Ran MengHuazhong Agricultural University | HZAU · College of Resources and Environment
Ran Meng
PhD in Remote Sensing and GIS
Associate Editor for Remote Sensing of Environment
About
46
Publications
22,703
Reads
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1,036
Citations
Citations since 2017
Introduction
I have received Ph.D. from the University of Utah in the United States . I also have three years of postdoctoral experience at the Brookhaven National Laboratory in the United States. My general research interests are using multi-scale/source remote sensing data (e.g. hyperspectral, multispectral, and Lidar) to monitor vegetation dynamics in forestry and agriculture with a focus on disturbances (e.g., wildfire, plant diseases and pests and forest logging) across spatial and temporal scales.
Additional affiliations
August 2018 - July 2022
June 2015 - July 2018
Position
- PostDoc Position
Description
- Measure and scale key plant processes and traits to link with UAS observations; Spectroscopic analysis of plant biochemical traits;Forest disturbance ecology study using airborne LiDAR, and imaging spectroscopy data
Publications
Publications (46)
Forest ecosystems in the Sierra Nevada Mountains of California are greatly influenced by wildfire as a natural disturbance, and increased fire severity and drought occurrence may alter the course of post-fire recovery in these ecosystems. We examined effects of fire severity, post-fire climate, and topographic factors on short-term (b5 years) veget...
As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30 m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scal...
Defoliation by herbivorous insects is a widespread forest disturbance driver, affecting global forest health and ecosystem dynamics. Compared with time- and labor-intensive field surveys, remote sensing provides the only realistic approach to mapping canopy defoliation by herbivorous insects over large spatial and temporal scales. However, the spec...
Compared with disturbance maps produced at annual or multi-year time steps, monthly mapping of forest harvesting can provide more temporal details needed for studying the socio-economic drivers (e.g., differentiating salvage logging and slash-and-burn from other timber harvesting) of harvesting and characterizing the associated intra-annual carbon...
The recent northward expansion of Southern Pine Beetle (SPB) outbreaks associated with warming winters has caused extensive tree mortality in temperate pine forests, significantly affecting forest dynamics, structure, and functioning. Spatially-explicit early warning and detection of SPB-induced tree mortality is critical for timely and sustainable...
Pre-harvest yield prediction of ratoon rice is critical for guiding crop interventions in precision agriculture. However, the unique agronomic practice (i.e., varied stubble height treatment) in rice ratooning could lead to inconsistent rice phenology, which had a significant impact on yield prediction of ratoon rice. Multi-temporal unmanned aerial...
Background:
Spatial-explicit weed information is critical for controlling weed infestation and reducing corn yield losses. The development of unmanned aerial vehicle (UAV) based remote sensing presents an unprecedented opportunity for efficient, timely weed mapping. Spectral, textural, and structural measurements have been used for weed mapping, w...
Ratoon rice production has been an emerging cropping system to increase food quality and productivity worldwide. Efficient monitoring of ratoon rice aboveground biomass (AGB) over large areas is valuable for precision agriculture, as AGB is closely related to crop grain yield and quality. Unmanned aerial vehicle (UAV) remote sensing has opened an u...
Environmental and green justice problems occur globally, especially in cities with unequal access to urban greenspaces. Recently, inequality in school greenspaces has drawn growing attention, given the importance of campus green environments in young students’ health and academic performance. However, the commonly used NDVI method for measuring gre...
Spatial information on urban forest canopy height (FCH) is fundamental for urban forest monitoring and assisting urban planning and management. Traditionally, ground-based canopy height measurements are time-consuming and laborious, making it challenging for periodic inventory of urban FCH at crown level. Airborne-light detection and ranging (LiDAR...
Background
Accurate mapping of tree species is highly desired in the management and research of plantation forests, whose ecosystem services are currently under threats. Time-series multispectral satellite images, e.g., from Landsat-8 (L8) and Sentinel-2 (S2), have been proven useful in mapping general forest types, yet we do not know quantitativel...
The fraction of absorbed photosynthetically active radiation (fAPAR) is a critical biophysical parameter for crop growth monitoring and yield estimation. Remote sensing provides an efficient way for measuring fAPAR over large areas, compared with the time-consuming and labor-intensive field measurements. However, the optical remote sensing signals...
Unmanned aerial vehicles-collected (UAVs) digital red–green–blue (RGB) images provided a cost-effective method for precision agriculture applications regarding yield prediction. This study aims to fully explore the potential of UAV-collected RGB images in yield prediction of winter wheat by comparing it to multi-source observations, including therm...
智慧农业代表着未来农业的发展方向，是我国农业高质量发展的重要内容。农业遥感技术在智慧农业的信息感知和定量决策中具有举足轻重的作用。文章重点梳理了农业遥感技术在作物识别与制图、病虫害监测、草害监测、作物关键理化参数反演及产量监测预测等方面的研究进展和应用，探讨了其未来发展方向和趋势。
Smart agriculture represents the development direction of agriculture in the future and is an important part of the high-quality development of agriculture in China. Agricultural remote sensing technology pl...
Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of...
Vegetation indices (VIs) data derived from satellite imageries play a vital role in land surface vegetation and dynamic monitoring. Due to the excessive noises (e.g., cloud cover, atmospheric contamination) in daily VI data, temporal compositing methods are commonly used to produce composite data to minimize the negative influence of noise over a g...
Southern Corn Rust (SCR) is one of the most destructive diseases in corn production, significantly affecting corn quality and yields globally. Field-based fast, nondestructive diagnosis of SCR is critical for smart agriculture applications to reduce pesticide use and ensure food safety. The development of spectral disease indices (SDIs), based on i...
Changes in vegetation distribution, structure, and function can modify the canopy properties of terrestrial ecosystems, with potential consequences for regional and global climate feedbacks. In the Arctic, climate is warming twice as fast as compared to the global average (known as ‘Arctic amplification’), likely having stronger impacts on arctic t...
Annual land use land cover (LULC) change information at medium spatial resolution (i.e. at 30 m) is required in numerous subjects, such as biophysical modelling, land management and global change studies. Annual LULC information, however, is usually not available at continental or national scale due to reasons such as insufficient remote sensing da...
Rapidly and accurately obtaining soil organic carbon (SOC) maps can help understand farmland soil fertility and serves managing mineral fertilizers. The pedogenesis theory provides a fundamental basis for digital soil mapping by environmental factors. However, these soil forming factors may be insensitive to soil properties in low relief areas, suc...
Accurate characterization of post-fire changes in tree mortality and carbon storage is critical in understanding and simulating future forest responses to wildfires in response to climate change. LiDAR remote sensing has been successfully used to estimate aboveground biomass (AGB) in undisturbed forests, but few studies focused on effects of burn s...
Leaf‐mass‐per‐area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial and temporal variability in LMA has been a long‐standing goal of ecological research and is an essential component for advancing Earth system models. Despite the substantial variation in L...
Understanding post-fire forest recovery is pivotal to the study of forest dynamics and global carbon cycle. Field-based studies indicated a convex response of forest recovery rate to burn severity at the individual tree level, related with fire-induced tree mortality; however, these findings were constrained in spatial/temporal extents, while not d...
Burned area and burn severity are the two most commonly used remote sensing metrics for characterizing effects of wildfire activity. Over the past decade, many new remote sensing sensors and techniques have been used to map burned areas and evaluate post-fire burn severity. Advances include the application of new change detection algorithms and new...
This study analyzed land use and land cover changes and their impact on land surface temperature using Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager and Thermal Infrared Sensor imagery of the Yellow River Delta. Six Landsat images comprising two time series were used to calculate the land surface temperature and correlated vegetat...
Leaf quantity (i.e. canopy leaf area index, LAI), quality (i.e. per-area photosynthetic capacity), and longevity all influence the photosynthetic seasonality of tropical evergreen forests. However, these components of tropical leaf phenology are poorly represented in most terrestrial biosphere models (TBMs). Here, we explored alternative options fo...
This paper analyzes the spatiotemporal dynamics of urban growth and models its spatial determinants in China through a case study of Suzhou, a rapidly industrializing and globalizing city. We conducted spatial analysis on land use data derived from multi-temporal remote sensing images of Suzhou from 1986 to 2008. Three urban growth types, namely in...
Forest recovery from past disturbance is an integral process of ecosystem carbon cycles, and remote sensing provides an effective tool for tracking forest disturbance and recovery over large areas. Although the disturbance products (tracking the conversion from forest to non-forest type) derived using the Landsat Time Series Stack-Vegetation Change...
During the 2014 Asia-Pacific Economic Cooperation (APEC) Economic Leaders' Meetings in Beijing, the Chinese government made significant efforts to clear Beijing's sky. The emission control measures were very effective and the improved air quality during the APEC Meetings was called the "APEC Blue". To monitor and estimate how these emission control...
Reprints happily provided upon request.
Defoliation by the northern tamarisk beetle (Diorhabda carinulata) causes changes in the reflectance of tamarisk (Tamarix spp.) canopies. Cross correlogram spectral matching was used to examine spectral separability of green, yellow desiccated, brown desiccated, and dead tamarisk canopy types. Using a feature...
Increased fire frequency has been shown to promote alien plant invasions in the western United States, resulting in persistent vegetation type change. Short interval fires are widely considered to be detrimental to reestablishment of shrub species in southern California chaparral, facilitating the invasion of exotic annuals and producing "type conv...
In the Sierra Nevada region of California, local ecological processes are greatly influenced by two major stand replacement disturbances: wildfire and selective logging. A study of vegetation regeneration in post-disturbance environment is essential for us to better understand and evaluate the effects of disturbances on ecological processes such as...
The spread of tamarisk (Tamarix spp., also known as saltcedar) is a significant ecological disturbance in western North America and has long been targeted for control, leading to the importation of the northern tamarisk beetle (Diorhabda carinulata) as a biological control agent. Following its initial release along the Colorado River near Moab, Uta...
The MODIS data has high temporal resolution but rather coarse spatial resolution, therefore, the MODIS-EVI data, which was more sensitive towards the phonological information than the MODIS-NDVI data, was chosen to build up the time series of studying area, in order to monitor and depict the original phonological characteristics of land cover. More...
The temporal and spatial characteristics of climate change in China during the recent 100 years were analyzed using CRU05 climate data. We studied the impacts of climate change during the recent 100 years on vegetation ecological zones in China by using Holdridge Life Zone Classification Model and Center-of-Gravity Model. It is concluded that the p...
Questions
Questions (2)
Hi there,
I am trying to convert the DN values of WorldView-3 multi-spectral to surface reflectance using the ENVI WV-3 Radiometric Calibration and FLAASH tools.
I can easily convert the DN values to radiance, but the calculated radiance values for the WV-3 coastal band seem not to be right (large spike values, please see the attachment for an example of vegetation spectra).
As a result, after conducting the FLAASH atmospheric correction, the reflectance in coastal and blue bands (Right) is also abnormal (vegetation spectra in blue band even has negative values, please see the attachment). My best guess is that the atmospheric scattering has huge impacts on the short-wavelength bands of WV-3 on this imagery, and it is too "wrong" to be corrected by WV-3 Radiometric Calibrations and FLAASH. My study site is in tropical region (Brazil) and the WV-3 acquisition time is in June. Any suggestions? Thanks a lot!
I know a lot people use a Leica Viva GS08 Plus differential GPS , but it is too expensive for us.
Projects
Projects (3)
https://www.mdpi.com/journal/remotesensing/special_issues/W13K4S3H3H
Dear Colleagues,
Remote sensing data have been successfully used to investigate various agricultural activities, such as crop type mapping, crop phenology detection, soil moisture assessment, and crop growth monitoring. From a practical point of view, agricultural management requires timely and accurate crop and soil information provided by remote sensing data within the crop-growing season (within-season). For example, it is preferable to acquire the spatial distribution of crop types in an earlier manner, which benefits timely crop management, protection, and yield forecast. However, current agricultural monitoring from remotely sensed data is often conducted after the crop-growing season. Within-season agricultural monitoring is still impeded by limitations in remote sensing data quality, monitoring algorithms, and computing platforms.
In this context, a Special Issue entitled “Within-Season Agricultural Monitoring from Remotely Sensed Data” is being planned in Remote Sensing journal. We welcome all research or review articles on agricultural monitoring as long as they focus on work carried out during the crop-growing season. In addition, methodology papers on processing within-season remote sensing data (e.g., time-series data) are also welcome. This issue has a broad range of topics, including crop monitoring (e.g., crop type classification, crop phenology detection, crop phenotyping, crop yield prediction) and agricultural condition investigations (e.g., agricultural drought, biotic/abiotic stresses). It should be noted that remotely sensed data from satellites, drones, or field instruments should be among the main data sources.
We look forward to receiving your contributions.
Dr. Ruyin Cao
Prof. Dr. Tao Cheng
Prof. Dr. Ran Meng
Dr. Andrej Halabuk
Dr. Clement E. Akumu
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
agricultural remote sensing
crop types
crop phenotype
crop yield
in-season
phenotyping
plant stress
precision agriculture
time-series data
within-season
Special Issue of Sensors "Advances in Remote Sensing and IoT Technologies in Smart Farming"
https://www.mdpi.com/journal/sensors/special_issues/YD799850K9
Dear Colleagues,
Smart farming refers to using advanced technologies to inform farming management decisions, which can increase the quantity and quality of agricultural products (grain, livestock, and dairy) while optimizing resource (e.g., water, fertilizer, and pesticide) use. Thanks to the tremendous progress of modern technologies, such as remote sensing and the Internet of Things (IoT), smart farming is becoming widespread for ensuring sustainable development in agriculture. The applications of smart farming technologies range from using IoT sensors measuring soil, plant, and environmental conditions to in-time monitoring crop stress, early prediction of crop yield, and using advanced data analytics to support site-specific agricultural management. We are pleased to announce a Special Issue entitled “Advances of Remote Sensing and IoT Technologies in Smart Farming”. This issue aims to present state-of-the-art research on the use of remote sensing and IoT techniques for crop growth monitoring, soil moisture estimation, crop stress detection, crop yield prediction, plant phenotyping, and any other related novel applications in smart farming.
Prof. Dr. Ran Meng
Dr. Yang Zhao
Dr. Lin Yuan
Dr. Bin Peng
Guest Editors
Our Unmanned Aerial System (UAS) research is enabling the Terrestrial Ecosystem Science & Technology (TEST) group to establish the foundational knowledge and technical competency necessary to enable a transformational change in the way next generation models are informed by observations and represent terrestrial ecosystem processes. Specifically, this research is developing and expanding upon critical linkages between optical and thermal properties at the leaf to canopy scales and key physiological parameters governing carbon, water, and energy fluxes in the terrestrial biosphere. Through this research the TEST group will developed the theoretical foundation and technical knowledge to provide next generation models - such as the new DOE Accelerated Climate Modeling for Energy (ACME) model - with the temporally and spatially resolved plant trait data necessary for model calibration and parameterization, as well as a means to ground truth prognostic model outputs. In order to advance the understanding and confidence in scaling remotely sensed plant physiology this project is focused on building a core competency of near surface remote sensing of plant physiological traits using off-the-shelf UASs and remote sensing instrumentation. This important research will provide new technical competence in UAS-borne remote sensing that will enable the scaling of leaf level understanding to current and future airborne and space-borne platforms.
Our UAS platform, the Osprey (FAA Aircraft Registration N640HA), shown below is based on a CarbonCore Cortex heavy-lift carbon fiber aiframe together with a sophisticated 3-D Robotics PixelHawk autopilot system. Operational control of the Osprey is managed by the open-source MissionPlanner together with the PixelHawk and our custom instrument package and controlling computer. Using this platform we are developing ecological scaling and mapping algorithms with this integrated software workflow and hardware package using off-the-shelf instrumentation including a high-resolution digital camera for Structure from Motion (three-dimensional vegetation and surface characterization), visible through near-infrared (VNIR) spectroradiometer, and a high-resolution thermal infrared (TIR) camera to capture surface and monitor vegetation functioning. Our workflow includes platform design, measurement, image processing, data management, and information extraction.
https://www.bnl.gov/envsci/test/UAS-research.php
