Integrated Remote Sensing Research Articles

The Performance of Spei Integrated Remote Sensing Data for Monitoring Agricultural Drought in the North China Plain

The Performance of Spei Integrated Remote Sensing Data for Monitoring Agricultural Drought in the North China Plain

Design of a Dual-Band Compact Integrated Remote Sensing System for Visible Light and Long-Wave Infrared

This paper presents a design of a dual-band integrated space telescope system for visible light and long-wave infrared. The system can simultaneously image the visible light band of 450–900 nm and the long-wave infrared band of 7700–10,500 nm. The dual-band integrated imaging system can freely switch the observation band to adapt to different scenes and environmental changes. The camera can also further expand its capabilities in the fields of multi-spectral observation and low-light observation by collocation with different detectors. This design is based on a coaxial reflection system, the two bands share the camera’s primary and secondary mirrors, and the separation of the two bands is achieved through a separate field of view design. After simulation, the average Modulation Transfer Function (MTF) value of the visible light band of the system at 50 lp/mm (line pairs per millimeter) reaches 0.45, and the average MTF value of the long-wave infrared band at 50 lp/mm reaches 0.36. In addition, tolerance analysis, ambient temperature analysis and transmittance analysis of the integrated system are carried out in this paper to further improve the integrated system scheme, and the feasibility of the system is further verified.

Natural and Anthropogenic Coastal Environmental Hazards: An Integrated Remote Sensing, GIS, and Geophysical-based Approach

Natural and Anthropogenic Coastal Environmental Hazards: An Integrated Remote Sensing, GIS, and Geophysical-based Approach

An Integrated Remote-Sensing and GIS Approach for Mapping Past Tin Mining Landscapes in Northwest Iberia

Northwest Iberia can be considered as one of the main areas where tin was exploited in antiquity. However, the location of ancient tin mining and metallurgy, their date and the intensity of tin production are still largely uncertain. The scale of mining activity and its socio-economical context have not been truly assessed, nor its evolution over time. With the present study, we intend to present an integrated, multiscale, multisensor and interdisciplinary methodology to tackle this problem. The integration of airborne LiDAR and historic aerial imagery has enabled us to identify and map ancient tin mining remains on the Tinto valley (Viana do Castelo, northern Portugal). The combination with historic mining documentation and literature review allowed us to confirm the impact of modern mining and define the best-preserved ancient mining areas for further archaeological research. After data processing and mapping, subsequent ground-truthing involved field survey and geological sampling that confirmed cassiterite exploitation as the key feature of the mining works. This non-invasive approach is of importance for informing future research and management of these landscapes.

Multi-Scenario Model of Plastic Waste Accumulation Potential in Indonesia Using Integrated Remote Sensing, Statistic and Socio-Demographic Data

As a significant contributor of plastic waste to the marine environment, Indonesia is striving to construct a national strategy for reducing plastic debris. Hence, the primary aim of this study is to create a model for plastic waste quantity originating from the mainland, accumulated in estuaries. This was achieved by compiling baseline data of marine plastic disposal from the mainland via comprehensive contextualisation of data generated by remote sensing technology and spatial analysis. The parameters used in this study cover plastic waste generation, land cover, population distribution, and human activity identification. These parameters were then used to generate the plastic waste disposal index; that is, the distribution of waste from the mainland, flowing through the river, and ultimately accumulating in the estuary. The plastic waste distribution is calculated based on the weighting method and overlap analysis between land and coastal areas. The results indicate that 0.6% of Indonesia, including metropolitan cities, account for the highest generation of plastic waste. Indicating of plastic releases to the ocean applied by of developing three different scenarios with the highest estimation 11.94 tonnes on a daily basis in an urban area, intended as the baseline study for setting priority zone for plastic waste management.

Mapping the Urban Population in Residential Neighborhoods by Integrating Remote Sensing and Crowdsourcing Data

Where urban dwellers live at a fine scale is essential for the planning of services and response to city emergencies. Currently, most existing population mapping approaches considered census data as observational data for specifying models. However, census data usually have low spatial resolution and low frequency. Here, we presented a framework for mapping populations in residential neighborhoods with 30 m spatial resolution with little dependency upon census data. The framework integrated remote sensing and crowdsourcing data. The observational populations and number of households at residential neighborhood scale were obtained from real-time crowdsourcing data instead of census data. We tested our framework in Beijing. We found that (1) the number of households from a real estate trade platform could be a good proxy for accurate observational population. (2) The accuracy of the mapping population in residential neighborhoods was reasonable. The mean absolute percentage error was 47.26% and the R2 was 0.78. (3) Our framework shows great potential in mapping the population in real time. Our findings expand the knowledge in estimating urban population. In addition, the proposed framework and approach provide an effective means to quantify population distribution data for cities, which is particularly important for many of the cities worldwide lacking census data at the residential neighborhood scale.

Characterisation of Uranium Mining and Tailings Pond Areas by Integrated Remote Sensing, Geophysical, Geological and Hydrogeological Methods

Remote sensing, geophysical, geological and hydrogeological methods were used to characterise the aquifer of a proposed uranium mining and tailings pond area in India. Examination of lithologs show uranium occurrence at the Eparchaean unconformity at a depth of about 50 to 60 m below the surface. Resistivity studies showed four distinct zones- top soil, highly weathered, moderately weathered/fractured rock and massive rock. Wells tap groundwater from the weathered and fractured zones. Highly weathered nature of the intrusives, dykes and faults do not allow them to act as barriers to groundwater flow. Rainfall is the major source of groundwater recharge apart from irrigation return flow. Recharge to unconfined aquifers is primarily by downward seepage of rainfall through the unsaturated zone. Groundwater flows towards southeast. Pumping tests resulted in hydraulic conductivity of this aquifer ranging from 5.4 to 19.3 m/d. Infiltration rates were obtained at 1.5 and 2.3 m/d for clayey residual unconsolidated materials and 3.7 and 5.2 m/d for sandy residual unconsolidated materials. Secondary data were used to verify the results of field tests and investigations. Results from this study were also successfully used in radionuclide and geochemical modelling of the effects of leaching from the tailings pond on groundwater quality and its impact on human health. This detailed study provides a wider understanding of the sub-surface characteristics of the proposed uranium mining and tailings pond area which offers useful information for future research and development activities.

Crop-Type Classification for Long-Term Modeling: An Integrated Remote Sensing and Machine Learning Approach

Long-term temporal and spatial information of crop type supports a wide range of applications including hydrological and climatological studies. In the U.S., yearly crop data layers (CDLs) are available starting in the early 2000s and have been developed using combined field information and sets of temporal imagery from multiple sensors. Development of long-term crop-type layers similar to CDLs is restricted by reduced accessibility to imagery and the necessary auxiliary datasets. In this study, a procedure to generate a historical crop type was developed and evaluated. Time series of Normalized Difference Vegetation Index (NDVI) datasets from Landsat 5 TM sensor for the Lower Bear Creek watershed were collected and processed. Object-based pseudo phenology curves, represented by the NDVI time series, were generated using noise filtering and dimensionality standardization procedures for the years 1985, 1990, 1995, 2000, and 2005. Classifiers were developed and evaluated using random-forest machine learning algorithms and CDL datasets as the reference. Increased generalization performance was obtained when the model was developed using multi-year datasets. This can be attributed to improved crop type representation during the training phase coupled with characterization of yearly variations due to natural (weather) and anthropogenic factors (farming management). Source of uncertainties were the presence of multiple crops within objects, phenological similarities between soybean and corn/maize, and the accuracy of CDL itself. The proposed procedure supports the development of historic crop types for long-term studies at the field scale in agricultural watersheds.

Early Identification of Serious Geological Hazards with Integrated Remote Sensing Technologies: Thoughts and Recommendations

Since 2017, many serious geological disasters have been reported, including the 2017 mountain collapse at high altitudes in Xinmo Village in Mao County, Sichuan Province, and the 2018 Baige landslide in Jinsha River, most of which are of great destructive power and hard to detect in advance. It is worth noting that although the geohazard prevention has been carried out extensively across the whole country which is supported by the state, many of these geological disasters occur outside the potential geohazard points estimated in advance. The early identification of these undetectable geohazards points remains a big challenge and a crucial task in current geohazard prevention work. In this paper, the characteristics of interferometric synthetic aperture radar (InSAR) and its inherited limitations are summarized. Based on the integrated remote sensing technologies (including optical, SAR/InSAR and LiDAR), the key observation concept with three forms morphology, deformation, situation is proposed. Through the integration of a range of remote sensing technologies, the locations of potential geohazards will be identified qualitatively, and their associated movements will be monitored quantitatively. Finally, a series of thoughts and recommendations are provided to guide our future work for the early detection of serious geological hazards.

基于多源遥感数据的农业干旱监测模型构建及应用

PDF HTML阅读 XML下载 导出引用 引用提醒 基于多源遥感数据的农业干旱监测模型构建及应用 DOI: 10.5846/stxb201711202068 作者: 作者单位: 安徽师范大学国土资源与旅游学院,安徽师范大学国土资源与旅游学院,北京师范大学减灾与应急管理研究院,北京师范大学减灾与应急管理研究院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41601023）；国家杰出青年科学基金项目（51425903）；地表过程与资源生态国家重点实验室开放基金资助项目（2017-KF-04）；安徽省自然科学基金（1808085QD117） An integrated agricultural drought monitoring model based on multi-source remote sensing data: model development and application Author: Affiliation: College of Territorial Resources and Tourism,College of Territorial Resources and Tourism,Academy of Disaster Reduction and Emergency Management，Beijing Normal University,Academy of Disaster Reduction and Emergency Management，Beijing Normal University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:干旱监测问题是干旱灾害模拟与预警及旱灾防灾减灾的关键。基于2001-2013年淮河流域40个气象站资料、28个土壤墒情站点和中分辨率成像光谱仪（MODIS）多源遥感数据，采用SEN趋势法和标准化降水蒸散指数（SPEI）等方法，综合了大气-植被-土壤相互作用等多元成因，构建了适用于淮河流域多源综合遥感干旱监测模型，探讨淮河流域干旱时空规律。研究表明：（1）基于多源数据构建综合干旱监测模型，利用土壤墒情和典型年份干旱监测对综合干旱监测模型适用性进行评价，通过了P < 0.01的显著性检验，构建的模型可综合反映出农业和气象干旱多源信息；（2）淮河流域干旱面积和干旱频率大都集中在4-5月和7-9月，9月份受旱面积最大。河南省是淮河流域受旱频率最高，其干旱面积占淮河流域多年平均干旱面积比重最大（38%），其次是安徽（22%），旱地受旱面积比重大于水田受旱面积比重；（3）淮河流域2、3和5月干旱有显著减弱趋势；而1、4和6月则有增强趋势。淮河流域小麦灌浆-成熟时期（4-6月）缺水对小麦粮食产量影响显著，综合淮河流域干旱趋势变化，需强化淮河流域4月份小麦的干旱监测与旱灾预警。 Abstract:Drought monitoring is crucial for the early warning of drought hazards. In this study, an attempt to develop an Integrated Remote Sensing Drought Monitoring Index (IRSDI) was made, based on multi-source remote sensing data, with the aim of investigating drought conditions across the Huai River basin in both space and time. The IRSDI relied on:meteorological data recorded from 2003 to 2013 at 40 stations, soil moisture data recorded at 16 stations, Moderate Resolution Imaging Spectroradiometer (MODIS) data obtained by linear trend detection, and on the Standardized Precipitation Evapotranspiration Index (SPEI). The results of this study indicate that:(1) the proposed IRSDI is able to describe and monitor drought conditions well in both space and time; (2) droughts and high drought frequency occur in the periods April-May and July-September. In particular, September is the month during which the largest area is dominated by droughts. In particular, droughts affect Henan province, which accounts for 38% of the total drainage area of the Huai River basin, followed by Anhui province, which accounts for 22% of the total drainage area of the Huai River basin; (3) droughts tend to decrease during February, March and May, whereas they increase during January, April and June. Wheat grouting and ripening occur in the period from April to June. Thus, droughts in these months can seriously affect wheat production, and a more careful allocation of water resources and irrigation management should be performed during this period. Nevertheless, spring droughts and autumn-winter droughts should receive proper attention as well, in order to manage and reduce drought-induced losses. 参考文献 相似文献 引证文献

Developing an Integrated Remote Sensing Based Biodiversity Index for Predicting Animal Species Richness

Many remote sensing metrics have been applied in large-scale animal species monitoring and conservation. However, the capabilities of these metrics have not been well compared and assessed. In this study, we investigated the correlation of 21 remote sensing metrics in three categories with the global species richness of three different animal classes using several statistical methods. As a result, we developed a new index by integrating several highly correlated metrics. Of the 21 remote sensing metrics analyzed, evapotranspiration (ET) had the greatest impact on species richness on a global scale (explained variance: 52%). The metrics with a high explained variance on the global scale were mainly in the energy/productivity category. The metrics in the texture category exhibited higher correlation with species richness at regional scales. We found that radiance and temperature had a larger impact on the distribution of bird richness, compared to their impacts on the distributions of both amphibians and mammals. Three machine learning models (i.e., support vector machine, random forests, and neural networks) were evaluated for metric integration, and the random forest model showed the best performance. Our newly developed index exhibited a 0.7 explained variance for the three animal classes’ species richness on a global scale, with an explained variance that was 20% higher than any of the univariate metrics.

Monitoring Land Cover Change Integrated Remote Sensing And GIS Techniques In Karbala Governorate

Monitoring Land Cover Change Integrated Remote Sensing And GIS Techniques In Karbala Governorate

Segmentation of Playa Geomorphological Facies in Desert Regions, Using Integrated Remote Sensing and Statistical Modeling of Soil Properties

Segmentation of Playa Geomorphological Facies in Desert Regions, Using Integrated Remote Sensing and Statistical Modeling of Soil Properties

INTEGRATED REMOTE SENSING AND GIS FOR MODELING ECONOMIC REHABILITATION DEVELOPMENT OF EX-MINE SITES

The potential environmental impacts of mining, increasing environmental legislation and public awareness have received increased attention world-wide in the last two decades. The focus of concern by the industry, environmental regulatory agencies and members of the public is the systematic rehabilitation of ex-mine sites to improve the quality at site for potential future commercial land use. The minerals extracted from these mine/quarry sites are essential in the construction, semiconductor, high-technology, ceramic and other manufacturing sectors for further industrial development. However, efficient engineering design and systematic economic evaluation of mine sites for site rehabilitation are required in maintaining the expected standards of environmental compliance. With escalating production costs and the keen competitiveness of the mining industry world-wide, the necessity to increase the efficiency in site rehabilitation is getting more prominence. A coordinated environmental protection and rehabilitation programme is essential if the environmental awareness of the community and the demands of the respective planning authorities are to be accommodated. There is thus a need to increase the base of knowledge for efficient planning in the systematic and progressive rehabilitation of current and future ex-mine sites. An efficient modeling tool is required for the systematic planning and design of potential economic land development of ex-mine sites. The applicability of Remote Sensing and Geographic Information System (GIS) technology is a useful tool to acquire spatial information for the systematic design and planning of potential development of ex-mine sites. This research was conducted to detect the trends in the suitability of land cover changes via land cover change detection of ex-mine sites and validated with reality. The findings are useful to assist in the development of a tool for efficient modeling and design of potential economic development of ex-mine sites. The aim of this research is to quantify, model and map the economic potential of the ex-mine sites for built up areas such as housing and other urban infrastructures. Land cover classes were interpreted into maps and the accuracy of the maps were validated to reference data and actual ground scenarios. The study for validation of the proposed modeling tool was carried out using the large prominent mining area in Malaysia namely the Kinta District. Results from the validation study carried out indicate that the correlation of the results obtained from this Integrated Remote Sensing and GIS tool for modeling to field data is in the range of 0.87-0.92 which is acceptable and close to reality.

Physical Flood Vulnerability Mapping Applying Geospatial Techniques in Okazaki City, Aichi Prefecture, Japan

Flooding has been increasing since 2004 in Japan due to localized heavy rainfall and geographical conditions. Determining areas vulnerable to flooding as one element of flood hazard maps related to disaster management for urban development is necessary. This research integrated Remote Sensing data, the Geography Information System (GIS) method and Analytical Hierarchy Process (AHP) calculation to determine the physical flood-vulnerable area in Okazaki City. We developed this research by applying data from the Geospatial Information Authority of Japan (GSI) to generate the slope map and drainage density; AMEDAS (Automated Meteorological Data Acquisition System) from the Japan Meteorological Agency (JMA) to generate the rainfall data; Soil map from the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) data; and Sentinel-2 imagery to generate the land cover map. We applied the AHP calculation for weighting pairwise the parameters by comparing five iterations of the normalized matrix. We utilized the spatial analysis tool in ArcGIS to run the pairwise comparison to adjudicate the distribution of flooding according to the AHP procedure. The percentage of relative weight was slope (43%), drainage density (20%), rainfall intensity (17%), then both infiltration rate and land cover (10%). The consistency value was reasonable: consistency index (CI—0.007) and consistency ratio (CR—0.6%). We generated high accuracy for flood vulnerability prediction; 0.88 for Probability of Detection (POD), 0.28 for Probability of False Detection (POFD), 0.44 for Critical Success Index (CSI), 1.9 for Bias, and 95 of Area under Curve (AUC). The flood vulnerability was matched to the flood inundation survey of Okazaki City in August 2008 and indicated an excellent Relative Operating Characteristic (ROC).

Hydrogeomorphological Study in Bamanghaty Subdivision of Mayurbhanj District, Odisha an Integrated Remote Sensing and GIS Approach

Remote Sensing (RS) and geographic information system (GIS) are now very essential tools for efficient planning and management and handling a range of data simultaneously in a time- and cost-efficient manner for targeting of groundwater, which assists in measuring, monitoring, and conserving groundwater resources. Survey of India toposheets, LISS-III and CARTOSAT DEM satellite imageries are used to prepare various thematic layers viz., geology, slope, lineament, drainage, and geomorphology, and were transformed to raster data using feature to raster conversion tool in ArcGIS spatial analysis, then we reclassify each raster map using reclassify tools. By using weight overlay analysis, each weighted thematic layer is statistically computed to get the ground water potential zones. Then, five different groundwater potential zones were identified, namely “very good”, “good”, “moderate”, “poor”, and “very poor”. The villages under poor groundwater potential zone and the villages under very good groundwater potential zone are finding out. The above study has clearly demonstrated the capabilities of Remote Sensing and GIS in demarcation of the different groundwater potential zones in hard rock terrain.

Integrated Remote Sensing and Structural Analysis Studies of Tayyib Al-Ism Area, Northwestern Arabian Shield, Saudi Arabia

Geological and structural mappings of Tayyib Al-Ism area were carried out using the rocks finite strain data, the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data and the field based observations. To analyze the finite strain in the studied rocks, the Rf /ϕ and Fry methods are applied to feldspar porphyroclasts and mafic grains from nine metavolcano-sedimentary samples (Hegaf Formation), four diorite-gabbros suite samples (Sawawin Complex), two meta-granite samples (Ifal suite) and five Zuhd alkali granite samples. The obtained data indicate traces of high to moderate level of deformation in the meta-granite and metavolcano-sedimentary rocks. The axial ratios along the XZ section range from 1.70 to 4.80 for the Rf/ϕ method and from 1.50 to 4.50 for the Fry method. A sub-vertical trend of short axes in association with sub-horizontal foliation is also observed. These informations allow us to conclude that a finite strain in the deformed granitic rocks is of the same order of magnitude as in the metavolcano-sedimentary rocks. The contacts between the metavolcano-sedimentary and granitic rocks in Tayyib al Ism area were formed during the granitic intrusions along some of the faults under brittle to semi-ductile deformation conditions. These faults have significantly influenced the geometry and style of rifting in the Red Sea during the Neogene. The finite strain was accumulated in the area during the process of deformation, which superimpose the already existed nappe structure. It indicates that the nappe contacts formed during the accumulation of finite strain. In addition to finite strain analysis, band ratio images (3/1, 5/3, 7/5) and Principal Component Analysis (PCA) technique have been used, which proved effective in mapping geological and structural features of various rock bodies exposed in the study area.

Estimation of Surface Run-off for Urban Area Using Integrated Remote Sensing and GIS Approach

Urban run-off increases significantly due to increased impervious area and reduced drainage network. Evaluation of land use in urban area plays a vital role as input to the estimation of runoff. The hydrological design standard for urban water resources planning and management is commonly based on the frequency of occurrence of heavy rainfall events. In the present study, the occurrence of most frequent heavy rainfall event is investigated for Thanjavur town, located in the State of Tamilnadu, India and used for estimation of run-off depth. The Soil Conservation Service Curve Number (SCS-CN) is used for evaluating run-off depth value for event rainfall starting from 10 mm to 400 mm. The land use detail for the study area was obtained by integration of GIS and remote sensing. The spatial variation of event rainfall is considered with certain percentage of deviation from base rainfall for each triangle area that contributes to the run-off. The results of the analysis indicate that the study area can produce the run-off volume more than that required for urban water management at an average seasonal rainfall.

An Integrated Optical Remote Sensing System for Environmental Perturbation Research

Remote sensing is the only technology that can systematically monitor physical properties of the biosphere over a vast region. However, it is still a challenge to make these measures meaningful for assessing the impacts of environmental perturbation. Here, we integrate an optical remote sensing system termed EcoiRS (Ecosystem observation by an integrated Remote Sensing system) specifically for this purpose. EcoiRS consists of three subsystems: an off-the-shelf atmospheric correction model (ACORN), a cloud/shadow removal model, and an advanced spectral mixture analysis model (AutoMCU). The core of ACORN is a set of radiative transfer codes that can be used to remove the effects of molecular/aerosol scatterings and water vapor absorption from remotely sensed data, and to convert these digital signals to surface reflectance. Shadow and cloud cover that would obscure the reflective properties of land surfaces in an image can be minimized by referring to their optical and thermal spectral profiles. AutoMCU executes iterative unmixing for each pixel using selected spectral endmembers based upon the rule of Monte Carlo simulation. The main outcomes of EcoiRS include cover fractions of green vegetation, non-photosynthetically active vegetation and bare soils, along with uncertainty measures for each pixel. The dynamics of these derived products are significant indicators for monitoring the change of states of terrestrial environments, and they can be used for investigating different environmental perturbations. Here, we demonstrate studies of implementing EcoiRS to map three major but relatively less studied cases in a western Pacific island (Taiwan): typhoons, tree diseases and alien plant invasion.

Assessment of Coastal Erosion Vulnerability around Midnapur-Balasore Coast, Eastern India using Integrated Remote Sensing and GIS Techniques

Digha coastal region in the northeastern part of the Bay of Bengal is potentially vulnerable to erosional hazard. The present study assessed the coastal erosion vulnerability along this 65 km long coastal stretch located between Rasulpur (Midnapur) and Subarnarekha (Balasore) estuarine complex, which had been subjected to anthropogenic intervention. Multi-resolution Landsat satellite imagery were used for shoreline change study from 1972 to 2010. During this period, accretion was recorded updrift of artificial structures, viz, seawall, groin, pylons and jetties; while, extensive erosion was recorded in downdrift areas of these structures. Assessment was subsequently divided into four categories ranging from “high erosion” to “accretion”. Data from several sources were compiled to map landuse and human activities in the coastal zone. This map was divided into four categories, ranging from “very high capital” to “no capital” landuse. Population density map of the surrounding coastal villages was generated using census data, and divided into four categories ranging from “high density area” to “very low density area”. Subsequently, coastal erosion vulnerability was assessed by combining coastal retreat with landuse type and population density in this study area using simple vector algebraic technique. Zones of vulnerability of different magnitude (viz., very high, high, moderate, and low) have been identified. Furthermore, calculation of “imminent collapse zone (ICZ)” shows that maximum values are around artificial structures and anthropogenic activities. The coastal erosion vulnerability map prepared from this study can be used for proper planning and management of this coastal region.