Modeling urban redevelopment:A novel approach using time-series remote sensing data and machine learning

Accurate mapping and timely monitoring of urban redevelopment are pivotal for urban studies and decisionmakers to foster sustainable urban development.Traditional mapping methods heavily depend on field surveys and subjective questionnaires,yielding less objective,reliable,and timely data.Recent advancements in Geographic Information Systems(GIS)and remote-sensing technologies have improved the identification and mapping of urban redevelopment through quantitative analysis using satellite-based observations.Nonetheless,challenges persist,particularly concerning accuracy and significant temporal delays.This study introduces a novel approach to modeling urban redevelopment,leveraging machine learning algorithms and remote-sensing data.This methodology can facilitate the accurate and timely identification of urban redevelopment activities.The study’s machine learning model can analyze time-series remote-sensing data to identify spatio-temporal and spectral patterns related to urban redevelopment.The model is thoroughly evaluated,and the results indicate that it can accurately capture the time-series patterns of urban redevelopment.This research’s findings are useful for evaluating urban demographic and economic changes,informing policymaking and urban planning,and contributing to sustainable urban development.The model can also serve as a foundation for future research on early-stage urban redevelopment detection and evaluation of the causes and impacts of urban redevelopment.

The first quantitative remote sensing of ocean internal waves by Chinese GF-3 SAR satellite

Quantitative analysis and retrieval is given by the State Key Laboratory of Satellite Ocean Environment Dynamics（SOED）,Second Institute of Oceanography（SIO）,State Oceanic Administration（SOA）,China,from the first batch of GF-3 synthetic aperture radar（SAR）data with ocean internal wave features in the Yellow Sea.

Separate Analysis of Remote Sensing Information of Structures of Different Geological Periods and Quantitative Study of Corresponding Tectonic Stress Fields

The structural feature shown on a remote sensing image is a synthetic result ofcombination of the deformations produced during the entire geological history of an area.Therefore, the respective tectonic stress field of each of the different stages in the complexdeformation of an area can be reconstructed in three steps: (1) geological structures formed atdifferent times are distinguished in remote sensing image interpretation; (2) structuraldeformation fields at different stages are determined by analyzing relationships betweenmicrostructures (joints and fractures) and the related structures (folds and faults); and (3)tectonic stress fields at different stages are respectively recovered through a study of the featuresof structural deformation fields in different periods. Circular structures and related circlular and radial joints are correlated in space to con-cealed structural rises. The authors propose a new method for establishing a natural model ofthe concealed structural rises and calculating the tectonic stress field by using quantitative dataof the remote sensing information of circular structures and related linear structures.

Primary Quantitative Study on Earthquake Damage Extracted from Remote Sensing Imagery——A Case Study of Seismic Damage in the Urban Area of Dujiangyan due to the M_S8.0 Wenchuan Earthquake

The fast developing remote sensing techniques play an increasingly important role in earthquake emergency response, disaster survey and loss estimation. As there is a lack of quantitative studies on seismic damage based on remote sensing, its practicality in seismic disaster management has usually been questioned. The paper introduces the essential quantitative study idea, the concept of the remote sensing seismic damage index (DRS_I RS) and analysis models, demonstrates the seismic damage indices (DG_IC) of buildings obtained from ground surveying and its quantitative relation to DRS_I RS in Dujiangyan city, Sichuan Province, which was destroyed by the 2008 Wenchuan earthquake with M_S8.0. The primary results show that an obvious relationship exists between the DRS_I RS of buildings obtained from the high resolution satellite or aerial remote sensing images and DG_I C or the building collapse ratio obtained through ground survey, which suggests that the quantitative study on seismic damage based on remote sensing will provide an effective method for seismic damage survey and loss estimation.

Application of High-Resolution Remote Sensing Technology in Quantitative Study on Coseismic Surface Rupture Zones: An Example of the 2008 M_w7.2 Yutian Earthquake

Objective Nowadays, high-resolution remote sensing technology has brought new changes to surveys of earthquakes, and the quantitative study of seismic faults based on this technology has become a trend in the world(Barzegari et al., 2017). An Mw 7.2 earthquake occurred in Yutian of Xinjiang on the western end of the Altyn Tagh fault on March 21 st, 2008. It is difficult to access this depopulated zone because of the high altitude and only 1–2 months of snowmelt. This study utilized high-resolution

Research advances of SAR remote sensing for agriculture applications: A review

Synthetic aperture radar(SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions. SAR is sensitive to the geometrical structures and dielectric properties of the targets and has a certain penetration ability to some agricultural targets. The capabilities of SAR for agriculture applications can be organized into three main categories: crop identification and crop planting area statistics, crop and cropland parameter extraction, and crop yield estimation. According to the above concepts, this paper systematically analyses the recent progresses, existing problems and future directions in SAR agricultural remote sensing. In recent years, with the remarkable progresses in SAR remote sensing systems, the available SAR data sources have been greatly enriched. The accuracies of the crop classification and parameter extraction by SAR data have been improved progressively. But the development of modern agriculture has put forwarded higher requirements for SAR remote sensing. For instance, the spatial resolution and revisiting cycle of the SAR sensors, the accuracy of crop classification, the whole phenological period monitoring of crop growth status, the soil moisture inversion under the condition of high vegetation coverage, the integrations of SAR remote sensing retrieval information with hydrological models and/or crop growth models, and so on, still need to be improved. In the future, the joint use of optical and SAR remote sensing data, the application of multi-band multi-dimensional SAR, the precise and high efficient modeling of electromagnetic scattering and parameter extraction of crop and farmland composite scene, the development of light and small SAR systems like those onboard unmanned aerial vehicles and their applications will be active research areas in agriculture remote sensing. This paper concludes that SAR remote sensing has great potential and will play a more significant role in the various fields of agricultural remote sensing.

Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data

Coastal depth is an important research focus of coastal waters and is also a key factor in coastal environment. Dongluo Island in South China Sea was taken as a typical study area. The band ratio model was established by using measured points and three multispectral images of Landsat-8, SPOT-6(Systeme Probatoire d'Observation de la Terre, No.6) and WorldView-2. The band ratio model with the highest accuracy is selected for the depth inversion respectively. The results show that the accuracy of SPOT-6 image is the highest in the inversion of coastal depth. Meanwhile, analyzing the error of inversion from different depth ranges, the accuracy of the inversion is lower in the range of 0–5 m because of the influence of human activities. The inversion accuracy of 5–10 m is the highest, and the inversion error increases with the increase of water depth in the range of 5–20 m for the three kinds of satellite images. There is no linear relationship between the accuracy of remote sensing water depth inversion and spatial resolution of remote sensing data, and it is affected by performance and parameters of sensor. It is necessary to strengthen the research of remote sensor in order to further improve the accuracy of inversion.

Accurate Quantification of Grassland Cover Density in an Alpine Meadow Soil Based on Remote Sensing and GPS

The principles of remotely estimating grassland cover density in an alpine meadow soil from space lie in the synchronous collection of in situ samples with the satellite pass and statistically linking these cover densities to their image properties according to their geographic coordinates. The principles and procedures for quantifying grassland cover density from satellite image data were presented with an example from Qinghai Lake, China demonstrating how quantification could be made more accurate through the integrated use of remote sensing and global positioning systems (GPS). An empirical model was applied to an entire satellite image to convert pixel values into ground cover density. Satellite data based on 68 field samples was used to produce a map of ten cover densities. After calibration a strong linear regression relationship (r2 = 0.745) between pixel values on the satellite image and in situ measured grassland cover density was established with an 89% accuracy level. However, to minimize positional uncertainty of field samples, integrated use of hyperspatial satellite data and GPS could be utilized. This integration could reduce disparity in ground and space sampling intervals, and improve future quantification accuracy even more.

Soil Moisture Retrieval Quantitatively with Remotely Sensed Data and Its Crucial Factors Analysis

The Ts/NDVI method was adopted to retrieve soil moisture with multi-temporal and multi-sensor remotely sensed data f ETM+ and ASTER in study area. The retrieved soil moisture maps were consistent with the soil type and vegetation, which were also the two main factors determining the distribution of soil moisture.

Estimation and verification of green tide biomass based on UAV remote sensing

Since 2007,the Yellow Sea green tide has broken out every summer,causing great harm to the environment and society.Although satellite remote sensing(RS)has been used in biomass research,there are several shortcomings,such as mixed pixels,atmospheric interference,and difficult field validation.The biomass of green tide has been lacking a high-precision estimation method.In this study,high-resolution unmanned aerial vehicle(UAV)RS was used to quantitatively map the biomass of green tides.By utilizing experimental data from previous studies,a robust relationship was established to link biomass to the red-green-blue floating algae index(RGB-FAI).Then,the lab-based model for green tide biomass from visible images taken by the UAV camera was developed and validated by field measurements.Re sults show that the accurate and cost-effective method is able to estimate the green tide biomass and its changes in given local waters of the near and far seas.The study provided an effective complement to the traditional satellite RS,as well as high-precision quantitative techniques for decision-making in disaster management.

Monitoring of winter wheat distribution and phenological phases based on MODIS time-series： A case study in the Yellow River Delta, China

Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in the Yellow River Delta（YRD） region using moderate resolution imaging spectroradiometer（MODIS） time-series data. The normalized difference vegetation index（NDVI） was obtained by calculating the surface reflectance in red and infrared. We used the Savitzky-Golay filter to smooth time series NDVI curves. We adopted a two-step classification to identify winter wheat. The first step was designed to mask out non-vegetation classes, and the second step aimed to identify winter wheat from other vegetation based on its phenological features. We used the double Gaussian model and the maximum curvature method to extract phenology. Due to the characteristics of the time-series profiles for winter wheat, a double Gaussian function method was selected to fit the temporal profile. A maximum curvature method was performed to extract phenological phases. Phenological phases such as the green-up, heading and harvesting phases were detected when the NDVI curvature exhibited local maximum values. The extracted phenological dates then were validated with records of the ground observations. The spatial patterns of phenological phases were investigated. This study concluded that, for winter wheat, the accuracy of classification is 87.07%, and the accuracy of planting acreage is 90.09%. The phenological result was comparable to the ground observation at the municipal level. The average green-up date for the whole region occurred on March 5, the average heading date occurred on May 9, and the average harvesting date occurred on June 5. The spatial distribution of the phenology for winter wheat showed a significant gradual delay from the southwest to the northeast. This study demonstrates the effectiveness of our proposed method for winter wheat classification and phenology detection.

Detecting winter canola(Brassica napus) phenological stages using an improved shape-model method based on time-series UAV spectral data

Accurate information about phenological stages is essential for canola field management practices such as irrigation, fertilization, and harvesting. Previous studies in canola phenology monitoring focused mainly on the flowering stage, using its apparent structure features and colors. Additional phenological stages have been largely overlooked. The objective of this study was to improve a shape-model method(SMM) for extracting winter canola phenological stages from time-series top-of-canopy reflectance images collected by an unmanned aerial vehicle(UAV). The transformation equation of the SMM was refined to account for the multi-peak features of the temporal dynamics of three vegetation indices(VIs)(NDVI, EVI, and CI). An experiment with various seeding scenarios was conducted, including four different seeding dates and three seeding densities. Three mathematical functions: asymmetric Gaussian function(AGF), Fourier function, and double logistic function, were employed to fit timeseries vegetation indices to extract information about phenological stages. The refined SMM effectively estimated the phenological stages of canola, with a minimum root mean square error(RMSE) of 3.7 days for all phenological stages. The AGF function provided the best fitting performance, as it captured multiple peaks in the growth dynamics characteristics for all seeding date scenarios using four scaling parameters. For the three selected VIs, CIred-edgeachieved the greatest accuracy in estimating the phenological stage dates. This study demonstrates the high potential of the refined SMM for estimating winter canola phenology.

Principles and methods for the validation of quantitative remote sensing products

We first discuss the relativity of "true value and homogeneity" for quantitative remote sensing products (QRSPs), and then propose the definitions of "eigenaccuracy" and "eigenhomogeneity" under practical conditions. The eigenaccuracy and eigenhomogeneity for land surface crucial parameters such as albedo, leaf area index (LAI), and surface temperature are analyzed based on a series of experiments. Secondly, we point out the differences and similarities between the scale-free phenomena of the QRSPs and the measurements of the coastline length (1-dimensional) and the curved surface area (2-dimensional). An information fractal algorithm for the QRSPs is presented. In a case study for the LAI, when the fractal dimension is 2.16, the ratio of the LAI retrieval values obtained respectively from remote sensing data of 30 m and 6 km pixel resolution can actually reach as high as 2.86 for the same 6 km pixel using the same retrieval model. Finally, we propose an operational validation method "one test and two matches" and multipoint observation when the real situation does not allow carrying out scanning measurement without gap and overlap on the ground surface.

Determination of regional distribution of crop transpiration and soil water use efficiency using quantitative remote sensing data through inversion

A two-layer model used to get the estimated values of crop transpiration by inversion using remote sensing data, which has been proved effective at some agricultural-ecological sta-tions, is first discussed. An important part of it is the temperature separation model (in which thesurface temperature in a mixed pixel is separated into soil surface temperature and crop canopysurface temperature) on the basis of bi-temporal radiometric temperature in a mixed pixel and its thermal inertia. To improve the inversion, the authors put forward some new algorithms, including an algorithm for the estimation of regional emissivities, a static feedback algorithm using surfacetemperature for the extension of air temperature at ecological stations to the region surroundingthem and a spatial extension algorithm for calculating the wind speed 2 m above the ground withsurface roughness and radiometric temperature. Finally, regional distributions of crop transpiration (CT) and soil water use efficiency (SWUE) in North China were calculated pixel by pixel usingNOAA-AVHRR data and surface measurements and calibrations. The results provide a way toassess the effects of various agricultural practices on SWUE by using remote sensing data inNorth China in spring.

从单星观测到体系协同:风云卫星智慧协同观测体系的技术特征与发展展望

风云卫星是我国民用遥感卫星中应用范围最广泛、成效最为显著的卫星系列之一,在推动国民经济发展和加强装备自主建设等方面发挥了重要作用。面向气象高质量发展、防灾减灾、应对气候变化、生态文明建设和国家安全等多重挑战,风云卫星将坚持创新驱动发展战略,以体系化、智能化为主要发展方向,突破平台和载荷关键技术,构建智慧协同观测体系。本文探讨了风云卫星智慧协同观测体系在气象综合观测效能提升、体系化智能化网络赋能及全方位环境保障等方面的必要性,在分析国内外气象卫星发展历程的基础上,提出了新一代风云气象卫星构建智慧协同观测体系的建设思路和初步方案,并对体系构建中的关键技术进行了分析。最后,展望了风云卫星智慧协同观测体系的应用前景。

基于水热平衡的分布式水文模型研究与应用

分布式水文模型在水利部“四预”建设中发挥了至关重要的作用,但无径流资料区(简称无资料区)分布式水文模拟技术仍需进一步完善。从水热循环产汇流理论、模型构建与求解、模型参数率定三个方面入手,改进传统分布式水文模型。提出了耦合热量平衡的流域产汇流理论,揭示了水量热量动力学过程耦合机理和水热循环通量量化关系。构建了系统完整的物理机制水热平衡分布式水文模型及其数值求解方法,实现了流域地表温度和水位的分布式数值模拟。明确了模型结构和参数的物理意义,降低了需要率定的参数个数。将参数率定目标由径流量转换为地表温度和水位,综合利用遥感反演和台站观测资料率定模型参数。将研发的模型在江西赣江流域进行应用,结果表明,构建的模型具有较强的物理机制,数值求解方法精度高、稳定性好,实现了无资料区参数率定和水文模拟,为无资料区分布式水文模型研发与应用提供了新思路。

基于国产资源一号02D卫星和机器学习算法的钦州湾滨海土壤盐分反演

以钦州湾滨海地区作为研究区,基于国产资源一号02D(ZY1-02D)多光谱卫星提取的相关特征参数,在Ada Booost、Light GBM、XGBoost、RFR以及Cat Boost五种不同机器学习算法的支持下,设置了5种不同的输入变量组合,并基于决定系数(R^(2))和均方根误差(RMSE)对不同模型的性能进行了评价.结果显示:研究区实测土壤盐分含量范围为0.740~10.352 g/kg,均值为1.739 g/kg;Cat Boost相较于Ada Booost、Light GBM、XGBoost、RFR有更好的预测性能,Cat Boost结合全变量组在预测阶段取得了最高精度(R^(2)=0.8317,RMSE=0.3957 g/kg);在全变量组中,纹理特征中的均值对土壤盐分含量最为敏感,贡献度最高;研究区土壤盐分含量预测值为0~8.784g/kg,均值为2.478g/kg,轻度盐渍土分布广泛,主要集中分布在研究区西部,在东部地区分布较零散.国产资源卫星遥感数据结合Cat Boost模型在钦州湾滨海土壤盐分反演中表现出较好的性能,可为大规模估算土壤盐分含量提供一种新的方法和思路.

矿山生态环境定量遥感监测与智能分析系统设计与实现

矿山生态环境监测与治理是国家生态文明建设和“双碳”目标下的重中之重,其信息化、智能化建设在新一代信息技术革命的助推下成为数字中国建设的重要一环,也是当下时代发展的必然趋势。然而,现有矿山生态环境监测系统仍然停留在单一专题、要素不全、基础量测、本地管理的初级阶段,无法满足现实环境中对矿山生态环境多要素、长时序、高频次监测与分析的需求。基于此,提出一种B/S架构下的矿山生态环境定量遥感监测与智能分析系统——矿山生态天眼,并详细介绍了其研发需求、技术架构、关键技术及核心功能。系统依托卫星遥感技术及其他监测手段,获取并聚合不同来源、信息丰富的矿山生态大数据,形成矿山分布一张图和数据资源服务;进而基于定量遥感反演矿山生态环境各生态参数,形成一套长时序、多要素的矿山生态监测产品,涵盖人类活动、自然地理条件和“植−土−水−气”各生态要素;在此基础上,系统提供GIS时空分析、统计分析及综合定量评价等工具集,分别实现对矿区土地利用、归一化植被指数(NDVI)等参数伴随采矿活动在空间上的变化监测,对土壤含水量、水体悬浮物浓度等生态要素历史统计值在不同时空位置和区域下的查询与可视化,对顾及多项生态因子的矿山生态环境质量综合定量评价,并最终形成矿山生态扰动与治理监测报告。矿山生态天眼的应用将服务于矿山生态环境全过程变化监测、数据管理、智能分析和决策应用,有望提高矿山生态环境监测与治理的效率和智能化水平,为推动生态文明信息化建设提供参考方案。

基于多源数据的青海东部城市群气候承载力评估与时空格局分析

为探索高原地区城市化与气候变化之间的相互作用机制。从气候承载力基本内涵出发,基于气候条件与人类活动相互作用关系,同时考虑气候资源和极端气候事件双重属性,本文识别并构建了包括气候天然容量、气候极端事件压力、城市气候压力以及城市协调发展能力四方面要素的气候承载力评估指标体系,以青海东部城市群为例,应用模型探讨了气候承载力的时空分布格局及变化规律。结果显示:从时间分布上来看,2001—2020年青海东部城市群气候承载力整体呈下降趋势,且因受到气候天然容量和极端气候事件压力的影响,波动幅度较大;从空间分布上来看,青海东部城市群低和较低气候承载力区主要集中于民和—乐都—西宁—湟中沿线大片区域,中气候承载力区零散分布于湟水谷地两岸区域,高承载力和较高承载力区则主要分布于该地区的西北部和东部北地区。研究结果提示本文建立的气候承载力评估指标体系对于青海东部城市群是合理可行的,可以描述城市群城市气候承载力在时空上的格局演变过程,可作为气候变化风险预警的依据。

中国东北黑土区地表土壤含水量时空分异特征研究

土壤含水量通过影响土壤养分循环进而控制植被分布格局,量化黑土区土壤含水量空间格局对于理解土地覆被特征及生态系统小气候变化等具有重要意义。本研究以Sentinel-1A双极化合成孔径雷达影像数据为基础,辅助同时段Sentinel-2光学影像数据,基于修正水云模型对东北黑土区不同土地覆被类型下复杂地表土壤含水量进行定量反演;借助MOD11A2和MOD13A3产品计算的温度植被干旱指数,挖掘东北黑土区土壤含水量时空变化特征及其控制因素。研究结果表明,借助VV极化的修正水云模型(R^(2)=0.75,R_(e)=0.015)反演东北黑土区土壤含水量精度最高,研究区内的土壤含水量具有较强的空间分异性,整体呈现偏干旱的状态,其中旱地的干旱现象最为严重,但随着时间的变化,干旱有减轻趋势;土壤含水量变化受降雨的影响高于温度,但研究区内复杂的环境致使干旱情况并不是受单一因素控制。