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Remote Sensing and Vegetation Mapping

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 32517

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Prof. Dr. Kenji Omasa

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Guest Editor

Faculty of Agriculture, Takasaki University of Health and Welfare, 54, Nakaorui-machi 370-0033, Gunma, Japan
Interests: remote sensing; plant phenotyping; agricultural informatics; environmental plant science; global environmental science
Special Issues, Collections and Topics in MDPI journals

Dr. Parinaz Rahimzadeh-Bajgiran

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Guest Editor

Remote Sensing of Natural Resources, School of Forest Resources, University of Maine 215 Nutting Hall, Orono, ME 04469-5755, USA
Interests: remote sending of forest health and productivity; forest disturbance; landscape dynamics; drought and evapotranspiration; optical-thermal remote sensing
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Prof. Dr. Shan Lu

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Guest Editor

School of Geographical Sciences, Northeast Normal University, 5268 Renmin Street, Changchun 130024, China
Interests: vegetation remote sensing; biophysical parameter retrieval; multi-angle reflectance; polarized remote sensing; hyperspectral remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, remote sensing techniques have progressed remarkably. These technological advancements have led to the accurate observation of the spatiotemporal variability of some vegetation parameters, such as aboveground biomass, plant functional types, and phenology. A wide variety of satellite imagery, airborne scanner images, UAV photographs, and tower monitoring data are acquired regularly because of the Earth's surface, providing a wealth of information that can be used to identify or map vegetation distributions. In addition, a wide range of passive and active sensors carried on various platforms deliver huge volumes of data, making the vegetation mapping in different ecosystems, such as agricultural land, grasslands, and forests, more efficient and accurate. Consequently, vegetation mapping such as vegetation type and composition, productivity, health, stress, and many other biophysical and biochemical property mapping has become a critical component of remote sensing applications and an essential tool for the evaluation of the sustainability and effective management of various ecosystems.

The Special Issue “Remote Sensing and Vegetation Mapping” encourages discussion concerning innovative techniques/approaches that are based on any type of remote sensing data, which are used for vegetation mapping in various ecosystems at different spatial and temporal scales.

Prof. Dr. Kenji Omasa
Dr. Parinaz Rahimzadeh-Bajgiran
Dr. Shan Lu
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 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

Crop mapping
Forest mapping
Smart agriculture
Vegetation phenology
Chlorophyll fluorescence of vegetation
Biophysical parameters retrieval
Vegetation health

Published Papers (8 papers)

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Research

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23 pages, 8375 KiB

Open AccessArticle

Spatio-Temporal Characteristics and Driving Factors of the Foliage Clumping Index in the Sanjiang Plain from 2001 to 2015

by Kehong Hu, Zhen Zhang, Hongliang Fang, Yijie Lu, Zhengnan Gu and Min Gao

Remote Sens. 2021, 13(14), 2797; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142797 - 16 Jul 2021

Cited by 7 | Viewed by 1901

Abstract

The Sanjiang Plain is the largest agricultural reclamation area and the biggest marsh area in China. The regional vegetation coverage in this area is vital to local ecological systems, and vegetation growth is affected by natural and anthropogenic factors. The clumping index (CI) is of great significance for land surface models and obtaining information on other vegetation structures. However, most existing ecological models and the retrieval of other vegetation structures do not consider the spatial and temporal variations of CI, and few studies have focused on detecting factors that influence the spatial differentiation of CI. To address these issues, this study investigated the spatial and temporal characteristics of foliage CI in the Sanjiang Plain, analysing the correlation between CI and leaf area index (LAI) through multiple methods (such as Theil−Sen trend analysis, the Mann−Kendall test, and the correlation coefficient) based on the 2001−2015 Chinese Academy of Sciences Clumping Index (CAS CI) and Global LAnd Surface Satellite Leaf Area Index (GLASS LAI). The driving factors of the spatial differentiation of CI were also investigated based on the geographical detector model (GDM) with natural data (including the average annual temperature, annual precipitation, elevation, slope, aspect, vegetation type, soil type, and geomorphic type) and anthropogenic data (the land use type). The results showed that (1) the interannual variation of foliage CI was not obvious, but the seasonal variation was obvious in the Sanjiang Plain from 2001 to 2015; (2) the spatial distribution of the multiyear mean CI of each season in the Sanjiang Plain was similar to the spatial distribution of the land use type, and the CI decreased slightly with increases in elevation; (3) the correlation between the growing season mean CI (CI_GS) and the growing season mean LAI (LAI_GS) time series was not significant, but their spatial distributions were negatively correlated; (4) topographic factors (elevation and slope) and geomorphic type dominated the spatial differentiation of foliage CI in the Sanjiang Plain, and the interactions between driving factors enhanced their explanatory power in terms of the spatial distribution of foliage CI. This study can help improve the accuracy of the retrieval of other vegetation structures and the simulation of land surface models in the Sanjiang Plain, providing invaluable insight for the analysis of the spatial and temporal variations of vegetation based on CI. Moreover, the results of this study support a theoretical basis for understanding the explanatory power of natural and anthropogenic factors in the spatial distribution of CI, along with its driving mechanism. Full article

(This article belongs to the Special Issue Remote Sensing and Vegetation Mapping)

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17 pages, 4994 KiB

Open AccessArticle

Assessment of the Spatial and Temporal Patterns of Cover Crops Using Remote Sensing

by Kushal KC, Kaiguang Zhao, Matthew Romanko and Sami Khanal

Remote Sens. 2021, 13(14), 2689; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142689 - 08 Jul 2021

Cited by 14 | Viewed by 4030

Abstract

Cover cropping is a conservation practice that helps to alleviate soil health problems and reduce nutrient losses. Understanding the spatial variability in historic and current adoption of cover cropping practices and their impacts on soil, water, and nutrient dynamics at a landscape scale is an important step in determining and prioritizing areas in a watershed to effectively utilize this practice. However, such data are lacking. Our objective was to develop a spatial and temporal inventory of winter cover cropping practices in the Maumee River watershed using images collected by Landsat satellites (Landsat 5, 7 and 8) from 2008 to 2019 in Google Earth Engine (GEE) platform. Each year, satellite images collected during cover crop growing season (i.e., between October and April) were converted into two seasonal composites based on cover crop phenology. Using these composites, various image-based covariates were extracted for 628 ground-truth (field) data. By integrating ground-truth and image-based covariates, a cover crop classification model based on a random forest (RF) algorithm was developed, trained and validated in GEE platform. Our classification scheme differentiated four cover crop categories: Winter Hardy, Winter Kill, Spring Emergent, and No Cover. The overall classification accuracy was 75%, with a kappa coefficient of 0.63. The results showed that more than 50% of the corn-soybean areas in the Maumee River watershed were without winter crops during 2008–2019 period. It was found that 2019/2020 and 2009/2010 were the years with the largest and lowest cover crop areas, with 34% and 10% in the watershed, respectively. The total cover cropping area was then assessed in relation to fall precipitation and cumulative growing degree days (GDD). There was no apparent increasing trend in cover crop areas between 2008 and 2019, but the variability in cover crops areas was found to be related to higher accumulated GDD and fall precipitation. A detailed understanding of the spatial and temporal distribution of cover crops using GEE could help in promoting site-specific management practices to enhance their environmental benefits. This also has significance to policy makers and funding agencies as they could use the information to localize areas in need of interventions for supporting adoption of cover cropping practice. Full article

(This article belongs to the Special Issue Remote Sensing and Vegetation Mapping)

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21 pages, 8101 KiB

Open AccessArticle

Mapping Winter Crops Using a Phenology Algorithm, Time-Series Sentinel-2 and Landsat-7/8 Images, and Google Earth Engine

by Li Pan, Haoming Xia, Xiaoyang Zhao, Yan Guo and Yaochen Qin

Remote Sens. 2021, 13(13), 2510; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132510 - 26 Jun 2021

Cited by 63 | Viewed by 6738

Abstract

With the increasing population and continuation of climate change, an adequate food supply is vital to economic development and social stability. Winter crops are important crop types in China. Changes in winter crops planting areas not only have a direct impact on China’s production and economy, but also potentially affects China’s food security. Therefore, it is necessary to obtain information on the planting of winter crops. In this study, we use the time series data of individual pixels, calculate the temporal statistics of spectral bands and the vegetation indices of optical data based on the phenological characteristics of specific vegetation or crops and record them in the time series data, and apply decision trees and rule-based algorithms to generate annual maps of winter crops. First, we constructed a dataset combining all the available images from Landsat 7/8 and Sentinel-2A/B. Second, we generated an annual map of land cover types to obtain the cropland mask in 2019. Third, we generated a time series of a single cropland pixel, and calculated the phenological indicators for classification by extracting the differences in phenological characteristics of different crops: these phenological indicators include SOS (start of season), SDP (start date of peak), EOS (end of season), GUS (green-up speed) and GSL (growing-season length). Finally, we identified winter crops in 2019 based on their phenological characteristics. The main advantages of the phenology-based algorithm proposed in this study include: (1) Combining multiple sensor data to construct a high spatiotemporal resolution image collection. (2) By analyzing the whole growth season of winter crops, the planting area of winter crops can be extracted more accurately, and (3) the phenological indicators of different periods are extracted, which is conducive to monitoring winter crop planting information and seasonal dynamics. The results show that the algorithm constructed in this study can accurately extract the planting area of winter crops, with user, producer, overall accuracies and Kappa coefficients of 96.61%, 94.13%, 94.56% and 0.89, respectively, indicating that the phenology-based algorithm is reliable for large area crop classification. This research will provide a point of reference for crop area extraction and monitoring. Full article

(This article belongs to the Special Issue Remote Sensing and Vegetation Mapping)

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16 pages, 25832 KiB

Open AccessArticle

Spatiotemporal Variation of Vegetation Productivity and Its Feedback to Climate Change in Northeast China over the Last 30 Years

by Ling Hu, Wenjie Fan, Wenping Yuan, Huazhong Ren and Yaokui Cui

Remote Sens. 2021, 13(5), 951; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050951 - 03 Mar 2021

Cited by 7 | Viewed by 2319

Abstract

Gross primary productivity (GPP) represents total vegetation productivity and is crucial in regional or global carbon balance. The Northeast China (NEC), abundant in vegetation resources, has a relatively large vegetation productivity; however, under obvious climate change (especially warming), whether and how will the vegetation productivity and ecosystem function of this region changed in a long time period needs to be revealed. With the help of GPP products provided by the Global LAnd Surface Satellite (GLASS) program, this paper gives an overview of the regional feedback of vegetation productivity to the changing climate (including temperature, precipitation, and solar radiation) across the NEC from 1982 to 2015. Analyzing results show a slight positive response of vegetation productivities to warming across the NEC with an overall increasing trend of GPP_GS (accumulated GPP within the growing season of each year) at 4.95 g C/m². yr⁻² over the last three decades. More specifically, the growth of crops, rather than forests, contributes more to the total increasing productivity, which is mainly induced by the agricultural technological progress as well as warming. As for GPP in forested area in the NEC, the slight increment of GPP_GS in northern, high-latitude forested region of the NEC was caused by warming, while non-significant variation of GPP_GS was found in southern, low-latitude forested region. In addition, an obvious greening trend, as reported in other regions, was also found in the NEC, but GPP_GS of forests in southern NEC did not have significant variations, which indicated that vegetation productivity is not bound to increase simultaneously with greening, except for these high-latitude forested areas in the NEC. The regional feedback of vegetation productivity to climate change in the NEC can be an indicator for vegetations growing in higher latitudes in the future under continued climate change. Full article

(This article belongs to the Special Issue Remote Sensing and Vegetation Mapping)

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21 pages, 14107 KiB

Open AccessArticle

A Robust Vegetation Index Based on Different UAV RGB Images to Estimate SPAD Values of Naked Barley Leaves

by Yu Liu, Kenji Hatou, Takanori Aihara, Sakuya Kurose, Tsutomu Akiyama, Yasushi Kohno, Shan Lu and Kenji Omasa

Remote Sens. 2021, 13(4), 686; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040686 - 13 Feb 2021

Cited by 32 | Viewed by 4502

Abstract

Chlorophyll content in plant leaves is an essential indicator of the growth condition and the fertilization management effect of naked barley crops. The soil plant analysis development (SPAD) values strongly correlate with leaf chlorophyll contents. Unmanned Aerial Vehicles (UAV) can provide an efficient way to retrieve SPAD values on a relatively large scale with a high temporal resolution. But the UAV mounted with high-cost multispectral or hyperspectral sensors may be a tremendous economic burden for smallholder farmers. To overcome this shortcoming, we investigated the potential of UAV mounted with a commercial digital camera for estimating the SPAD values of naked barley leaves. We related 21 color-based vegetation indices (VIs) calculated from UAV images acquired from two flight heights (6.0 m and 50.0 m above ground level) in four different growth stages with SPAD values. Our results indicated that vegetation extraction and naked barley ears mask could improve the correlation between image-calculated vegetation indices and SPAD values. The VIs of ‘L*,’ ‘b*,’ ‘G − B’ and ‘2G − R − B’ showed significant correlations with SPAD values of naked barley leaves at both flight heights. The validation of the regression model showed that the index of ‘G-B’ could be regarded as the most robust vegetation index for predicting the SPAD values of naked barley leaves for different images and different flight heights. Our study demonstrated that the UAV mounted with a commercial camera has great potentiality in retrieving SPAD values of naked barley leaves under unstable photography conditions. It is significant for farmers to take advantage of the cheap measurement system to monitor crops. Full article

(This article belongs to the Special Issue Remote Sensing and Vegetation Mapping)

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19 pages, 5261 KiB

Open AccessArticle

Forest Potential Productivity Mapping by Linking Remote-Sensing-Derived Metrics to Site Variables

by Parinaz Rahimzadeh-Bajgiran, Chris Hennigar, Aaron Weiskittel and Sean Lamb

Remote Sens. 2020, 12(12), 2056; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12122056 - 26 Jun 2020

Cited by 22 | Viewed by 4717

Abstract

A fine-resolution region-wide map of forest site productivity is an essential need for effective large-scale forestry planning and management. In this study, we incorporated Sentinel-2 satellite data into an increment-based measure of forest productivity (biomass growth index (BGI)) derived from climate, lithology, soils, and topographic metrics to map improved BGI (iBGI) in parts of North American Acadian regions. Initially, several Sentinel-2 variables including nine single spectral bands and 12 spectral vegetation indices (SVIs) were used in combination with forest management variables to predict tree volume/ha and height using Random Forest. The results showed a 10–12 % increase in out of bag (OOB) r² when Sentinel-2 variables were included in the prediction of both volume and height together with BGI. Later, selected Sentinel-2 variables were used for biomass growth prediction in Maine, USA and New Brunswick, Canada using data from 7738 provincial permanent sample plots. The Sentinel-2 red-edge position (S2REP) index was identified as the most important variable over others to have known influence on site productivity. While a slight improvement in the iBGI accuracy occurred compared to the base BGI model (~2%), substantial changes to coefficients of other variables were evident and some site variables became less important when S2REP was included. Full article

(This article belongs to the Special Issue Remote Sensing and Vegetation Mapping)

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17 pages, 3442 KiB

Open AccessArticle

Estimating Frost during Growing Season and Its Impact on the Velocity of Vegetation Greenup and Withering in Northeast China

by Guorong Deng, Hongyan Zhang, Lingbin Yang, Jianjun Zhao, Xiaoyi Guo, Hong Ying, Wu Rihan and Dan Guo

Remote Sens. 2020, 12(9), 1355; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091355 - 25 Apr 2020

Cited by 17 | Viewed by 2861

Abstract

Vegetation phenology and photosynthetic primary production have changed simultaneously over the past three decades, thus impacting the velocity of vegetation greenup (Vgreenup) and withering (Vwithering). Although climate warming reduces the frequency of frost events, vegetation is exposed more frequently to frost due to the extension of the growing season. Currently, little is known about the effect of frost during the growing season on Vgreenup and Vwithering. This study analyzed spatiotemporal variations in Vgreenup and Vwithering in Northeast China between 1982 to 2015 using Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index (GIMMS 3g NDVI) data. Frost days and frost intensity were selected as indicators to investigate the influence of frost during the growing season on Vgreenup and Vwithering, respectively. Increased frost days during the growing season slowed Vgreenup and Vwithering. The number of frost days had a greater impact on Vwithering compared to Vgreenup. In addition, Vgreenup and Vwithering of forests were more vulnerable to frost days, while frost days had a lesser effect on grasslands. In contrast to frost days, frost intensity, which generally decreased during the growing season, accelerated Vgreenup and Vwithering for all land cover types. Changes in frost intensity had less of an impact on forests, whereas the leaf structure of grasslands is relatively simple and thus more vulnerable to frost intensity. The effects of frost during the growing season on Vgreenup and Vwithering in Northeast China were highlighted in this study, and the results provide a useful reference for understanding local vegetation responses to global climate change. Full article

(This article belongs to the Special Issue Remote Sensing and Vegetation Mapping)

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Review

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25 pages, 15312 KiB

Open AccessReview

Forest Leaf Mass per Area (LMA) through the Eye of Optical Remote Sensing: A Review and Future Outlook

by Tawanda W. Gara, Parinaz Rahimzadeh-Bajgiran and Roshanak Darvishzadeh

Remote Sens. 2021, 13(17), 3352; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173352 - 24 Aug 2021

Cited by 13 | Viewed by 3441

Abstract

Quantitative remote sensing of leaf traits offers an opportunity to track biodiversity changes from space. Augmenting field measurement of leaf traits with remote sensing provides a pathway for monitoring essential biodiversity variables (EBVs) over space and time. Detailed information on key leaf traits such as leaf mass per area (LMA) is critical for understanding ecosystem structure and functioning, and subsequently the provision of ecosystem services. Although studies on remote sensing of LMA and related constituents have been conducted for over three decades, a comprehensive review of remote sensing of LMA—a key driver of leaf and canopy reflectance—has been lacking. This paper reviews the current state and potential approaches, in addition to the challenges associated with LMA estimation/retrieval in forest ecosystems. The physiology and environmental factors that influence the spatial and temporal variation of LMA are presented. The scope of scaling LMA using remote sensing systems at various scales, i.e., near ground (in situ), airborne, and spaceborne platforms is reviewed and discussed. The review explores the advantages and disadvantages of LMA modelling techniques from these platforms. Finally, the research gaps and perspectives for future research are presented. Our review reveals that although progress has been made, scaling LMA to regional and global scales remains a challenge. In addition to seasonal tracking, three-dimensional modeling of LMA is still in its infancy. Over the past decade, the remote sensing scientific community has made efforts to separate LMA constituents in physical modelling at the leaf level. However, upscaling these leaf models to canopy level in forest ecosystems remains untested. We identified future opportunities involving the synergy of multiple sensors, and investigated the utility of hybrid models, particularly at the canopy and landscape levels. Full article

(This article belongs to the Special Issue Remote Sensing and Vegetation Mapping)

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