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Remote Sensing for Terrestrial Ecosystem Health

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 54386

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Special Issue Editors

Prof. Dr. Yuhong He

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Guest Editor

Department of Geography, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, ON L5L 1C6, Canada
Interests: ecosystem modeling; plant biophysical and biochemical traits in relation to environmental and anthropogenic driving factors
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Qihao Weng

E-Mail Website
Guest Editor

Center for Urban and Environmental Change, Department of Earth and Environmental Systems, Indiana State University, Terre Haute, IN 47809, USA
Interests: remote sensing; imaging science; GIS; land use and land cover change; urban environment and ecosystem
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Dr. Zhaoqin Li

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Guest Editor

Global Institute for Water Security, School of Environment and Sustainability, University of Saskatchewan, 11 Innovation Boulevard, Saskatoon, SK S7N 3H5, Canada
Interests: remote sensing; GIS; water-ecosystem-agriculture nexus; flood forecast

Dr. Cameron Proctor

E-Mail Website
Guest Editor

Department of Geography and Programs in Environment, University of Toronto Mississauga, 3359 Mississauga Rd., Mississauga, ON L5L 1C6, Canada
Interests: hyperspectral; unmanned aerial vehicles (UAV); belowground carbon; root exudates; ecological modelling; methane biogeochemistry
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Dr. Bing Lu

E-Mail Website
Guest Editor

Fiera Biological Consulting, Edmonton, AB T6E 1Z9, Canada
Interests: vegetation remote sensing; imagery classification; grassland ecology; UAV; multispectral and hyperspectral imaging; radiation transfer modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The health of terrestrial ecosystems has diminished because of human activities and climate change in the past few decades. Yet, detecting and documenting trends in ecosystem health has proven to be challenging. The increased availability in remote sensing data has improved the scale, depth, and fidelity at which ecosystem traits can be quantified. Multi-frequency and/or multi-polarization microwave data, multispectral and hyperspectral data, and their integration are important sources for spatially explicit terrestrial ecosystem health assessment. LiDAR and digital aerial photos have provided essential data and insights into the vegetation composition and biophysical structure of ecosystems. The long-time series of remote sensing data, such as Landsat and MODIS, have offered opportunities to investigate ecosystem’s resilience to disturbances. On the other hand, unmanned aerial systems (UAVs) offer the possibility of a landscape synoptic view from previously unfeasible temporal and spatial scales.

In recent years, more algorithms or tools have become available for processing remote sensing data and extracting various information, such as machine learning, deep learning, artificial intelligence (AI), and Google Earth Engine, which have provided new opportunities for the further investigation of terrestrial ecosystems.

Remote sensing data have also been integrated with point-based biophysical and biochemical observations into ecological modeling, in order to understand ecosystems in a physically explicit manner. The remote sensing data can be directly used as a model input, or indirectly used to calibrate model parameters and/or validate model outputs.

This Special Issue welcomes original research focusing on the recent advances in remote sensing for integrating measurements of ecosystem vigor, structure, and resilience at multiple spatial or temporal scales. The articles shall focus on, but are not limited to, the following:

Innovative research using multispectral or hyperspectral data for terrestrial ecosystem health assessment
Polarmetric SAR and SAR interferometry on retrieving ecosystem biophysical and biochemical attributes
UAV-based remote sensing for assessing terrestrial ecosystem health
LiDAR and digital aerial photo for ecosystem health assessment
The synergy of multi-source/multi-type remote sensing data (e.g., optical, thermal, Lidar, and Radar) in improving the accuracy of ecosystem attributes
Multispatial or multitemporal data for terrestrial ecosystem health monitoring
Novel studies focusing on addressing the spatial issues of ecosystem attributes retrieval and ecological modeling
Data assimilation of remote sensing into ecological modeling
New algorithms or tools (e.g., machine learning, deep learning, AI, and Google Earth Engine) for monitoring ecosystem attributes
Case studies on near-real time ecosystem health monitoring with remote sensing approaches
Remote sensing investigating interactions between human and physical environment

Prof. Yuhong He
Prof. Qihao Weng
Dr. Zhaoqin Li
Dr. Cameron Proctor
Dr. Bing Lu
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 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.

Published Papers (7 papers)

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Research

25 pages, 4816 KiB

Open AccessArticle

Investigating Banksia Coastal Woodland Decline Using Multi-Temporal Remote Sensing and Field-Based Monitoring Techniques

by Rose-Anne Bell and J. Nikolaus Callow

Remote Sens. 2020, 12(4), 669; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12040669 - 18 Feb 2020

Cited by 6 | Viewed by 2988

Abstract

Coastal woodlands, notable for their floristic diversity and ecosystem service values, are increasingly under threat from a range of interacting biotic and abiotic stressors. Monitoring these complex ecosystems has traditionally been confined to field-scale vegetation surveys; however, remote sensing applications are increasingly becoming more viable. This study reports on the application of field-based monitoring and remote sensing/(Geographic Information System) GIS to interrogate trends in Banksia coastal woodland decline (Kings Park, Perth and Western Australia) and documents the patterns, and potential drivers, of tree mortality over the period 2012–2016. Application of geographic object-based image analysis (GEOBIA) at a park scale was of limited benefit within the closed-canopy ecosystem, with manual digitisation methods feasible only at the smaller transect scale. Analysis of field-based identification of tree mortality, crown-specific spectral characteristics and park-scale change detection imagery identified climate-driven stressors as the likely primary driver of tree mortality in the woodland, with vegetation decline exacerbated by secondary factors, including water stress and low system resilience occasioned by the inability to access the water table and competition between tree species. The results from this paper provide a platform to inform monitoring efforts using airborne remote sensing within coastal woodlands. Full article

(This article belongs to the Special Issue Remote Sensing for Terrestrial Ecosystem Health)

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23 pages, 20610 KiB

Open AccessArticle

Quantifying Drought Sensitivity of Mediterranean Climate Vegetation to Recent Warming: A Case Study in Southern California

by Chunyu Dong, Glen MacDonald, Gregory S. Okin and Thomas W. Gillespie

Remote Sens. 2019, 11(24), 2902; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11242902 - 05 Dec 2019

Cited by 16 | Viewed by 4703

Abstract

A combination of drought and high temperatures (“global-change-type drought”) is projected to become increasingly common in Mediterranean climate regions. Recently, Southern California has experienced record-breaking high temperatures coupled with significant precipitation deficits, which provides opportunities to investigate the impacts of high temperatures on the drought sensitivity of Mediterranean climate vegetation. Responses of different vegetation types to drought are quantified using the Moderate Resolution Imaging Spectroradiometer (MODIS) data for the period 2000–2017. The contrasting responses of the vegetation types to drought are captured by the correlation and regression coefficients between Normalized Difference Vegetation Index (NDVI) anomalies and the Palmer Drought Severity Index (PDSI). A novel bootstrapping regression approach is used to decompose the relationships between the vegetation sensitivity (NDVI–PDSI regression slopes) and the principle climate factors (temperature and precipitation) associated with the drought. Significantly increased sensitivity to drought in warmer locations indicates the important role of temperature in exacerbating vulnerability; however, spatial precipitation variations do not demonstrate significant effects in modulating drought sensitivity. Based on annual NDVI response, chaparral is the most vulnerable community to warming, which will probably be severely affected by hotter droughts in the future. Drought sensitivity of coastal sage scrub (CSS) is also shown to be very responsive to warming in fall and winter. Grassland and developed land will likely be less affected by this warming. The sensitivity of the overall vegetation to temperature increases is particularly concerning, as it is the variable that has had the strongest secular trend in recent decades, which is expected to continue or strengthen in the future. Increased temperatures will probably alter vegetation distribution, as well as possibly increase annual grassland cover, and decrease the extent and ecological services provided by perennial woody Mediterranean climate ecosystems as well. Full article

(This article belongs to the Special Issue Remote Sensing for Terrestrial Ecosystem Health)

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23 pages, 9868 KiB

Open AccessArticle

An Operational Workflow of Deciduous-Dominated Forest Species Classification: Crown Delineation, Gap Elimination, and Object-Based Classification

by Yuhong He, Jian Yang, John Caspersen and Trevor Jones

Remote Sens. 2019, 11(18), 2078; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11182078 - 05 Sep 2019

Cited by 9 | Viewed by 3347

Abstract

Recent advances in remote sensing technology provide sufficient spatial detail to achieve species-level classification over large vegetative ecosystems. In deciduous-dominated forests, however, as tree species diversity and forest structural diversity increase, the frequency of spectral overlap between species also increases and our ability to classify tree species significantly decreases. This study proposes an operational workflow of individual tree-based species classification for a temperate, mixed deciduous forest using three-seasonal WorldView images, involving three steps of individual tree crown (ITC) delineation, non-forest gap elimination, and object-based classification. The process of species classification started with ITC delineation using the spectral angle segmentation algorithm, followed by object-based random forest classifications. A total of 672 trees was located along three triangular transects for training and validation. For single-season images, the late-spring, mid-summer, and early-fall images achieve the overall accuracies of 0.46, 0.42, and 0.35, respectively. Combining the spectral information of the early-spring, mid-summer, and early-fall images increases the overall accuracy of classification to 0.79. However, further adding the late-fall image to separate deciduous and coniferous trees as an extra step was not successful. Compared to traditional four-band (Blue, Green, Red, Near-Infrared) images, the four additional bands of WorldView images (i.e., Coastal, Yellow, Red Edge, and Near-Infrared2) contribute to the species classification greatly (OA: 0.79 vs. 0.53). This study gains insights into the contribution of the additional spectral bands and multi-seasonal images to distinguishing species with seemingly high degrees of spectral overlap. Full article

(This article belongs to the Special Issue Remote Sensing for Terrestrial Ecosystem Health)

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22 pages, 4704 KiB

Open AccessArticle

Evaluating Empirical Regression, Machine Learning, and Radiative Transfer Modelling for Estimating Vegetation Chlorophyll Content Using Bi-Seasonal Hyperspectral Images

by Bing Lu and Yuhong He

Remote Sens. 2019, 11(17), 1979; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11171979 - 22 Aug 2019

Cited by 32 | Viewed by 4408

Abstract

Different types of methods have been developed to retrieve vegetation attributes from remote sensing data, including conventional empirical regressions (i.e., linear regression (LR)), advanced empirical regressions (e.g., multivariable linear regression (MLR), partial least square regression (PLSR)), machine learning (e.g., random forest regression (RFR), decision tree regression (DTR)), and radiative transfer modelling (RTM, e.g., PROSAIL). Given that each algorithm has its own strengths and weaknesses, it is essential to compare them and evaluate their effectiveness. Previous studies have mainly used single-date multispectral imagery or ground-based hyperspectral reflectance data for evaluating the models, while multi-seasonal hyperspectral images have been rarely used. Extensive spectral and spatial information in hyperspectral images, as well as temporal variations of landscapes, potentially influence the model performance. In this research, LR, PLSR, RFR, and PROSAIL, representing different types of methods, were evaluated for estimating vegetation chlorophyll content from bi-seasonal hyperspectral images (i.e., a middle- and a late-growing season image, respectively). Results show that the PLSR and RFR generally performed better than LR and PROSAIL. RFR achieved the highest accuracy for both images. This research provides insights on the effectiveness of different models for estimating vegetation chlorophyll content using hyperspectral images, aiming to support future vegetation monitoring research. Full article

(This article belongs to the Special Issue Remote Sensing for Terrestrial Ecosystem Health)

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17 pages, 3500 KiB

Open AccessArticle

Tidal and Meteorological Influences on the Growth of Invasive Spartina alterniflora: Evidence from UAV Remote Sensing

by Xudong Zhu, Lingxuan Meng, Yihui Zhang, Qihao Weng and James Morris

Remote Sens. 2019, 11(10), 1208; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11101208 - 22 May 2019

Cited by 49 | Viewed by 5899

Abstract

Rapid invasion of Spartina alterniflora into Chinese coastal wetlands has attracted much attention. Many field and remote sensing studies have examined the spatio-temporal dynamics of S. alterniflora invasion; however, spatially explicit quantitative analyses of S. alterniflora invasion and its underlying mechanisms at both patch and landscape scales are seldom reported. To fill this knowledge gap, we integrated multi-temporal unmanned aerial vehicle (UAV) imagery, light detection and ranging (LiDAR)-derived elevation data, and tidal and meteorological time series to explore the growth potential (lateral expansion rates and canopy greenness) of S. alterniflora over the intertidal zone in a subtropical coastal wetland (Zhangjiang estuarine wetland, Fujian, China). Our analyses of patch expansion indicated that isolated S. alterniflora patches in this wetland experienced high lateral expansion over the past several years (averaged at 4.28 m/year in patch diameter during 2014–2017), and lateral expansion rates (

y

, m/year) showed a statistically significant declining trend with increasing inundation (

x

, h/day;

3 \leq x \leq 18

y = - 0.17 x + 5.91

R^{2} = 0.78

. Our analyses of canopy greenness showed that the seasonality of the growth potential of S. alterniflora was driven by temperature (Pearson correlation coefficient

r = 0.76

) and precipitation (

r = 0.68

), with the growth potential peaking in early/middle summer with high temperature and adequate precipitation. Together, we concluded that the growth potential of S. alterniflora was co-regulated by tidal and meteorological regimes, in which spatial heterogeneity is controlled by tidal inundation while temporal variation is controlled by both temperature and precipitation. To the best of our knowledge, this is the first spatially explicit quantitative study to examine the influences of tidal and meteorological regimes on both spatial heterogeneity (over the intertidal zone) and temporal variation (intra- and inter-annual) of S. alterniflora at both patch and landscape scales. These findings could serve critical empirical evidence to help answer how coastal salt marshes respond to climate change and assess the vulnerability and resilience of coastal salt marshes to rising sea level. Our UAV-based methodology could be applied to many types of plant community distributions. Full article

(This article belongs to the Special Issue Remote Sensing for Terrestrial Ecosystem Health)

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29 pages, 5396 KiB

Open AccessArticle

Homogeneity Distance Classification Algorithm (HDCA): A Novel Algorithm for Satellite Image Classification

by Mohammad Karimi Firozjaei, Iman Daryaei, Amir Sedighi, Qihao Weng and Seyed Kazem Alavipanah

Remote Sens. 2019, 11(5), 546; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11050546 - 06 Mar 2019

Cited by 14 | Viewed by 3602

Abstract

Image classification is one of the most common methods of information extraction from satellite images. In this paper, a novel algorithm for image classification based on gravity theory was developed, which was called “homogeneity distance classification algorithm (HDCA)”. The proposed HDCA used texture and spectral information for classifying images in two iterative supplementary computing stages: (1) merging, (2) traveling and escaping operators. The HDCA was equipped by a new concept of distance, the weighted Manhattan distance (WMD). Moreover, an improved gravitational search algorithm (IGSA) was applied for selecting features and determining optimal feature space scale in HDCA. In the case of multispectral satellite image classification, the proposed method was compared with two well-known classification methods, Maximum Likelihood classifier (MLC) and Support Vector Machine (SVM). The results of the comparison indicated that overall accuracy values for HDCA, MLC, and SVM are 95.99, 93.15, and 95.00, respectively. Furthermore, the proposed HDCA method was also used for classifying hyperspectral reference datasets (Indian Pines, Salinas and Salinas-A scene). The classification results indicated substantial improvement over previous algorithms and studies by 2% in Indian Pines dataset, 0.7% in the Salinas dataset and 1.2% in the Salinas-A scene. These experimental results demonstrate that the proposed algorithm can classify both multispectral and hyperspectral remote sensing images with reliable accuracy because this algorithm uses the WMD in the classification process and the IGSA to select automatically optimal features for image classification based on spectral and texture information. Full article

(This article belongs to the Special Issue Remote Sensing for Terrestrial Ecosystem Health)

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21 pages, 10147 KiB

Open AccessArticle

Spatially Explicit Mapping of Soil Conservation Service in Monetary Units Due to Land Use/Cover Change for the Three Gorges Reservoir Area, China

by Shicheng Li, Zilu Bing and Gui Jin

Remote Sens. 2019, 11(4), 468; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11040468 - 25 Feb 2019

Cited by 78 | Viewed by 28594

Abstract

Studies of land use/cover change (LUCC) and its impact on ecosystem service (ES) in monetary units can provide information that governments can use to identify where protection and restoration is economically most important. Translating ES in monetary units into decision making strongly depends on the availability of spatially explicit information on LUCC and ES. Yet such datasets are unavailable for the Three Gorges Reservoir Area (TGRA) despite its perceived soil conservation service value (SCSV). The availability of remote sensing-based datasets and advanced GIS techniques has enhanced the potential of spatially explicit ES mapping exercises. Here, we first explored LUCC in the TGRA for four time periods (1995–2000, 2000–2005, 2005–2010, and 2010–2015). Then, applying a value transfer method with an equivalent value factor spatialized using the normalized difference vegetation index (NDVI), we estimated the changes of monetary SCSV in response to LUCC in a spatially explicit way. Finally, the sensitivity of SCSV changes in response to LUCC was determined. Major findings: (i) Expansion of construction land and water bodies and contraction of cropland characterized the LUCC in all periods. Their driving factors include the relocation of residents, construction of the Three Gorges Dam, urbanization, and the Grain for Green Program; (ii) The SCSV for TGRA was generally stable for 1995–2015, declining slightly (<1%), suggesting a sustainable human–environment relationship in the TGRA. The SCSV prevails in regions with elevations (slopes) of 400–1600 m (0°–10°); for Chongqing and its surrounding regions it decreased significantly during 1995–2015; (iii) SCSV’s sensitivity index was 1.04, 0.53, 0.92, and 1.25 in the four periods, respectively, which is generally low. Chongqing and its surrounding regions, with their pervasive urbanization and dense populations, had the highest sensitivity. For 1995–2015, 70.63% of the study area underwent increases in this sensitivity index. Our results provide crucial information for policymaking concerning ecological conservation and compensation. Full article

(This article belongs to the Special Issue Remote Sensing for Terrestrial Ecosystem Health)

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