orchards, lava flows and human settlement. The two classifications were evaluated with independent verification data, and the results showed that with multi-temporal EVI data, the classification accuracy was improved 5.2%. Evaluated by McNemar's test, this improved was significant, with significance level p=0.01.     

Hydrogeomorphic studies in Banaskantha and Mehsana districts (Gujarat) using Landsat data

A hydrogeomorphic approach is used in analyzing hydrologic conditions in the Mehsana and Banaskantha districts of Gujarat state. Using Landsat images, it was possible to delineate geological units, hydrogeomorphic features and vegetation density levels on a regional scale. A relationship between hydrogeomorphic features and vegetation density levels along with ground based hydrologic data was established in Mehsana district and the same was extended to the adjoining Banaskantha district. The ground water potential areas identified were from alluvium and piedmont zone. On the basis of different vegetation density levels, these areas were further subdivided into three different potential zones as regards the availability of groundwater viz. good, fair and poor. The applicability of the remotely sensed data has been found quite useful in quick identification of regional hydrogeomorphic setting of the area.     

Advances in active fire detection using a multi-temporal method for next-generation geostationary satellite data

ABSTRACT

A vital component of fire detection from remote sensors is the accurate estimation of the background temperature of an area in fire's absence, assisting in identification and attribution of fire activity. New geostationary sensors increase the data available to describe background temperature in the temporal domain. Broad area methods to extract the expected diurnal cycle of a pixel using this temporally rich data have shown potential for use in fire detection. This paper describes an application of a method for priming diurnal temperature fitting of imagery from the Advanced Himawari Imager. The BAT method is used to provide training data for temperature fitting of target pixels, to which thresholds are applied to detect thermal anomalies in 4?μm imagery over part of Australia. Results show the method detects positive thermal anomalies with respect to the diurnal model in up to 99% of cases where fires are also detected by Low Earth Orbiting (LEO) satellite active fire products. In absence of LEO active fire detection, but where a burned area product recorded fire-induced change, this method also detected anomalous activity in up to 75% of cases. Potential improvements in detection time of up to 6?h over LEO products are also demonstrated.     

Land use/land cover changes near HAzira Region, Gujarat using remote sensing satellite data

Human activities have great influence on fragile coastal ecosystem. For sustainable use of coastal resources it is very important to understand land use/land cover changes and its implications on coastal systems. Remote sensing data because of its synoptic, multispectral and multi temporal nature can be a very good source for mapping, monitoring and understanding these changes. IRS LISS III sensor data were used to find out the rate of land use/land cover changes in Hazira area near Surat, Gujarat. Because of major industrial activities it has become a hot spot area which requires regular monitoring. In the present study, land cover information of the period 1970–1972 from the Survey of India topographical maps, and satellite data of the year 1989 and 1999–2002 have been used and visual analysis has been carried out to measure the land use/land cover changes. Erosion and deposition has been observed around the newly constructed jetty. Forest area and agriculture area is found to decreased, whereas built-up area has increased.     

Analysing urban dynamics using multi-temporal satellite images in the case of a mountain area,Sinaia (Romania)

The purpose of this study is to produce an analysis of the urban expansion in the case of a mountain resort in the Romanian Carpathians through the integration of different cartographic and ancillary material in the remote sensing imagery processing. The spatial pattern analysis of the changes underwent by the urban landscape was based on multi-temporal information sources, covering 28 years, which highlighted the major turning points in landscape evolution, meaning industrial development under the communist production planning and residential expansion in recent years. To fully exploit the combination of satellite image processing in IDRISI, the manual image classification and database interrogation in ArcGis, we used a uniform grid, representing a set of vector data for each year available from the Landsat image archive. The image comparison was completed by using appropriate quantitative techniques. In conclusion the urban landscape evolution was linked to the socio-economic context. At a historic scale the main phenomenon identified is the concentration of mass tourism facilities, located in contiguity to a protected area, a situation reflected in the constant fragmentation of surfaces covered with vegetation at the urban fringe. In the digital earth science, the interplay between mountain ecosystems and human activities encompasses a key role in the management of viable mountain landscapes.     

Identification of Loriya Fault,Its Reactivation Due to 2001 Bhuj Earthquake, (Using Remote Sensing Data) and Its Bearing on the Kachchh Mainland Fault,Kachchh District,Gujarat

The 2001 Bhuj earthquake (M_w 7.7), one of the most severe earthquakes in the recent history of India, reactivated various existing active faults. It is manifested in the form of coseismic ground fissures/cracks and upheaval of land in the form of bumps. Identification and reactivation of Loriya Fault is established by 1—Geomorphic changes with the help of digital imagery (LISS III images). 2—Coseismic changes through ground checks and 3—Geophysical signatures through magnetic and gravity survey. A lineament cutting the north-western part of the Pur River alluvial fan has been revealed by satellite imagery. The streams flowing along the lineament add to the evidences of a weak plane, while the occurrence of coseismic ground fissures confirms the existence of an active fault. No dip slip movement is recorded in the trenches made across the Loriya active fault while the en-echelon pattern of ground fissures suggest strike slip movement along the fault due to 2001 earthquake.     

Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data

Wetland ecosystems have experienced dramatic challenges in the past few decades due to natural and human factors. Wetland maps are essential for the conservation and management of terrestrial ecosystems. This study is to obtain an accurate wetland map using an object-based stacked generalization (Stacking) method on the basis of multi-temporal Sentinel-1 and Sentinel-2 data. Firstly, the Robust Adaptive Spatial Temporal Fusion Model (RASTFM) is used to get time series Sentinel-2 NDVI, from which the vegetation phenology variables are derived by the threshold method. Subsequently, both vertical transmit-vertical receive (VV) and vertical transmit-horizontal receive (VH) polarization backscatters (σ0 VV, σ0 VH) are obtained using the time series Sentinel-1 images. Speckle noise inherent in SAR data, resulting in over-segmentation or under-segmentation, can affect image segmentation and degrade the accuracies of wetland classification. Therefore, we segment Sentinel-2 multispectral images to delineate meaningful objects in this study. Then, in order to reduce data redundancy and computation time, we analyze the optimal feature combination using the Sentinel-2 multispectral images, Sentinel-2 NDVI time series, phenological variables and other vegetation index derived from Sentinel-2 multispectral images, as well as time series Sentinel-1 backscatters at the object level. Finally, the stacked generalization algorithm is utilized to extract the wetland information based on the optimal feature combination in the Dongting Lake wetland. The overall accuracy and Kappa coefficient of the object-based stacked generalization method are 92.46% and 0.92, which are 3.88% and 0.04 higher than that using the pixel-based method. Moreover, the object-based stacked generalization algorithm is superior to single classifiers in classifying vegetation of high heterogeneity areas.     

Wavelet-based detection of bush encroachment in a savanna using multi-temporal aerial photographs and satellite imagery

Although increased woody plant abundance has been reported in tropical savannas worldwide, techniques for detecting the direction and magnitude of change are mostly based on visual interpretation of historical aerial photography or textural analysis of multi-temporal satellite images. These techniques are prone to human error and do not permit integration of remotely sensed data from diverse sources. Here, we integrate aerial photographs with high spatial resolution satellite imagery and use a discrete wavelet transform to objectively detect the dynamics in bush encroachment at two protected Zimbabwean savanna sites. Based on the recently introduced intensity-dominant scale approach, we test the hypotheses that: (1) the encroachment of woody patches into the surrounding grassland matrix causes a shift in the dominant scale. This shift in the dominant scale can be detected using a discrete wavelet transform regardless of whether aerial photography and satellite data are used; and (2) as the woody patch size stabilises, woody cover tends to increase thereby triggering changes in intensity. The results show that at the first site where tree patches were already established (Lake Chivero Game Reserve), between 1972 and 1984 the dominant scale of woody patches initially increased from 8 m before stabilising at 16 m and 32 m between 1984 and 2012 while the intensity fluctuated during the same period. In contrast, at the second site, which was formely grass-dominated site (Kyle Game Reserve), we observed an unclear dominant scale (1972) which later becomes distinct in 1985, 1996 and 2012. Over the same period, the intensity increased. Our results imply that using our approach we can detect and quantify woody/bush patch dynamics in savanna landscapes.     

Mapping dominant vegetation communities at Meili Snow Mountain,Yunnan Province,China using satellite imagery and plant community data

Mapping dominant vegetation communities is important work for vegetation scientists. It is very difficult to map dominant vegetation communities using multispectral remote sensing data only, especially in mountain areas. However plant community data contain useful information about the relationships between plant communities and their environment. In this paper, plant community data are linked with remote sensing to map vegetation communities. The Bayesian soft classifier was used to produce posterior probability images for each class. These images were used to calculate the prior probabilities. One hundred and eighty plant plots at Meili Snow Mountain, Yunnan Province, China were used to characterize the vegetation distribution for each class along altitude gradients. Then, the frequencies were used to modify the prior probabilities of each class. After stratification in a vegetation part and a non-vegetation part, a maximum-likelihood classification with equal prior probabilities was conducted, yielding an overall accuracy of 82.1% and a kappa accuracy of 0.797. Maximum-likelihood classification with modified prior probabilities in the vegetation part, conducted with a conventional maximum-likelihood classification for the non-vegetation part, yielded an overall accuracy of 87.7%, and a kappa accuracy of 0.861.     

小卫星数据的变化监测能力分析

为了促进小卫星数据在地质灾害监测中的应用与推广,本文以监测采矿图斑变化为目标,通过对北京地区的2006年和2007年两期北京一号小卫星4m全色影像采用正射校正、配准和迭加对比的方法发现变化信息。结合1∶50 000影像图、野外调查和测量,确定变化图斑的边界、变化原因和规模,并对小卫星全色图像的变化监测能力进行分析和总结。本文通过对小卫星4m全色影像数据的分析,基本上可以概括出其变化监测能力。     

Saline degradation of Indian agricultural lands : A case study in Khambhat Taluka,Gujarat state (India), using satellite remote sensing

In certain agricultural fields of Khambhat Taluka in Gujarat State, the salinity has increased considerably rendering the land completely infertile. The occurrence of salinity in this area can be attributed partly to subsurface sea‐water ingress and partly to improper land and water management practices prior to implementation of irrigation. Landsat MSS or TM and IRS IA LISS II data was used to test the feasibility of delineating saline soils by both visual image interpretation and digital analysis. The study of saline soils using multi‐temporal Landsat images of the year 1977, 1983, and 1987, indicated an evident increase in saline areas in past few years. The Soil Brightness Index (SBI) generated from the IRS‐IA data by the application of MSS equivalent coefficients brought out different categories of soil degradation. The supervised classification scheme aided in generating various salinity levels. The analysis of the soil samples of the above area exhibited increasing values of Electrical Conductivity (ECe), and the soluble cations with increasing levels of salinity.     

Estimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data

Urbanisation generates greater population densities and an increase in anthropogenic heat generation. These factors elevate the urban–rural air temperature (T_a) difference, thus generating the Urban Heat Island (UHI) phenomenon. T_a is used in the fields of public health and epidemiology to quantify deaths attributable to heat in cities around the world: the presence of UHI can exacerbate exposure to high temperatures during summer periods, thereby increasing the risk of heat-related mortality. Measuring and monitoring the spatial patterns of T_a in urban contexts is challenging due to the lack of a good network of weather stations. This study aims to produce a parsimonious model to retrieve maximum T_a (T_max) at high spatio-temporal resolution using Earth Observation (EO) satellite data. The novelty of this work is twofold: (i) it will produce daily estimations of T_max for London at 1 km² during the summertime between 2006 and 2017 using advanced statistical techniques and satellite-derived predictors, and (ii) it will investigate for the first time the predictive power of the gradient boosting algorithm to estimate T_max for an urban area. In this work, 6 regression models were calibrated with 6 satellite products, 3 geospatial features, and 29 meteorological stations. Stepwise linear regression was applied to create 9 groups of predictors, which were trained and tested on each regression method. This study demonstrates the potential of machine learning algorithms to predict T_max: the gradient boosting model with a group of five predictors (land surface temperature, Julian day, normalised difference vegetation index, digital elevation model, solar zenith angle) was the regression model with the best performance (R² = 0.68, MAE = 1.60 °C, and RMSE = 2.03 °C). This methodological approach is capable of being replicated in other UK cities, benefiting national heat-related mortality assessments since the data (provided by NASA and the UK Met Office) and programming languages (Python) sources are free and open. This study provides a framework to produce a high spatio-temporal resolution of T_max, assisting public health researchers to improve the estimation of mortality attributable to high temperatures. In addition, the research contributes to practice and policy-making by enhancing the understanding of the locations where mortality rates may increase due to heat. Therefore, it enables a more informed decision-making process towards the prioritisation of actions to mitigate heat-related mortality amongst the vulnerable population.     

基于GF-1号卫星WFV数据的太湖水质遥感监测

为研究高分一号(GF-1)卫星数据监测太湖水质的可行性,基于新发射的GF-1卫星16 m分辨率的多光谱宽覆盖(wide field of view,WFV)相机和HJ-1A CCD数据,对太湖的叶绿素a、悬浮物、透明度和富营养化状况进行遥感监测,以评价GF-1 WFV相机的应用潜力。研究结果表明:GF-1 WFV与HJ-1A CCD数据对水质参数的反演结果具有一致性,可有效反映叶绿素a浓度、悬浮物浓度、透明度和富营养化指数的空间变化规律。其中,太湖西北部分布有少量水华蓝藻,在大面积蓝藻爆发区域附近,叶绿素a浓度明显高于其他区域的水体,平均浓度为62.46 mg·m-3;悬浮物浓度以竺山湾及西部沿岸湖区较大,沿西北向东南方向递减,平均浓度为26.07 mg·L-1;透明度整体从西北向东南递增,与悬浮物浓度的分布趋势相反,平均值为22.1 cm;富营养化指数整体从西北向东南递减,与叶绿素浓度的分布趋势相同,平均值为69.62。遥感监测指标的结果均符合常规监测规律。     

Mapping and monitoring carbon stocks with satellite observations: a comparison of methods

Mapping and monitoring carbon stocks in forested regions of the world, particularly the tropics, has attracted a great deal of attention in recent years as deforestation and forest degradation account for up to 30% of anthropogenic carbon emissions, and are now included in climate change negotiations. We review the potential for satellites to measure carbon stocks, specifically aboveground biomass (AGB), and provide an overview of a range of approaches that have been developed and used to map AGB across a diverse set of conditions and geographic areas. We provide a summary of types of remote sensing measurements relevant to mapping AGB, and assess the relative merits and limitations of each. We then provide an overview of traditional techniques of mapping AGB based on ascribing field measurements to vegetation or land cover type classes, and describe the merits and limitations of those relative to recent data mining algorithms used in the context of an approach based on direct utilization of remote sensing measurements, whether optical or lidar reflectance, or radar backscatter. We conclude that while satellite remote sensing has often been discounted as inadequate for the task, attempts to map AGB without satellite imagery are insufficient. Moreover, the direct remote sensing approach provided more coherent maps of AGB relative to traditional approaches. We demonstrate this with a case study focused on continental Africa and discuss the work in the context of reducing uncertainty for carbon monitoring and markets.     

Preparation of volume table of SAL (Shorea robusta) - an approach using satellite data

Sal (Shorea robusta) is an important forest tree species in north and north-eastern India. Large-scale plantations of this species have been raised there under taungya and coppice system of management. The conventional volume table prepared for high sal forest is referred to infer the volume of production of this species. Earlier workers have used aerial remote sensing data to develop volume tables of this species. In the present study a volume table for sal is developed based on remotely sensed satellite data using a regression technique. A two-step method was developed to estimate mean tree volume from satellite data. In step 1, mean crown diameter — an intermediate variable - was estimated from satellite data. In step 2, the estimated mean crown diameter was used to estimate the mean tree volume. Addition of age of the crop as an independent variable improved the predictive ability of the regression equation.     

Tea acreage estimation and condition assessment using satellite data in nilgiri district,tamilnadu

In the present study an attempt has been made to estimate acreage and condition of tea plantations by using satellite based digital remotely sensed data in visible, near infra-red and middle infra-red spectral regions, in the Nilgiri district of Tamilnadu state. Landsat MSS and TM data, acquired on Dec. 26, 1990 were used in the analysis, Different spectral band combinations, Landsat MSS (1234), TM (1234), TM (2345) and TM (123457) were used for identification of tea plantations. District-boundary-overlaying approach with complete enumeration of digital data was used for estimation of tea acreages. Condition assessment of tea plantations is based on the Greenness Index. Use of Landsat MSS data resulted in an underestimation of area under tea whereas the acreages estimated by using TM spectral band combinations 1234 and 2345 compared closely with the estimates of Department of Horticulture (DOH). The distribution pattern of various condition classes of tea plantations compared well with the prevailing ground conditions as observed during post-classification field survey in September 1992 in the district.