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Earth Observation in Forest Biophysical/Biochemical Parameter Retrieval

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 46886

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Dr. Prashant K Srivastava

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Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India
Interests: hyperspectral remote sensing; soft computing
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Dr. Ramandeep Kaur M. Malhi

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Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India

Dr. Mukunda Dev Behera

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Centre for Oceans, Rivers, Atmosphere and Land Sciences (CORAL) & School of Water Resources, Indian Institute of Technology (IIT), Khargpur 721302, West Bengal, India
Interests: ecological climatology; biophysical variables; spatial biodiversity; forest cover dynamics
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Prof. Dr. G. Sandhya Kiran

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Department of Botany, M S University of Baroda, Vadodara, India

Dr. Prem Chandra Pandey

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Center for Environmental Sciences and Engineering, School of Natural Science, Shiv Nadar University, Greater Noida, India
Interests: remote sensing applications in forestry & agriculture; hyperspectral remote sensing
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Dr. George P. Petropoulos

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Department of Geography, Harokopio University of Athens, 176 71 Moschato, Greece
Interests: earth observation; modeling; land surface interactions; soil moisture; evapotrasnpiration; land use/cover mapping; change detection; natural hazards; floods; wildfires; sensitivity analysis; soil vegetation atmosphere transfer modeling; operational products benchmarking
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Dear Colleagues,

Forests, which occupy about one-third of the terrestrial surface of Earth, play an indispensable role in monitoring global climate change and ecosystem dynamics. The health of forests has been affected in recent years by various stress factors, such as forest fragmentation and deforestation, biodiversity loss, climate change, invasive species, drought, and unsustainable management. In this situation, quantitative appraisal of biophysical and biochemical parameters is imperative since it can provide a direct assessment and prediction of forest health and sustainability.

Earth Observation aids in the spatiotemporally explicit retrieval of forest biophysical and biochemical parameters in both the optical and microwave domain. This information can be utilized to monitor and forecast short- and long-term changes in forest ecosystems that occur due to different factors. Different methods have been explored to retrieve forest biophysical/biochemical parameters, such as parametric regression (including vegetation indices, shape indices, and spectral transformations), nonparametric regression (including linear and nonlinear machine learning regression algorithms), physically based methods (including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches), and hybrid regression methods (that combine RTM simulations with machine learning regression methods).

This Special Issue will cover the evaluation of different techniques for the retrieval of different biophysical/biochemical parameters using available Earth Observation data. We invite you to contribute a research article to this Special Issue on one of the following topics:

retrieval of biophysical parameters, viz. LAI, vegetation water content, height, biomass, DBH, etc.;
measurement of biochemical parameters, viz. chlorophyll, pigments, etc.;
radiative transfer models in the retrieval of biophysical/biochemical parameters;
establishment of relationships between in situ measured biophysical/biochemical parameters and ground-measured data;
airborne or spaceborne hyperspectral data in the retrieval of biophysical/biochemical parameters;
LiDAR and RADAR remote sensing in the estimation of biophysical variables;
relation of biophysical/biochemical parameters to climatic factors;
biophysical/biochemical parameter retrieval from an Unmanned Autonomous Vehicle (UAV).

Dr. Prashant K Srivastava
Dr. Ramandeep Kaur M. Malhi
Dr. Mukunda Dev Behera
Prof. Dr. G. Sandhya Kiran
Dr. Prem Chandra Pandey
Dr. George P. Petropoulos
Guest Editors

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Published Papers (10 papers)

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Research

17 pages, 2952 KiB

Open AccessArticle

Using Field Spectroradiometer to Estimate the Leaf N/P Ratio of Mixed Forest in a Karst Area of Southern China: A Combined Model to Overcome Overfitting

by Wen He, Yanqiong Li, Jinye Wang, Yuefeng Yao, Ling Yu, Daxing Gu and Longkang Ni

Remote Sens. 2021, 13(17), 3368; https://doi.org/10.3390/rs13173368 - 25 Aug 2021

Cited by 2 | Viewed by 2298

Abstract

The ratio between nitrogen and phosphorus (N/P) in plant leaves has been widely used to assess the availability of nutrients. However, it is challenging to rapidly and accurately estimate the leaf N/P ratio, especially for mixed forest. In this study, we collected 301 samples from nine typical karst areas in Guangxi Province during the growing season of 2018 to 2020. We then utilized five models (partial least squares regression (PLSR), backpropagation neural network (BPNN), general regression neural network (GRNN), PLSR+BPNN, and PLSR+GRNN) to estimate the leaf N/P ratio of plants based on these samples. We also applied the fractional differentiation to extract additional information from the original spectra of each sample. The results showed that the average leaf N/P ratio of plants was 17.97. Plant growth was primarily limited by phosphorus in these karst areas. The sensitive spectra to estimate leaf N/P ratio had wavelengths ranging from 400–730 nm. The prediction capabilities of these five models can be ranked in descending order as PLSR+GRNN, PLSR+BPNN, PLSR, GRNN, and BPNN when considering both accuracy and robustness. The PLSR+GRNN model yielded high R² and performance to deviation (RPD), and low root mean squared error (RMSE) with values of 0.91, 3.15, and 1.98, respectively, for the training test and 0.81, 2.25, and 2.46, respectively, for validation test. Compared with the PLSR model, both PLSR+BPNN and PLSR+GRNN models had higher accuracy and were more stable. Moreover, both PLSR+BPNN and PLSR+GRNN models overcame the issue of overfitting, which occurs when a single model is used to predict leaf N/P ratio. Therefore, both PLSR+BPNN and PLSR+GRNN models can be used to predict the leaf N/P ratio of plants in karst areas. Fractional differentiation is a promising spectral preprocessing technique that can improve the accuracy of models. We conclude that the leaf N/P ratio of mixed forest can be effectively estimated using combined models based on field spectroradiometer data in karst areas. Full article

(This article belongs to the Special Issue Earth Observation in Forest Biophysical/Biochemical Parameter Retrieval)

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17 pages, 3137 KiB

Open AccessCommunication

Applicability of Smoothing Techniques in Generation of Phenological Metrics of Tectona grandis L. Using NDVI Time Series Data

by Ramandeep Kaur M. Malhi, G. Sandhya Kiran, Mangala N. Shah, Nirav V. Mistry, Viral H. Bhavsar, Chandra Prakash Singh, Bimal Kumar Bhattarcharya, Philip A. Townsend and Shiv Mohan

Remote Sens. 2021, 13(17), 3343; https://doi.org/10.3390/rs13173343 - 24 Aug 2021

Cited by 7 | Viewed by 3094

Abstract

Information on phenological metrics of individual plant species is meager. Phenological metrics generation for a specific plant species can prove beneficial if the species is ecologically or economically important. Teak, a dominating tree in most regions of the world has been focused on in the present study due to its multiple benefits. Forecasts on such species can attain a substantial improvement in their productivity. MODIS NDVI time series when subjected to statistical smoothing techniques exhibited good output with Tukey’s smoothing (TS) with a low RMSE of 0.042 compared to single exponential (SE) and double exponential (DE). Phenological metrics, namely, the start of the season (SOS), end of the season (EOS), maximum of the season (MAX), and length of the season (LOS) were generated using Tukey-smoothed MODIS NDVI data for the years 2003–2004 and 2013–2014. Post shifts in SOS and EOS by 14 and 37 days respectively with a preshift of 28 days in MAX were observed in the year 2013–2014. Preshift in MAX was accompanied by an increase in greenness exhibiting increased NDVI value.LOS increased by 24 days in the year 2013–2014, showing an increase in the duration of the season of teak. Dates of these satellite-retrieved phenological occurrences were validated with ground phenological data calculated using crown cover assessment. The present study demonstrated the potential of a spatial approach in the generation of phenometrics for an individual plant species, which is significant in determining productivity or a crucial trophic link for a given region. Full article

(This article belongs to the Special Issue Earth Observation in Forest Biophysical/Biochemical Parameter Retrieval)

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18 pages, 7504 KiB

Open AccessArticle

Satellite Based Fraction of Absorbed Photosynthetically Active Radiation Is Congruent with Plant Diversity in India

by Swapna Mahanand, Mukunda Dev Behera, Partha Sarathi Roy, Priyankar Kumar, Saroj Kanta Barik and Prashant Kumar Srivastava

Remote Sens. 2021, 13(2), 159; https://doi.org/10.3390/rs13020159 - 6 Jan 2021

Cited by 8 | Viewed by 3615

Abstract

A dynamic habitat index (DHI) based on satellite derived biophysical proxy (fraction of absorbed photosynthetically active radiation, FAPAR) was used to evaluate the vegetation greenness pattern across deserts to alpine ecosystems in India that account to different biodiversity. The cumulative (DHI-cum), minimum (DHI-min), and seasonal (DHI-sea) DHI were generated using Moderate Resolution Imaging Spectroradiometer (MODIS)-based FAPAR. The higher DHI-cum and DHI-min represented the biodiversity hotspots of India, whereas the DHI-sea was higher in the semi-arid, the Gangetic plain, and the Deccan peninsula. The arid and the trans-Himalaya are dominated with grassland or barren land exhibit very high DHI-sea. The inter-year correlation demonstrated an increase in vegetation greenness in the semi-arid region, and continuous reduction in greenness in the Northeastern region. The DHI components validated using field-measured plant richness data from four biogeographic regions (semi-arid, eastern Ghats, the Western Ghats, and Northeast) demonstrated good congruence. DHI-cum that represents the annual greenness strongly correlated with the plant richness (R² = 0.90, p-value < 0.001), thereby emerging as a suitable indicator for assessing plant richness in large-scale biogeographic studies. Overall, the FAPAR-based DHI components across Indian biogeographic regions provided understanding of natural variability of the greenness pattern and its congruence with plant diversity. Full article

(This article belongs to the Special Issue Earth Observation in Forest Biophysical/Biochemical Parameter Retrieval)

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27 pages, 8593 KiB

Open AccessArticle

Spaceborne Multifrequency PolInSAR-Based Inversion Modelling for Forest Height Retrieval

by Shashi Kumar, Himanshu Govil, Prashant K. Srivastava, Praveen K. Thakur and Satya P. S. Kushwaha

Remote Sens. 2020, 12(24), 4042; https://doi.org/10.3390/rs12244042 - 10 Dec 2020

Cited by 16 | Viewed by 4217

Abstract

Spaceborne and airborne polarimetric synthetic-aperture radar interferometry (PolInSAR) data have been extensively used for forest parameter retrieval. The PolInSAR models have proven their potential in the accurate measurement of forest vegetation height. Spaceborne monostatic multifrequency data of different SAR missions and the Global Ecosystem Dynamics Investigation (GEDI)-derived forest canopy height map were used in this study for vegetation height retrieval. This study tested the performance of PolInSAR complex coherence-based inversion models for estimating the vegetation height of the forest ranges of Doon Valley, Uttarakhand, India. The inversion-based forest height obtained from the three-stage inversion (TSI) model had higher accuracy than the coherence amplitude inversion (CAI) model-based estimates. The vegetation height values of GEDI-derived canopy height map did not show good relation with field-measured forest height values. It was found that, at several locations, GEDI-derived forest height values underestimated the vegetation height. The statistical analysis of the GEDI-derived estimates with field-measured height showed a high root mean square error (RMSE; 5.82 m) and standard error (SE; 5.33 m) with a very low coefficient of determination (R²; 0.0022). An analysis of the spaceborne-mission-based forest height values suggested that the L-band SAR has great potential in forest height retrieval. TSI-model-based forest height values showed lower p-values, which indicates the significant relation between modelled and field-measured forest height values. A comparison of the results obtained from different SAR systems is discussed, and it is observed that the L-band-based PolInSAR inversion gives the most reliable result with low RMSE (2.87 m) and relatively higher R² (0.53) for the linear regression analysis between the modelled tree height and the field data. These results indicate that higher wavelength PolInSAR datasets are more suitable for tree canopy height estimation using the PolInSAR inversion technique. Full article

(This article belongs to the Special Issue Earth Observation in Forest Biophysical/Biochemical Parameter Retrieval)

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18 pages, 1179 KiB

Open AccessArticle

Potential Lidar Height, Intensity, and Ratio Parameters for Plot Dominant Species Discrimination and Volume Estimation

by Taejin Park

Remote Sens. 2020, 12(19), 3266; https://doi.org/10.3390/rs12193266 - 8 Oct 2020

Cited by 3 | Viewed by 2975

Abstract

Precise stand species classification and volume estimation are key research topics for automated forest inventory. This study aims to explore the feasibility of light detection and ranging (lidar) height, intensity, and ratio parameters for discriminating dominant species (Pinus densiflora, Larix kaempferi, and Quercus spp.) and estimating volume at plot scale. To achieve these objectives, multiple linear discriminant and regression analyses were utilized after a separate selection of explanatory variables from extracted 38 lidar height, intensity, and ratio parameters. A kappa accuracy of 0.75 was achieved in discriminating the plot-dominant species from three different species by adopting a combination of nine selected explanatory variables. Further investigation found that dispersion and mean of lidar intensity within a plot are key classifiers of identifying three species. Species-specific optimal plot volume models for Pinus densiflora, Larix kaempferi, and Quercus spp. were evaluated by coefficients of determination of 0.71, 0.74, and 0.56, respectively. Compared to species classification, height-related lidar variables play a key role in modeling forest plot volume. Several explanatory variables for each modeling practice were correlated to canopy vertical and horizontal structures and were enough to represent species-specific characteristics in both approaches for species classification and plot volume estimation. Additionally, observed different variable combinations for two important applications imply that future studies should use proper variable combinations for each purpose. Full article

(This article belongs to the Special Issue Earth Observation in Forest Biophysical/Biochemical Parameter Retrieval)

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21 pages, 14980 KiB

Open AccessArticle

Assessing the Influence of UAV Altitude on Extracted Biophysical Parameters of Young Oil Palm

by Ram Avtar, Stanley Anak Suab, Mohd Shahrizan Syukur, Alexius Korom, Deha Agus Umarhadi and Ali P. Yunus

Remote Sens. 2020, 12(18), 3030; https://doi.org/10.3390/rs12183030 - 17 Sep 2020

Cited by 26 | Viewed by 6091

Abstract

The information on biophysical parameters—such as height, crown area, and vegetation indices such as the normalized difference vegetation index (NDVI) and normalized difference red edge index (NDRE)—are useful to monitor health conditions and the growth of oil palm trees in precision agriculture practices. The use of multispectral sensors mounted on unmanned aerial vehicles (UAV) provides high spatio-temporal resolution data to study plant health. However, the influence of UAV altitude when extracting biophysical parameters of oil palm from a multispectral sensor has not yet been well explored. Therefore, this study utilized the MicaSense RedEdge sensor mounted on a DJI Phantom–4 UAV platform for aerial photogrammetry. Three different close-range multispectral aerial images were acquired at a flight altitude of 20 m, 60 m, and 80 m above ground level (AGL) over the young oil palm plantation area in Malaysia. The images were processed using the structure from motion (SfM) technique in Pix4DMapper software and produced multispectral orthomosaic aerial images, digital surface model (DSM), and point clouds. Meanwhile, canopy height models (CHM) were generated by subtracting DSM and digital elevation models (DEM). Oil palm tree heights and crown projected area (CPA) were extracted from CHM and the orthomosaic. NDVI and NDRE were calculated using the red, red-edge, and near-infrared spectral bands of orthomosaic data. The accuracy of the extracted height and CPA were evaluated by assessing accuracy from a different altitude of UAV data with ground measured CPA and height. Correlations, root mean square deviation (RMSD), and central tendency were used to compare UAV extracted biophysical parameters with ground data. Based on our results, flying at an altitude of 60 m is the best and optimal flight altitude for estimating biophysical parameters followed by 80 m altitude. The 20 m UAV altitude showed a tendency of overestimation in biophysical parameters of young oil palm and is less consistent when extracting parameters among the others. The methodology and results are a step toward precision agriculture in the oil palm plantation area. Full article

(This article belongs to the Special Issue Earth Observation in Forest Biophysical/Biochemical Parameter Retrieval)

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18 pages, 3922 KiB

Open AccessArticle

Assessing the Performance of ICESat-2/ATLAS Multi-Channel Photon Data for Estimating Ground Topography in Forested Terrain

by Yanqiu Xing, Jiapeng Huang, Armin Gruen and Lei Qin

Remote Sens. 2020, 12(13), 2084; https://doi.org/10.3390/rs12132084 - 29 Jun 2020

Cited by 31 | Viewed by 4211

Abstract

As a continuation of Ice, Cloud, and Land Elevation Satellite-1 (ICESat-1), the ICESat-2/Advanced Topographic Laser Altimeter System (ATLAS) employs a micro-pulse multi-beam photon counting approach to produce photon data for measuring global terrain. Few studies have assessed the accuracy of different ATLAS channels in retrieving ground topography in forested terrain. This study aims to assess the accuracy of measuring ground topography in forested terrain using different ATLAS channels and the correlation between laser intensity parameters, laser pointing angle parameters, and elevation error. The accuracy of ground topography measured by the ATLAS footprints is evaluated by comparing the derived Digital Terrain Model (DTM) from the ATL03 (Global Geolocated Photon Data) and ATL08 (Land and Vegetation Height) products with that from the airborne Light Detection And Ranging (LiDAR). Results show that the ATLAS product performed well in the study area at all laser intensities and laser pointing angles, and correlations were found between the ATLAS DTM and airborne LiDAR DTM (coefficient of determination––R² = 1.00, root mean squared error––RMSE = 0.75 m). Considering different laser intensities, there is a significant correlation between the tx_pulse_energy parameter and elevation error. With different laser pointing angles, there is no significant correlation between the tx_pulse_skew_est, tx_pulse_width_lower, tx_pulse_width_upper parameters and the elevation error. Full article

(This article belongs to the Special Issue Earth Observation in Forest Biophysical/Biochemical Parameter Retrieval)

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21 pages, 5557 KiB

Open AccessArticle

Canopy Height Estimation Using Sentinel Series Images through Machine Learning Models in a Mangrove Forest

by Sujit Madhab Ghosh, Mukunda Dev Behera and Somnath Paramanik

Remote Sens. 2020, 12(9), 1519; https://doi.org/10.3390/rs12091519 - 9 May 2020

Cited by 56 | Viewed by 8908

Abstract

Canopy height serves as a good indicator of forest carbon content. Remote sensing-based direct estimations of canopy height are usually based on Light Detection and Ranging (LiDAR) or Synthetic Aperture Radar (SAR) interferometric data. LiDAR data is scarcely available for the Indian tropics, while Interferometric SAR data from commercial satellites are costly. High temporal decorrelation makes freely available Sentinel-1 interferometric data mostly unsuitable for tropical forests. Alternatively, other remote sensing and biophysical parameters have shown good correlation with forest canopy height. The study objective was to establish and validate a methodology by which forest canopy height can be estimated from SAR and optical remote sensing data using machine learning models i.e., Random Forest (RF) and Symbolic Regression (SR). Here, we analysed the potential of Sentinel-1 interferometric coherence and Sentinel-2 biophysical parameters to propose a new method for estimating canopy height in the study site of the Bhitarkanika wildlife sanctuary, which has mangrove forests. The results showed that interferometric coherence, and biophysical variables (Leaf Area Index (LAI) and Fraction of Vegetation Cover (FVC)) have reasonable correlation with canopy height. The RF model showed a Root Mean Squared Error (RMSE) of 1.57 m and R² value of 0.60 between observed and predicted canopy heights; whereas, the SR model through genetic programming demonstrated better RMSE and R² values of 1.48 and 0.62 m, respectively. The SR also established an interpretable model, which is not possible via any other machine learning algorithms. The FVC was found to be an essential variable for predicting forest canopy height. The canopy height maps correlated with ICESat-2 estimated canopy height, albeit modestly. The study demonstrated the effectiveness of Sentinel series data and the machine learning models in predicting canopy height. Therefore, in the absence of commercial and rare data sources, the methodology demonstrated here offers a plausible alternative for forest canopy height estimation. Full article

(This article belongs to the Special Issue Earth Observation in Forest Biophysical/Biochemical Parameter Retrieval)

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15 pages, 8706 KiB

Open AccessArticle

Forest Height Estimation Based on P-Band Pol-InSAR Modeling and Multi-Baseline Inversion

by Xiaofan Sun, Bingnan Wang, Maosheng Xiang, Liangjiang Zhou and Shuai Jiang

Remote Sens. 2020, 12(8), 1319; https://doi.org/10.3390/rs12081319 - 22 Apr 2020

Cited by 9 | Viewed by 2587

Abstract

The Gaussian vertical backscatter (GVB) model has a pivotal role in describing the forest vertical structure more accurately, which is reflected by P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) with strong penetrability. The model uses a three-dimensional parameter space (forest height, Gaussian mean representing the strongest backscattered power elevation, and the corresponding standard deviation) to interpret the forest vertical structure. This paper establishes a two-dimensional GVB model by simplifying the three-dimensional one. Specifically, the two-dimensional GVB model includes the following three cases: the Gaussian mean is located at the bottom of the canopy, the Gaussian mean is located at the top of the canopy, as well as a constant volume profile. In the first two cases, only the forest height and the Gaussian standard deviation are variable. The above approximation operation generates a two-dimensional volume only coherence solution space on the complex plane. Based on the established two-dimensional GVB model, the three-baseline inversion is achieved without the null ground-to-volume ratio assumption. The proposed method improves the performance by 18.62% compared to the three-baseline Random Volume over Ground (RVoG) model inversion. In particular, in the area where the radar incidence angle is less than 0.6 rad, the proposed method improves the inversion accuracy by 34.71%. It suggests that the two-dimensional GVB model reduces the GVB model complexity while maintaining a strong description ability. Full article

(This article belongs to the Special Issue Earth Observation in Forest Biophysical/Biochemical Parameter Retrieval)

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25 pages, 7373 KiB

Open AccessArticle

Use of Hyperion for Mangrove Forest Carbon Stock Assessment in Bhitarkanika Forest Reserve: A Contribution Towards Blue Carbon Initiative

by Akash Anand, Prem Chandra Pandey, George P. Petropoulos, Andrew Pavlides, Prashant K. Srivastava, Jyoti K. Sharma and Ramandeep Kaur M. Malhi

Remote Sens. 2020, 12(4), 597; https://doi.org/10.3390/rs12040597 - 11 Feb 2020

Cited by 51 | Viewed by 7103

Abstract

Mangrove forest coastal ecosystems contain significant amount of carbon stocks and contribute to approximately 15% of the total carbon sequestered in ocean sediments. The present study aims at exploring the ability of Earth Observation EO-1 Hyperion hyperspectral sensor in estimating aboveground carbon stocks in mangrove forests. Bhitarkanika mangrove forest has been used as case study, where field measurements of the biomass and carbon were acquired simultaneously with the satellite data. The spatial distribution of most dominant mangrove species was identified using the Spectral Angle Mapper (SAM) classifier, which was implemented using the spectral profiles extracted from the hyperspectral data. SAM performed well, identifying the total area that each of the major species covers (overall kappa = 0.81). From the hyperspectral images, the NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) were applied to assess the carbon stocks of the various species using machine learning (Linear, Polynomial, Logarithmic, Radial Basis Function (RBF), and Sigmoidal Function) models. NDVI and EVI is generated using covariance matrix based band selection algorithm. All the five machine learning models were tested between the carbon measured in the field sampling and the carbon estimated by the vegetation indices NDVI and EVI was satisfactory (Pearson correlation coefficient, R, of 86.98% for EVI and of 84.1% for NDVI), with the RBF model showing the best results in comparison to other models. As such, the aboveground carbon stocks for species-wise mangrove for the study area was estimated. Our study findings confirm that hyperspectral images such as those from Hyperion can be used to perform species-wise mangrove analysis and assess the carbon stocks with satisfactory accuracy. Full article

(This article belongs to the Special Issue Earth Observation in Forest Biophysical/Biochemical Parameter Retrieval)

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