scholarly journals Classification and Observed Seasonal Phenology of Broadleaf Deciduous Forests in a Tropical Region by Using Multitemporal Sentinel-1A and Landsat 8 Data

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 235
Author(s):  
Anh Tuan Tran ◽  
Kim Anh Nguyen ◽  
Yuei An Liou ◽  
Minh Hang Le ◽  
Van Truong Vu ◽  
...  
Keyword(s):  
Forest Management ◽  
Large Scale ◽  
Vegetation Index ◽  
Deciduous Forest ◽  
Forest Type ◽  
Deciduous Forests ◽  
Tropical Region ◽  
Landsat 8 ◽  
Tropical Regions ◽  
Dipterocarp Forests

Broadleaf deciduous forests (BDFs) or dry dipterocarp forests play an important role in biodiversity conservation in tropical regions. Observations and classification of forest phenology provide valuable inputs for ecosystem models regarding its responses to climate change to assist forest management. Remotely sensed observations are often used to derive the parameters corresponding to seasonal vegetation dynamics. Data acquired from the Sentinel-1A satellite holds a great potential to improve forest type classification at a medium-large scale. This article presents an integrated object-based classification method by using Sentinel-1A and Landsat 8 OLI data acquired during different phenological periods (rainy and dry seasons). The deciduous forest and nondeciduous forest areas are classified by using NDVI (normalized difference vegetation index) from Landsat 8 cloud-free composite images taken during dry (from February to April) and rainy (from June to October) seasons. Shorea siamensis Miq. (S. siamensis), Shorea obtusa Wall. ex Blume (S. obtusa), and Dipterocarpus tuberculatus Roxb. (D. tuberculatus) in the deciduous forest area are classified based on the correlation between phenology of BDFs in Yok Don National Park and backscatter values of time-series Sentinel-1A imagery in deciduous forest areas. One hundred and five plots were selected during the field survey in the study area, consisting of dominant deciduous species, tree height, and canopy diameter. Thirty-nine plots were used for training to decide the broadleaf deciduous forest areas of the classified BDFs by the proposed method, and the other sixty-six plots were used for validation. Our proposed approach used the changes of backscatter in multitemporal SAR images to implement BDF classification mapping with acceptable accuracy. The overall accuracy of classification is about 79%, with a kappa coefficient of 0.7. Accurate classification and mapping of the BDFs using the proposed method can help authorities implement forest management in the future.

scholarly journals Evaluation of Sensor and Environmental Factors Impacting the Use of Multiple Sensor Data for Time-Series Applications

2018 ◽  
Vol 10 (11) ◽  
pp. 1678 ◽  
Author(s):  
Rajagopalan Rengarajan ◽  
John Schott
Keyword(s):  
Time Series ◽  
Forest Canopy ◽  
Vegetation Index ◽  
Deciduous Forest ◽  
Spectral Response ◽  
Sensor Data ◽  
Landsat 8 ◽  
Real World Data ◽  
Large Variability ◽  
Atmospheric Compensation

Many remote sensing sensors operate in similar spatial and spectral regions, which provides an opportunity to combine the data from different sensors to increase the temporal resolution for short and long-term trend analysis. However, combining the data requires understanding the characteristics of different sensors and presents additional challenges due to their variation in operational strategies, sensor differences and environmental conditions. These differences can introduce large variability in the time-series analysis, limiting the ability to model, predict and separate real change in signal from noise. Although the research community has identified the factors that cause variations, the magnitude or the effect of these factors have not been well explored and this is due to the limitations with the real-world data, where the effects of the factors cannot be separated. Our work mitigates these shortcomings by simulating the surface, atmosphere, and sensors in a virtual environment. We modeled and characterized a deciduous forest canopy and estimated its at-sensor response for the Landsat 8 (L8) and Sentinel 2 (S2) sensors using the MODerate resolution atmospheric TRANsmission (MODTRAN) modeled atmosphere. This paper presents the methods, analysis and the sensitivity of the factors that impacts multi-sensor observations for temporal analysis. Our study finds that atmospheric compensation is necessary as the variation due to the atmosphere can introduce an uncertainty as high as 40% in the Normalized Difference Vegetation Index (NDVI) products used in change detection and time-series applications. The effect due to the differences in the Relative Spectral Response (RSR) of the two sensors, if not compensated, can introduce uncertainty as high as 20% in the NDVI products. The view angle differences between the sensors can introduce uncertainty anywhere from 9% to 40% in NDVI depending on the atmospheric compensation methods. For a difference of 5 days in acquisition, the effect of solar zenith angle can vary between 4% to 10%, depending on whether the atmospheric attenuations are compensated or not for the NDVI products.

scholarly journals Land Cover and Forest Type Classification by Values of Vegetation Indices and Forest Structure of Tropical Lowland Forests in Central Vietnam

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Hung Nguyen Trong ◽  
The Dung Nguyen ◽  
Martin Kappas
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Forest Type ◽  
Vegetation Indices ◽  
Landsat 8 ◽  
Tropical Lowland ◽  
Central Vietnam ◽  
Lowland Forests ◽  
Type Classification ◽  
Sentinel 2

This paper aims to (i) optimize the application of multiple bands of satellite images for land cover classification by using random forest algorithms and (ii) assess correlations and regression of vegetation indices of a better-performed land cover classification image with vertical and horizontal structures of tropical lowland forests in Central Vietnam. In this study, we used Sentinel-2 and Landsat-8 to classify seven land cover classes of which three forest types were substratified as undisturbed, low disturbed, and disturbed forests where forest inventory of 90 plots, as ground-truth, was randomly sampled to measure forest tree parameters. A total of 3226 training points were sampled on seven land cover types. The performance of Landsat-8 showed out-of-bag error of 31.6%, overall accuracy of 68%, kappa of 67.5%, while Sentinel-2 showed out-of-bag error of 14.3% and overall accuracy of 85.7% and kappa of 83%. Ten vegetation indices of the better-performed image were extracted to find out (i) the correlation and regression of horizontal and vertical structures of trees and (ii) assess the variation values between ground-truthing plots and training sample plots in three forest types. The result of the t test on vegetation indices showed that six out of ten vegetation indices were significant at p<0.05. Seven vegetation indices had a correlation with the horizontal structure, but four vegetation indices, namely, Enhanced Vegetation Index, Perpendicular Vegetation Index, Difference Vegetation Index, and Transformed Normalized Difference Vegetation Index, had better correlations r = 0.66, 0.65, 0.65, 0.63 and regression results were of R2 = 0.44, 0.43, 0.43, and 0.40, respectively. The correlations of tree height were r = 0.46, 0.43, 0.43, and 0.49 and its regressions were of R2 = 0.21, 0.19, 0.18, and 0.24, respectively. The results show the possibility of using random forest algorithm with Sentinel-2 in forest type classification in line with vegetation indices application.

scholarly journals Chlorophyll Changes Using Neural Network and Vegetation Indices in Tropical deciduous forest

2020 ◽  
Author(s):  
Saurabh Kumar Gupta ◽  
Arvind Chandra Pandey
Keyword(s):  
Neural Network ◽  
Vegetation Index ◽  
Deciduous Forest ◽  
Plant Stress ◽  
Vegetation Indices ◽  
Radiative Transfer Model ◽  
Tropical Deciduous Forest ◽  
Transfer Model ◽  
Landsat 8 ◽  
Spatio Temporal

Abstract Background: Ongoing climate and Earth’s atmosphere changes create profound effect on distribution and composition of forest, as well as on the fauna that depends on forest. The Sentinel-2A satellite data eases the mapping of Leaf Chlorophyll Content (LCC) at higher spatial and temporal resolution. In the present study, the temporal dimension of LCC was evaluated as an indicator of plant stress. LCC was retrieved using the inversion of the radiative transfer model based on an artificial neural network. The data used for Spatio-temporal modelling of LCC was Landsat data.Result: From the Sentinel imagery derived vegetation indices, it was found that the narrowband indices having high correlation with LCC were pigment specific simple ratio and normalized difference index (45) (R2 > 0.7; p < 0.001) centred at 665 nm, 705 nm, and 740 nm. Landsat 8 infrared percentage vegetation index had a strong relationship with LCC (R2 =0.8). The Spatio-temporal (1997 to 2017) plant stress were detected using changes in LCC through an equation of correlation. The negative changes and deterioration of LCC were seen in the forest during the year 1997 to 20I7(rate = -1.2 µgcm-2year-1) showing higher rate of forest health decline. Conclusion: The 33% of plant stress increased currently in the protected forest mainly because of anthropogenic influences. These vast decline in the chlorophyll gives rise to various photosynthetic vulnerabilities in forest ecosystem and indirectly affects human including wildlife.

scholarly journals Extracting Coastal Raft Aquaculture Data from Landsat 8 OLI Imagery

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1221 ◽  
Author(s):  
Jun Wang ◽  
Lichun Sui ◽  
Xiaomei Yang ◽  
Zhihua Wang ◽  
Yueming Liu ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Eastern China ◽  
Chlorophyll Concentration ◽  
False Negative ◽  
Distribution Data ◽  
Landsat 8 ◽  
Negative Results

Information, especially spatial distribution data, related to coastal raft aquaculture is critical to the sustainable development of marine resources and environmental protection. Commercial high spatial resolution satellite imagery can accurately locate raft aquaculture. However, this type of analysis using this expensive imagery requires a large number of images. In contrast, medium resolution satellite imagery, such as Landsat 8 images, are available at no cost, cover large areas with less data volume, and provide acceptable results. Therefore, we used Landsat 8 images to extract the presence of coastal raft aquaculture. Because the high chlorophyll concentration of coastal raft aquaculture areas cause the Normalized Difference Vegetation Index (NDVI) and the edge features to be salient for the water background, we integrated these features into the proposed method. Three sites from north to south in Eastern China were used to validate the method and compare it with our former proposed method using only object-based visually salient NDVI (OBVS-NDVI) features. The new proposed method not only maintains the true positive results of OBVS-NDVI, but also eliminates most false negative results of OBVS-NDVI. Thus, the new proposed method has potential for use in rapid monitoring of coastal raft aquaculture on a large scale.

scholarly journals Global Vegetation Photosynthetic Phenology Products Based on MODIS Vegetation Greenness and Temperature: Modeling and Evaluation

2021 ◽  
Vol 13 (24) ◽  
pp. 5080
Author(s):  
Xiaojun Xu ◽  
Yan Tang ◽  
Yiling Qu ◽  
Zhongsheng Zhou ◽  
Junguo Hu
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Deciduous Forest ◽  
Forest Type ◽  
Gross Primary Production ◽  
Plant Functional Types ◽  
Linear Regression Models ◽  
Climatic Regions ◽  
Functional Types ◽  
Global Vegetation

Land surface phenology (LSP) products that are derived from different data sources have different definitions and biophysical meanings. Discrepancies among these products and their linkages with carbon fluxes across plant functional types and climatic regions remain somewhat unclear. In this study, to differentiate LSP related to gross primary production (GPP) from LSP related to remote sensing data, we defined the former as vegetation photosynthetic phenology (VPP), including the starting and ending days of GPP (SOG and EOG, respectively). Specifically, we estimated VPP based on a combination of observed VPP from 145 flux-measured GPP sites together with the vegetation index and temperature data from MODIS products using multiple linear regression models. We then compared VPP estimates with MODIS LSP on a global scale. Our results show that the VPP provided better estimates of SOG and EOG than MODIS LSP, with a root mean square error (RMSE) for SOG of 12.7 days and a RMSE for EOG of 10.5 days. The RMSE was approximately three weeks for both SOG and EOG estimates of the non-forest type. Discrepancies between VPP and LSP estimates varied across plant functional types (PFTs) and climatic regions. A high correlation was observed between VPP and LSP estimates for deciduous forest. For most PFTs, using VPP estimates rather than LSP improved the estimation of GPP. This study presents a useful method for modeling global VPP, investigates in detail the discrepancies between VPP and LSP, and provides a more effective global vegetation phenology product for carbon cycle modeling than the existing ones.

The synergistic use of Sentinel SAR and optical remote sensing for mapping high-resolution soil moisture

2020 ◽  
Author(s):  
Jianxiu Qiu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Large Scale ◽  
Vegetation Index ◽  
Estimation Accuracy ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Biological Variables ◽  
Research Activities

&lt;p&gt;The launch of series of Sentinel constellations has provided data continuity of ERS, Envisat, and SPOT-like observations, in order to meet various observational needs for spatially explicit physical, biogeophysical, and biological variables of the ocean, cryosphere, and land research activities. The synergistic use of this publicly-accessible SAR images and temporally collocated optical remote sensing datasets has provided great potential for estimating high-resolution soil moisture information. In this study, advanced integral equation model (AIEM) which simulates the backscattering coefficient of bare soil and the Water-Cloud Model (WCM) accounting for the scattering effect from vegetation, are coupled to map high-resolution soil moisture. Validation conducted in large-scale campaign of Heihe Watershed Allied Telemetry Experimental Research (HiWATER-MUSOEXE) in northwest of China showed RMSE of 0.04~0.071 m3m3. In addition, the accuracies in describing vegetation contribution from backscatter coefficient were intercompared between different models including WCM and ratio vegetation model. Sensitivity analysis of soil moisture estimation accuracy to vegetation index also extends to different optical remote sensing data sets including Sentinel-2, Landsat 8 and MODIS.&lt;/p&gt;

scholarly journals MAPPING AND MODELLING ABOVEGROUND WOODY BIOMASS AND CARBON STOCK IN SAL (<i>SHOREA ROBUSTA</i> GAERTN. F.) FORESTS OF DOON VALLEY USING GEOSPATIAL TECHNIQUES

2018 ◽  
Vol XLII-5 ◽  
pp. 877-882
Author(s):  
S. Purohit ◽  
S. P. Aggarwal ◽  
N. R. Patel
Keyword(s):  
Forest Management ◽  
Forest Type ◽  
Woody Biomass ◽  
Exponential Model ◽  
Measured Data ◽  
Landsat 8 ◽  
Prediction Errors ◽  
Forest Management Planning ◽  
Doon Valley ◽  
Shorea Robusta

<p><strong>Abstract.</strong> Information on the quantitative and qualitative distribution of forest biomass is helpful for effective forest management. Besides its quantitative use, Biomass plays a twin role by acting as a carbon source and sinks but its long-term carbon-storing ability is of considerable importance which is helpful in lessening global warming and climate change impacts. The present study was done for mapping aboveground woody biomass (Bole) (AGWB) of <i>Shorea robusta</i> (Gaertn.f.) forests in Doon valley by establishing relationships between field measured data, satellite data derived variables and geostatistical techniques. Landsat 8 Operational Land Imager (OLI) data was used in preparing the forest homogeneity map (forest type and density). 55 sampling plots of 0.1<span class="thinspace"></span>ha were laid across the Doon Valley using stratified random sampling. Correlations were established between Landsat 8 OLI derived variables and field measured data and were evaluated. Field measured biomass has got the maximum correlation with NDVI (0.7553) and it was further used for carrying out multivariate kriging (Cok) for biomass prediction map. Prediction errors for the AGWB were lowest for exponential model with RMSE<span class="thinspace"></span>=<span class="thinspace"></span>66.445<span class="thinspace"></span>Mg/ha, Average Standard Error<span class="thinspace"></span>=<span class="thinspace"></span>71.07694<span class="thinspace"></span>Mg/ha and RMSS<span class="thinspace"></span>=<span class="thinspace"></span>0.95097. Carbon is calculated as 47% of the biomass value.AGWB was ranged from 163.381 to 750.025 Mg/ha and Carbon from 76.789 to 352.512<span class="thinspace"></span>Mg/ha. Cokriging was found as a better alternative as compared to direct radiometric relationships for the spatial distribution of the AGWB of <i>Shorea robusta</i> (Gaertn.f.) forests and this study would be helpful in better forest management planning and research purposes.</p>

scholarly journals Multitemporal Landsat Imagery with Optimum Band Ratio Techniques for Deciduous Forest Classification

2021 ◽  
pp. 21-27
Author(s):  
W Paengwangthong
Keyword(s):  
Deciduous Forest ◽  
Forest Type ◽  
Landsat Imagery ◽  
Kappa Coefficient ◽  
Landsat 8 ◽  
Data Sets ◽  
Band Ratio ◽  
Data Set ◽  
Multitemporal Data ◽  
Type Classification

The objective of the study is to evaluate optimum band ratio combinations data set derived from monthly Landsat 8 imageries for forest type classification around the Sirikit dam reservoir using supervised classification with Maximum Likelihood Classifier (MLC). In this study, imageries data acquired from January 2014 to November 2017 were used to create the monthly band ratio data set of Normalized Difference Vegetation Indices (NDVI), Normalized Difference Moisture Indices (NDMI), and Normalized Burn Ratios (NBR) and used to create the monthly multispectral (MS) data set represented as a case of without applying band ratio techniques. In classifying deciduous forest type, four data sets were used to classify two classes of deciduous forests, namely mixed deciduous forest and dry deciduous dipterocarp forest. After the accuracy assessment, the result showed that the overall accuracy and kappa coefficient of all data sets were between 78.33% – 86.21% and between 44.32% – 62.83%, respectively. Herein, the monthly NDVI multitemporal data set provided the highest overall accuracy and kappa coefficient which were better than the monthly MS multitemporal data set about 4% and 8%, respectively. In conclusion, applying monthly multitemporal data of Landsat 8 with band ratio technique, especially NDVI, can increase the accuracy of deciduous forest type classification.

scholarly journals Soil moisture index estimation from landsat 8 images for prediction and monitoring landslide occurrences in Ulu Kelang, Selangor, Malaysia

2021 ◽  
Vol 11 (3) ◽  
pp. 2101
Author(s):  
Noraisyah Tajudin ◽  
Norsuzila Ya'acob ◽  
Darmawaty Mohd Ali ◽  
Nor Aizam Adnan
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Large Scale ◽  
Vegetation Index ◽  
Landsat 8 ◽  
Northeast Monsoon ◽  
Contributing Factors ◽  
Moisture Index ◽  
Rainfall Distribution ◽  
Rainfall Analysis

Soil moisture is one of the contributing factors that accelerates soil erosion and landslide events due to the increase in pore pressure which eventually reduces the soil strength. For landslide prediction and monitoring purposes, large-scale measurement involves estimating the soil moisture. However, estimation of soil moisture usually involves point-based measurements at a particular site and time, which is difficult to capture the spatial and temporal soil moisture dynamics. This paper presents the estimation of the SMI using Landsat 8 images for prediction and monitoring of landslide events in Ulu Kelang, Selangor. The selected SMI map for dry, moist, and wet seasons are obtained from climatology rainfall analysis over 20-year periods (1998-2017). SMI is assessed based on remote sensing data which are land surface temperature (LST) and normalized difference vegetation index (NDVI) using GIS software. Overall results indicated that rainfall distribution is high during inter-monsoon (IM), followed by northeast monsoon (NEM) and southwest monsoon (SWM) season. High rainfall distribution is a direct contributor towards SMI condition. Results from simulation show that April 2017 is known to have the highest SMI estimation season and selected to be the best SMI mapping parameter to be applied for prediction and monitoring of landslide events.

scholarly journals PHENOLOGY ANALYSIS OF FOREST VEGETATION TO ENVIRONMENTAL VARIABLES DURING PRE- AND POST-MONSOON SEASONS IN WESTERN HIMALAYAN REGION OF INDIA

2016 ◽  
Vol XLI-B2 ◽  
pp. 15-19
Author(s):  
S. Khare ◽  
H. Latifi ◽  
K. Ghosh
Keyword(s):  
Land Surface ◽  
Environmental Variables ◽  
Large Scale ◽  
Vegetation Index ◽  
Linear Regression Analysis ◽  
Himalayan Region ◽  
Landsat 8 ◽  
Post Monsoon ◽  
Western Himalayan Region ◽  
Landsat 7

To assess the phenological changes in Moist Deciduous Forest (MDF) of western Himalayan region of India, we carried out NDVI time series analysis from 2013 to 2015 using Landsat 8 OLI data. We used the vegetation index differencing method to calculate the change in NDVI (NDVI<sub>change</sub>) during pre and post monsoon seasons and these changes were used to assess the phenological behaviour of MDF by taking the effect of a set of environmental variables into account. To understand the effect of environmental variables on change in phenology, we designed a linear regression analysis with sample-based NDVI<sub>change</sub> values as the response variable and elevation aspect, and Land Surface Temperature (LST) as explanatory variables. The Landsat-8 derived phenology transition stages were validated by calculating the phenology variation from Nov 2008 to April 2009 using Landsat-7 which has the same spatial resolution as Landsat-8. The Landsat-7 derived NDVI trajectories were plotted in accordance with MODIS derived phenology stages (from Nov 2008 to April 2009) of MDF. Results indicate that the Landsat -8 derived NDVI trajectories describing the phenology variation of MDF during spring, monsoon autumn and winter seasons agreed closely with Landsat-7 and MODIS derived phenology transition from Nov 2008 to April 2009. Furthermore, statistical analysis showed statistically significant correlations (p < 0.05) amongst the environmental variables and the NDVI<sub>change</sub> between full greenness and maximum frequency stage of Onset of Greenness (OG) activity.. The major change in NDVI was observed in medium (600 to 650 m) and maximum (650 to 750 m) elevation areas. The change in LST showed also to be highly influential. The results of this study can be used for large scale monitoring of difficult-to-reach mountainous forests, with additional implications in biodiversity assessment. By means of a sufficient amount of available cloud-free imagery, detailed phenological trends across mountainous forests could be explained.

Sign in / Sign up

Export Citation Format

Share Document