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.

Comparison between TVDI and CWSI for drought monitoring in the Guanzhong Plain,China

Temperature vegetation dryness index （TVDI） and crop water stress index （CWSI） are two commonly used remote sens- ing-based agricultural drought indicators. This study explored the applicability of monthly moderate resolution imaging spectroradiometer （MODIS） normalized difference vegetation index （NDVI） and land surface temperature （LST） data for agricultural drought monitoring in the Guanzhong Plain, China in 2003. The data were processed using TVDI, calculated by parameterizing the relationship between the MODIS NDVI and LST data. We compared the effectiveness of TVDI against CWSI, derived from the MOD16 products, for drought monitoring. In addition, the surface soil moisture and monthly pre- cipitation were collected and used for verification of the results. Results from the study showed that： （1） drought conditions measured by TVDI and CWSI had a number of similarities, which indicated that both CWSI and TVDI can be used for drought monitoring, although they had some discrepancies in the spatiotemporal characteristics of drought intensity of this region; and （2） both standardized precipitation index （SPI） and SM contents at the depth of 10 and 20 cm had better correlations to CWSI than to TVDI, indicating that there were more statistically significant relationships between CWSI and SPI/SM, and that CWSI is a more reliable indicator for assessing and monitoring droughts in this region.

Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer

Flood events and their impact on crops are extremely significant scientific research issues; however, flood monitoring is an exceedingly complicated process. Flood damages on crops are directly related to yield change, which requires accurate assessment to quantify the damages. Various remote sensing products and indices have been used in the past for this purpose. This paper utilizes the moderate resolution imaging spectroradiometer （MODIS） weekly normalized difference vegetation index （NDVI） product to detect and further quantify flood damages on corn within the major corn producing states in the Midwest region of the US. County-level analyses were performed by taking weighted average of all pure corn pixels （〉90%） masked by the United States Department of Agriculture （USDA） Cropland Data Layer （CDL）. The NDVI-based time-series difference between flood years and normal year （median of years 2000-2014） was used to detect flood occur- rences. To further measure the impact of the flood on corn yield, regression analysis between change in NDVI and change in corn yield as independent and dependent variables respectively was performed for 30 different flooding events within growing seasons of the corn. With the R2 value of 0.85, the model indicates statistically significant linear relation between the NDVI and corn yield. Testing the predictability of the model with 10 new cases, the average relative error of the model was only 4.47%. Furthermore, small error （4.8%） of leave-one-out cross validation （LOOCV） along with smaller statistical error indicators （root mean square error （RMSE）, mean absolute error （MAE）, and mean bias error （MBE））, further validated the accuracy of the model. Utilizing the linear regression approach, change in NDVI during the growing season of corn appeared to be a good indicator to quantify the yield loss due to flood. Additionally, with the 250 m MODIS-based NDVI, these yield losses can be estimated up to field level.

Developing crop specific area frame stratifications based on geospatial crop frequency and cultivation data layers

Area Sampling Frames （ASFs） are the basis of many statistical programs around the world. To improve the accuracy, objectivity and efficiency of crop survey estimates, an automated stratification method based on geospatial crop planting frequency and cultivation data is proposed. This paper investigates using 2008-2013 geospatial corn, soybean and wheat planting frequency data layers to create three corresponding single crop specific and one multi-crop specific South Dakota （SD） U.S. ASF stratifications. Corn, soybeans and wheat are three major crops in South Dakota. The crop specific ASF stratifications are developed based on crop frequency statistics derived at the primary sampling unit （PSU） level based on the Crop Frequency Data Layers. The SD corn, soybean and wheat mean planting frequency strata of the single crop stratifications are substratified by percent cultivation based on the 2013 Cultivation Layer. The three newly derived ASF stratifications provide more crop specific information when compared to the current National Agricultural Statistics Service （NASS） ASF based on percent cultivation alone. Further, a multi-crop stratification is developed based on the individual corn, soybean and wheat planting frequency data layers. It is observed that all four crop frequency based ASF stratifications consistently predict corn, soybean and wheat planting patterns well as verified by the 2014 Farm Service Agency （FSA） Common Land Unit （CLU） and 578 administrative data. This demonstrates that the new stratifications based on crop planting frequency and cultivation are crop type independent and applicable to all major crops. Further, these results indicate that the new crop specific ASF stratifications have great potential to improve ASF accuracy, efficiency and crop estimates.

RF-CLASS： A remote-sensing-based flood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making

Floods often cause significant crop loss in the United States. Timely and objective information on flood-related crop loss, such as flooded acreage and degree of crop damage, is very important for crop monitoring and risk management in ag- ricultural and disaster-related decision-making at many concerned agencies. Currently concerned agencies mostly rely on field surveys to obtain crop loss information and compensate farmers＇ loss claim. Such methods are expensive, labor intensive, and time consumptive, especially for a large flood that affects a large geographic area. The results from such methods suffer from inaccuracy, subjectiveness, untimeliness, and lack of reproducibility. Recent studies have demonstrated that Earth observation （EO） data could be used in post-flood crop loss assessment for a large geographic area objectively, timely, accurately, and cost effectively. However, there is no operational decision support system, which employs such EO-based data and algorithms for operational flood-related crop decision-making. This paper describes the development of an EO-based flood crop loss assessment cyber-service system, RF-CLASS, for supporting flood-related crop statistics and insurance decision-making. Based on the service-orientated architecture, RF-CLASS has been implemented with open interoperability specifications to facilitate the interoperability with EO data systems, particularly the National Aeronautics and Space Administration （NASA） Earth Observing System Data and Information System （EOSDIS）, for automatically fetching the input data from the data systems. Validated EO algorithms have been implemented as web services in the system to operationally produce a set of flood-related products from EO data, such as flood frequency, flooded acreage, and degree of crop damage, for supporting decision-making in flood statistics and flood crop insurance policy. The system leverages recent advances in the remote sensing-based flood monitoring and assessment, the near-real-time availability of EO data, the service-oriented architecture, geospatial interoperability standards, and the standard-based geospatial web service technology. The prototypical system has automatically generated the flood crop loss products and demonstrated the feasibility of using such products to improve the agricultural decision-making. Evaluation of system by the end-user agencies indicates that significant improvement on flood-related crop decision-making has been achieved with the system.

Remote sensing for agricultural applications

Agricultural geospatial information is critical for agricultural policy formulation and decision making, land use monitoring, agricultural sustainability, crop acreage and yield estimation, disaster assessment, bioenergy crop inventory, food security policy, environmental assessment, carbon accounting, and other research topics that are of vital importance to agricul- ture and economy. Remote sensing technology enables us to collect, process, and analyze remotely sensed data and to retrieve, synthesize, visualize valuable geospatial information for agriculture uses. Specifically, remote sensing technology empowers capability for large scale field level or regional assessment and monitoring of crop land cover,

A capability matching and ontology reasoning method for high precision OGC web service discovery

Finding the right spatially aware web service in a heterogeneous distributed environment using criteria such as service type,version,time,space,and scale has become a challenge in the integration of geospatial information services.A new method for retrieving Open Geospatial Consortium(OGC)Web Service(OWS)that deals with this challenge using page crawling,link detection,service capability matching,and ontology reasoning,is described in this paper.Its major components are distributed OWS,the OWS search engine,the OWS ontology generator,the ontology-based OWS catalog service,and the ontology-based multi-protocol OWS client.Experimental results show that the execution time of this proposed method equals only 0.26 of that of Nutch’s method.In addition,the precision is much higher.Moreover,this proposed method can carry out complex OWS reasoning-based queries.It is being used successfully for the Antarctica multi-protocol OWS portal of the Geo-Information Web Service Portal of the Polar.

Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index(DVDI)

Accurate crop-specific damage assessment immediately after flood events is crucial for grain pricing,food policy,and agricultural trade.The main goal of this research is to estimate the crop-specific damage that occurs immediately after flood events by using a newly developed Disaster Vegetation Damage Index(DVDI).By incorporating the DVDI along with information on crop types and flood inundation extents,this research assessed crop damage for three case-study events:Iowa Severe Storms and Flooding(DR 4386),Nebraska Severe Storms and Flooding(DR 4387),and Texas Severe Storms and Flooding(DR 4272).Crop damage is assessed on a qualitative scale and reported at the county level for the selected flood cases in Iowa,Nebraska,and Texas.More than half of flooded corn has experienced no damage,whereas 60%of affected soybean has a higher degree of loss in most of the selected counties in Iowa.Similarly,a total of 350 ha of soybean has moderate to severe damage whereas corn has a negligible impact in Cuming,which is the most affected county in Nebraska.A total of 454 ha of corn are severely damaged in Anderson County,Texas.More than 200 ha of alfalfa have moderate to severe damage in Navarro County,Texas.The results of damage assessment are validated through the NDVI profile and yield loss in percentage.A linear relation is found between DVDI values and crop yield loss.An R2 value of 0.54 indicates the potentiality of DVDI for rapid crop damage estimation.The results also indicate the association between DVDI class and crop yield loss.

Geoscience model service integrated workflow for rainstorm waterlogging analysis

This paper proposed a geoscience model service integrated workflowbased rainstorm waterlogging analysis method to overcome the defects of conventional waterlogging analysis systems.In this research,we studied a general OGC WPS service invoking strategy,an automatic asynchronous invoking mechanism of WPS services in the BPEL workflow,and a distributed waterlogging analysis services integrated workflow to realize the reconstruction of a waterlogging analysis model based on the proposed method.The proposed method can make use of the flexible adjustment capability of the workflow and not only overcomes the inherent defects of conventional geoscience analysis methods but also realizes the integration and calculation of distributed geospatial data,models and computing resources automatically.The method has better construction convenience,execution reliability,extensibility and intelligence potential than a conventional method and has important value for dealing with more natural disasters and environmental challenges.

Phenological metrics-based crop classification using HJ-1 CCD images and Landsat 8 imagery

Crop type data are an important piece of information for many applications in agriculture.Extracting crop type using remote sensing is not easy because multiple crops are usually planted into small parcels with limited availability of satellite images due to weather conditions.In this research,we aim at producing crop maps for areas with abundant rainfall and small-sized parcels by making full use of Landsat 8 and HJ-1 charge-coupled device(CCD)data.We masked out non-vegetation areas by using Landsat 8 images and then extracted a crop map from a longterm time-series of HJ-1 CCD satellite images acquired at 30-m spatial resolution and two-day temporal resolution.To increase accuracy,four key phenological metrics of crops were extracted from time-series Normalized Difference Vegetation Index curves plotted from the HJ-1 CCD images.These phenological metrics were used to further identify each of the crop types with less,but easier to access,ancillary field survey data.We used crop area data from the Jingzhou statistical yearbook and 5.8-m spatial resolution ZY-3 satellite images to perform an accuracy assessment.The results show that our classification accuracy was 92%when compared with the highly accurate but limited ZY-3 images and matched up to 80%to the statistical crop areas.

Metadata requirements analysis for the emerging Sensor Web

The Sensor Web has emerged from Earth Science research with the development of Web technology,to achieve process automation,sensor interoperation,and service synergy.These promises require the discovery of the right sensor at the right time and the right location with the right quality.Metadata,for sensor,platform,and data,are crucial for achieving such goals.However,analysis and practical use of these metadata reveals that the metadata and their associations are not applicable or suitable for the Sensor Web.The shortfalls are(1)the nonstandard metadata expression language;(2)the missing link between sensor and domain knowledge;(3)the insufficiency in the information for geographic locating and sensor tasking;and(4)the enhanced requirements on the quality,security,and ownership of both sensors and their sensed data.This paper reviews the current standards that have metadata components for the sensor and its platform,especially those from ISO TC211,Open Geospatial Consortium Inc.,and The National Aeronautics and Space Administration Global Change Master Directory.A recommendation on metadata that meets the requirement of crossmission sensor discovery in a pervasive Web environment is derived from them.The recommendation addresses issues on language formalization,sensor geolocation,semantics,quality,and accessibility.Roles of the emerging semantic Web technology for enabling robust discovery of sensor are discussed.