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Remote Sensing and its Application in Ecosystems
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Remote Sensing and its Application in Ecosystems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 13853

Special Issue Editors

Prof. Dr. Kenji Omasa

E-Mail Website
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

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shan Lu

E-Mail Website
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,

Nowadays, a variety of remote sensors have been launched to study ecosystems from space. Thanks to their broad spatial extent and high temporal resolution, remote sensing can facilitate research related to identifying the biophysical characteristics of habitats, predicting the species distribution and spatial variability in species richness, and detecting natural and human-caused change at scales ranging from individual landscapes to regions or the entire world, and over time. Consequently, remote sensing and its applications are important for ecosystem monitoring, service and management.

The Special Issue “Remote sensing and its application in ecosystems” encourages discussion concerning innovative techniques/approaches that are based on remote sensing data, which are used for applications in ecosystems at different spatial and temporal scales.

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

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Keywords

  • biodiversity monitoring by remote sensing
  • ecological processes
  • ecological function
  • environmental change
  • GIS
  • remote sensing
  • satellite imagery time series
  • species distribution and diversity modelling
  • disturbance
  • vegetation health

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Published Papers (4 papers)

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Research

15 pages, 8086 KiB  
Article
Experimental and Numerical Investigation of Dustfall Effect on Remote Sensing Retrieval Accuracy of Chlorophyll Content
by Baodong Ma, Xuexin Li, Aiman Liang, Yuteng Chen and Defu Che
Sensors 2019, 19(24), 5530; https://doi.org/10.3390/s19245530 - 14 Dec 2019
Cited by 5 | Viewed by 2764
Abstract
Chlorophyll is the dominant pigment in the photosynthetic light-harvesting complexes that is related to the physiological function of leaves and is responsible for light absorption and energy transfer. Dust pollution has become an environmental problem in many areas in China, indicating that accurately [...] Read more.
Chlorophyll is the dominant pigment in the photosynthetic light-harvesting complexes that is related to the physiological function of leaves and is responsible for light absorption and energy transfer. Dust pollution has become an environmental problem in many areas in China, indicating that accurately estimating chlorophyll content of vegetation using remote sensing for assessing the vegetation growth status in dusty areas is vital. However, dust deposited on the leaf may affect the chlorophyll content retrieval accuracy. Thus, quantitatively studying the dustfall effect is essential. Using selected vegetation indices (VIs), the medium resolution imaging spectrometer terrestrial chlorophyll index (MTCI), and the double difference index (DD), we studied the retrieval accuracy of chlorophyll content at the leaf scale under dusty environments based on a laboratory experiment and spectra simulation. First, the retrieval accuracy under different dustfall amounts was studied based on a laboratory experiment. Then, the relationship between dustfall amount and fractional dustfall cover (FDC) was experimentally analyzed for spectra simulation of dusty leaves. Based on spectral data simulated using a PROSPECT-based mixture model, the sensitivity of VIs to dust under different chlorophyll contents was analyzed comprehensively, and the MTCI was modified to reduce its sensitivity to dust. The results showed that (1) according to experimental investigation, the DD model provides low retrieval accuracy, the MTCI model is highly accurate when the dustfall amount is less than 80 g/m2, and the retrieval accuracy decreases significantly when the dustfall amount is more than 80 g/m2; (2) a logarithmic relationship exists between FDC and dustfall amount, and the PROSPECT-based mixture model can simulate the leaf spectra under different dustfall amounts and different chlorophyll contents with a root mean square error of 0.015; and (3) according to numerical investigation, MTCI’s sensitivity to dust in the chlorophyll content range of 25 to 60 μg/cm2 is lower than in other chlorophyll content ranges; DD’s sensitivity to dust was generally high throughout the whole chlorophyll content range. These findings may contribute to quantitatively understanding the dustfall effect on the retrieval of chlorophyll content and would help to accurately retrieve chlorophyll content in dusty areas using remote sensing. Full article
(This article belongs to the Special Issue Remote Sensing and its Application in Ecosystems)
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16 pages, 2343 KiB  
Article
Estimating Chlorophyll Content of Leafy Green Vegetables from Adaxial and Abaxial Reflectance
by Fan Lu, Zhaojun Bu and Shan Lu
Sensors 2019, 19(19), 4059; https://doi.org/10.3390/s19194059 - 20 Sep 2019
Cited by 12 | Viewed by 3740
Abstract
As a primary pigment of leafy green vegetables, chlorophyll plays a major role in indicating vegetable growth status. The application of hyperspectral remote sensing reflectance offers a quick and nondestructive method to estimate the chlorophyll content of vegetables. Reflectance of adaxial and abaxial [...] Read more.
As a primary pigment of leafy green vegetables, chlorophyll plays a major role in indicating vegetable growth status. The application of hyperspectral remote sensing reflectance offers a quick and nondestructive method to estimate the chlorophyll content of vegetables. Reflectance of adaxial and abaxial leaf surfaces from three common leafy green vegetables: Pakchoi var. Shanghai Qing (Brassica chinensis L. var. Shanghai Qing), Chinese white cabbage (Brassica campestris L. ssp. Chinensis Makino var. communis Tsen et Lee), and Romaine lettuce (Lactuca sativa var longifoliaf. Lam) were measured to estimate the leaf chlorophyll content. Modeling based on spectral indices and the partial least squares regression (PLS) was tested using the reflectance data from the two surfaces (adaxial and abaxial) of leaves in the datasets of each individual vegetable and the three vegetables combined. The PLS regression model showed the highest accuracy in estimating leaf chlorophyll content of pakchoi var. Shanghai Qing (R2 = 0.809, RMSE = 62.44 mg m−2), Chinese white cabbage (R2 = 0.891, RMSE = 45.18 mg m−2) and Romaine lettuce (R2 = 0.834, RMSE = 38.58 mg m−2) individually as well as of the three vegetables combined (R2 = 0.811, RMSE = 55.59 mg m−2). The good predictability of the PLS regression model is considered to be due to the contribution of more spectral bands applied in it than that in the spectral indices. In addition, both the uninformative variable elimination PLS (UVE-PLS) technique and the best performed spectral index: MDATT, showed that the red-edge region (680–750 nm) was effective in estimating the chlorophyll content of vegetables with reflectance from two leaf surfaces. The combination of the PLS regression model and the red-edge region are insensitive to the difference between the adaxial and abaxial leaf structure and can be used for estimating the chlorophyll content of leafy green vegetables accurately. Full article
(This article belongs to the Special Issue Remote Sensing and its Application in Ecosystems)
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25 pages, 15206 KiB  
Article
Using Airborne Hyperspectral Imaging Spectroscopy to Accurately Monitor Invasive and Expansive Herb Plants: Limitations and Requirements of the Method
by Dominik Kopeć, Agata Zakrzewska, Anna Halladin-Dąbrowska, Justyna Wylazłowska, Adam Kania and Jan Niedzielko
Sensors 2019, 19(13), 2871; https://doi.org/10.3390/s19132871 - 28 Jun 2019
Cited by 16 | Viewed by 3644
Abstract
Remote sensing (RS) is currently regarded as one of the standard tools used for mapping invasive and expansive plants for scientific purposes and it is increasingly widely used in nature conservation management. The applicability of RS methods is determined by its limitations and [...] Read more.
Remote sensing (RS) is currently regarded as one of the standard tools used for mapping invasive and expansive plants for scientific purposes and it is increasingly widely used in nature conservation management. The applicability of RS methods is determined by its limitations and requirements. One of the most important limitations is the species percentage cover at which the classification result is correct and useful for nature conservation. The primary objective, carried out in 2017 in three areas of Poland, was to determine the minimum percentage cover from which it is possible to identify a target species by RS methods. A secondary objective of this research, related to the requirements of the method, was to optimize the set of training polygons for a target species in terms of the number of polygons and abundance percentage cover of the target species. Our method has to be easy to use, effective, and applicable, therefore the analysis was carried out using the basic set of rasters—the first 30 channels after the Minimum Noise Fraction (MNF) transformation (the mosaic of hyperspectral data from HySpex sensors with spectral range 0.4–2.5 µm) and commonly used Random Forest algorithm. The analysis used airborne hyperspectral data with a spatial resolution of 1 m to perform classification of one invasive and three expansive plants—two grasses and two large perennials. On-ground training and validation data sets were collected simultaneously with airborne data collection. When testing different classification scenarios, only the set of training polygons for a target species was changed. Classification results were evaluated based on three methods: accuracy measures (Kappa and F1), true-positive pixels in subclasses with different species cover and compatibility with field mapping. The classification results indicate that to classify the target plant species at the accepted level, the training dataset should contain polygons with a species cover ranging from 80–100%. Training performed only using polygons with a species characterized by a variable, but lower, cover (20–70%) and missing samples in the 80–100% range, led to a map which was not acceptable because of a high overestimation of target species. We achieved effective identification of species in areas where the species cover is above 50%, considering that ecosystems are heterogeneous. The results of these studies developed a methodology of field data acquisition and the necessity of synchronization in the acquisition of airborne data, and training and validation of on-ground sampling. Full article
(This article belongs to the Special Issue Remote Sensing and its Application in Ecosystems)
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12 pages, 3024 KiB  
Article
Spatiotemporal Variability of Asymmetric Daytime and Night-Time Warming and Its Effects on Vegetation in the Yellow River Basin from 1982 to 2015
by Liqun Ma, Haoming Xia and Qingmin Meng
Sensors 2019, 19(8), 1832; https://doi.org/10.3390/s19081832 - 17 Apr 2019
Cited by 16 | Viewed by 3298
Abstract
Temperatures from 1982 to 2015 have exhibited an asymmetric warming pattern between day and night throughout the Yellow River Basin. The response to this asymmetric warming can be linked to vegetation growth as quantified by the NDVI (Normalized Difference Vegetation Index). In this [...] Read more.
Temperatures from 1982 to 2015 have exhibited an asymmetric warming pattern between day and night throughout the Yellow River Basin. The response to this asymmetric warming can be linked to vegetation growth as quantified by the NDVI (Normalized Difference Vegetation Index). In this study, the time series trends of the maximum temperature (Tmax) and the minimum temperature (Tmin) and their spatial patterns in the growing season (April–October) of the Yellow River Basin from 1982 to 2015 were analyzed. We evaluated how vegetation NDVI had responded to daytime and night-time warming, based on NDVI and meteorological parameters (precipitation and temperature) over the period 1982–2015. We found: (1) a persistent increase in the growing season Tmax and Tmin in 1982–2015 as confirmed by using the Mann–Kendall (M–K) non-parametric test method (p < 0.01), where the rate of increase of Tmin was 1.25 times that of Tmax, and thus the diurnal warming was asymmetric during 1982–2015; (2) the partial correlation between Tmax and NDVI was significantly positive only for cultivated plants, shrubs, and desert, which means daytime warming may increase arid and semi-arid vegetation’s growth and coverage, and cultivated plants’ growth and yield. The partial correlation between Tmin and NDVI of all vegetation types except broadleaf forest is very significant (p < 0.01) and, therefore, it has more impacts vegetation across the whole basin. This study demonstrates a methodogy for studying regional responses of vegetation to climate extremes under global climate change. Full article
(This article belongs to the Special Issue Remote Sensing and its Application in Ecosystems)
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