scholarly journals Recognition and Characterization of Forest Plant Communities through Remote-Sensing NDVI Time Series

Diversity ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 313 ◽  
Author(s):  
Simone Pesaresi ◽  
Adriano Mancini ◽  
Simona Casavecchia
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Plant Communities ◽  
Remotely Sensed ◽  
Functional Principal Component Analysis ◽  
Floristic Similarity ◽  
Plant Associations ◽  
Ndvi Time Series ◽  
Functional Components

Phytosociology is a reference method to classify vegetation that relies on field data. Its classification in hierarchical vegetation units, from plant associations to class level, hierarchically reflects the floristic similarity between different sites on different spatial scales. The development of remotely sensed multispectral platforms as satellites enormously contributes to the detection and mapping of vegetation on all scales. However, the integration between phytosociology and remotely sensed data is rather difficult and little practiced despite being a goal for the modern science of vegetation. In this study, we demonstrate how normalized difference vegetation index (NDVI) time series with functional principal component analysis (FPCA) could support the analyses of phytosociologists. The approach supports the recognition and characterization of forest plant communities identified on the ground by the phytosociological approach by using NDVI time series that encode phenological behaviors. The methodology was evaluated in two study areas of central Italy, and it could characterize and discriminate six different forest plant associations that have similar dominant tree species but distinct specific composition: three dominated by black hornbeam (Ostrya carpinifolia) and three dominated by holm oak (Quercus ilex). The methodology was also able to optimize the ground data collection of unexplored areas (from a phytosociological point of view) by using a phenoclustering approach. The obtained results confirmed that by using remote sensing, it is possible to separate and distinguish plant communities in an objective/instrumental way, thus overcoming the subjectivity intrinsic to the phytosociological method. In particular, FPCA functional components (NDVI seasonalities) were significantly correlated with vegetation abundance data variation (Mantel r = 0.76, p < 0.001).

scholarly journals Mapping Mediterranean Forest Plant Associations and Habitats with Functional Principal Component Analysis Using Landsat 8 NDVI Time Series

2020 ◽  
Vol 12 (7) ◽  
pp. 1132 ◽  
Author(s):  
Simone Pesaresi ◽  
Adriano Mancini ◽  
Giacomo Quattrini ◽  
Simona Casavecchia
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Principal Component ◽  
Component Analysis ◽  
Functional Principal Component Analysis ◽  
Mediterranean Forest ◽  
Plant Associations ◽  
Functional Principal Component ◽  
Ndvi Time Series

The classification of plant associations and their mapping play a key role in defining habitat biodiversity management, monitoring, and conservation strategies. In this work we present a methodological framework to map Mediterranean forest plant associations and habitats that relies on the application of the Functional Principal Component Analysis (FPCA) to the remotely sensed Normalized Difference Vegetation Index (NDVI) time series. FPCA, considering the chronological order of the data, reduced the NDVI time series data complexity and provided (as FPCA scores) the main seasonal NDVI phenological variations of the forests. We performed a supervised classification of the FPCA scores combined with topographic and lithological features of the study area to map the forest plant associations. The supervised mapping achieved an overall accuracy of 87.5%. The FPCA scores contributed to the global accuracy of the map much more than the topographic and lithological features. The results showed that (i) the main seasonal phenological variations (FPCA scores) are effective spatial predictors to obtain accurate plant associations and habitat maps; (ii) the FPCA is a suitable solution to simultaneously express the relationships between remotely sensed and ecological field data, since it allows us to integrate these two different perspectives about plant associations in a single graph. The proposed approach based on the FPCA is useful for forest habitat monitoring, as it can contribute to produce periodically detailed vegetation-based habitat maps that reflect the “current” status of vegetation and habitats, also supporting the study of plant associations.

The Vegetation Remote Sensing Phenology of Qinling Mountains Based on the NDVI and the Response of Temperature to it

2014 ◽  
Vol 700 ◽  
pp. 394-399 ◽  
Author(s):  
Xin Ping Ma ◽  
Hong Ying Bai ◽  
Ying Na He ◽  
Shu Heng Li
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Phenology ◽  
Qinling Mountains ◽  
Threshold Method ◽  
Climate Change Research ◽  
Phenological Data ◽  
Growing Period ◽  
Macro Scale ◽  
Ndvi Time Series

The acquisition vegetation phenology information by using time series of satellite data is an important aspect of the application of remote sensing and climate change research . Based on the MODOS NDVI time series of images in 2000-2010, Dynamic threshold method and GIS tools were used to extract the vegetation phenology parameters of Qinling Mountains in 2000-2010 , the accuracy of remote sensing phenology results was verified combined with the measured phenological data, And analyzed the characteristis of phenological variation and the relationship between temperature changes and the phenology of Qinling region,and quantified the extent of temperature change on vegetation phenology in a macro scale. Calculated :the trend of vegetation phenology variation based on the NDVI and the results of phenological data are consistent. Results show that NDVI has good revealed effect on vegetation phenology; From 2000 to 2010,it ahead of 1.8 days at the beginning period of vegetation phenology and late back 1.2 days at the end period ; The start phenology NDVI was generally greater than the late phenology on spatial distribution; The effective temperatures and the temperature in spring, growing period had a maximum influence on NDVI at beginning phenology period,the temperatures in summer and autumn had greater impact on the final NDVI .

scholarly journals Phenological Characterization of Desert Sky Island Vegetation Communities with Remotely Sensed and Climate Time Series Data

2010 ◽  
Vol 2 (2) ◽  
pp. 388-415 ◽  
Author(s):  
Willem J.D. Van Leeuwen ◽  
Jennifer E. Davison ◽  
Grant M. Casady ◽  
Stuart E. Marsh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Remotely Sensed ◽  
Series Data ◽  
Vegetation Communities ◽  
Climate Time Series ◽  
Sky Island

Assessing and monitoring the vulnerability to drought and climate anomalies of Mediterranenan oak forests by using NDVI

2020 ◽  
Author(s):  
Maria Castellaneta ◽  
Angelo Rita ◽  
J. Julio Camarero ◽  
Michele Colangelo ◽  
Angelo Nolè ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Tree Mortality ◽  
Vegetation Index ◽  
Google Earth ◽  
Oak Forests ◽  
Summer Drought ◽  
Area Index ◽  
Ndvi Time Series ◽  
The Impact

&lt;p&gt;Several die-off episodes related to heat weaves and drought spells have evidenced the high vulnerability of Mediterranean oak forests. These events consisted in the loss in tree vitality and manifested as growths decline, elevated crown transparency (defoliation) and rising tree mortality rate. In this context, the changes in vegetation productivity and canopy greenness may represent valuable proxies to analyze how extreme climatic events trigger forest die-off. Such changes in vegetation status may be analyzed using remote-sensing data, specifically multi-temporal spectral information. For instance, the Normalized Difference Vegetation Index (NDVI) measures changes in vegetation greenness and is a proxy of changes in leaf area index (LAI), forest aboveground biomass and productivity. In this study, we analyzed the temporal patterns of vegetation in three Mediterranean oak forests showing recent die-off in response to the 2017 severe summer drought. For this purpose, we used an open-source platform (Google Earth Engine) to extract collections of MODIS NDVI time-series from 2000 to 2019. The analysis of both NDVI trends and anomalies were used to infer differential patterns of vegetation phenology among sites comparing plots where most trees were declining and showed high defoliation (test) versus plots were most trees were considered healthy (ctrl) and showed low or no defoliation. Here we discuss: i) the likely offset in NDVI time-series between test- versus ctrl- sites; and ii) the impact of summer droughts &amp;#160;on NDVI.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords&lt;/strong&gt;: climate change, forest vulnerability, time series, remote sensing.&lt;/p&gt;

Monitoring of Sugarcane Growth Based on the Fused Remote Sensing NDVI Series and Ground Seeding Survey

2020 ◽  
Vol 63 (6) ◽  
pp. 1795-1804
Author(s):  
Yanli Chen ◽  
Weihua Mo ◽  
Jianfei Mo ◽  
Meihua Ding
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Southern China ◽  
Growth Monitoring ◽  
List Type ◽  
Fusion Model ◽  
Growth Status ◽  
Modis Data ◽  
Timing Data ◽  
Ndvi Time Series

HighlightsThe spatial and temporal fusion model ESTARFM was used to obtain NDVI timing data with high fusion accuracy and high spatial and temporal resolution.High-quality NDVI timing data could be obtained by using ESTARFM to fuse HJ-1 CCD and MODIS data.Fused NDVI data coupled with ground seeding survey data could effectively monitor sugarcane growth status.Abstract. This study addressed the instability of clear-sky remote sensing data with high spatial resolution in sugarcane growing areas in southern China and the current inconsistency between traditional survey results and remote sensing results for seedling growth. Moderate-resolution imaging spectroradiometer (MODIS) data and China land resources satellite (HJ-1 CCD) data were used to build high-resolution normalized difference vegetation index (NDVI) time series using the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM). Agronomic indicators of sugarcane were obtained by field sampling and were used for determining the remote sensing monitoring index (NDVI) of sugarcane growth. The method provided satisfactory results for evaluating sugarcane growth, with accuracy exceeding 90%. Moreover, sugarcane growth monitoring in a wider area was highly correlated with yield per unit area. Keywords: Growth status, HJ-1 CCD, MODIS, NDVI time series, Spatial and temporal fusion, Sugarcane.

scholarly journals Optimal Estimate of Global Biome—Specific Parameter Settings to Reconstruct NDVI Time Series with the Harmonic ANalysis of Time Series (HANTS) Method

2021 ◽  
Vol 13 (21) ◽  
pp. 4251
Author(s):  
Jie Zhou ◽  
Li Jia ◽  
Massimo Menenti ◽  
Xuan Liu
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Harmonic Analysis ◽  
Remote Sensing Data ◽  
Atmospheric Conditions ◽  
Reconstruction Performance ◽  
Ndvi Time Series ◽  
Harmonic Components ◽  
Analysis Of Time Series ◽  
Optimal Settings

Terrestrial remote sensing data products retrieved from radiometric measurements in the optical and thermal infrared spectrum such as vegetation spectral indices can be heavily contaminated by atmospheric conditions, including cloud and aerosol layers. This contamination results in gaps or noisy observations. The harmonic analysis of time series (HANTS) has been widely used for time series reconstruction of remote sensing imagery in recent decades. To use HANTS model, a series of parameters, such as number of frequencies (NF), fitting error tolerance (FET), degree of over-determinedness (DoD), and regularization factor (Delta), need to be defined by users. These parameters provide flexibilities, but also make it difficult for non-expert users to determine appropriate settings for specific applications. This study systematically evaluated the reconstruction performance of the model under different parameter setting scenarios by simulating pixel-wise reference and noisy NDVI time series. The results of these numerical experiments were further used to identify optimal settings and improve global NDVI reconstruction performance. The results suggested optimal settings for different areas (local optimization). If a user opts to use unique settings for global reconstruction, the setting NF = 4, FET = 0.05, DoD = 5, and Delta = 0.5 can produce the best performance across all setting scenarios (global optimization). In addition, several internal improvements, such as dynamic weighting scheme, polynomial and inter-annual harmonic components, and ancillary attributes of input data can be used to further improve the performance of reconstruction. With these results, future non-expert users can easily determine appropriate settings of HANTS for specific applications in different regions.

scholarly journals Interaction of Seasonal Sun-Angle and Savanna Phenology Observed and Modelled using MODIS

2019 ◽  
Vol 11 (12) ◽  
pp. 1398 ◽  
Author(s):  
Xuanlong Ma ◽  
Alfredo Huete ◽  
Ngoc Nguyen Tran
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Growing Season ◽  
The Sun ◽  
Space And Time ◽  
Ndvi Time Series ◽  
Angle Effect ◽  
Phenological Metrics

Remote sensing of phenology usually works at the regional and global scales, which imposes considerable variations in the solar zenith angle (SZA) across space and time. Variations in SZA alters the shape and profile of the surface reflectance and vegetation index (VI) time series, but this effect on remote-sensing-derived vegetation phenology has not been adequately evaluated. The objective of this study is to understand the behaviour of VIs response to SZA, and to further improve the interpretation of satellite observed vegetation dynamics, across space and time. In this study, the sensitivity of two widely used VIs—the normalised difference vegetation index (NDVI) and the enhanced vegetation index (EVI)—to SZA was investigated at four northern Australian savanna sites, over a latitudinal distance of 9.8° (~1100 km). Complete time series of surface reflectances, as acquired with different SZA configurations, were simulated using Bidirectional Reflectance Distribution Function (BRDF) parameters provided by MODerate Resolution Imaging Spectroradiometer (MODIS). The sun-angle dependency of the four phenological transition dates were assessed. Results showed that while NDVI was very sensitive to SZA, such sensitivity was nearly absent for EVI. A negative correlation was also observed between NDVI sensitivity to SZA and vegetation cover, with sensitivity declining to the same level as EVI when vegetation cover was high. Different sun-angle configurations resulted in considerable variations in the shape and magnitude of the phenological profiles. The sensitivity of VIs to SZA was generally greater during the dry season (with only active trees present) than in the wet season (with both active trees and grasses), thus, the sun-angle effect on VIs was phenophase-dependent. The sun-angle effect on NDVI time series resulted in considerable differences in the phenological metrics across different sun-angle configurations. Across four sites, the sun-angle effect caused 15.5 days, 21.6 days, and 20.5 days differences in the start, peak, and the end of the growing season derived from NDVI time series, with seasonally varying SZA at local solar noon, as compared to those metrics derived from NDVI time series with fixed SZA. In comparison, those differences in the start, peak, and end of the growing season for EVI were significantly smaller, with only 4.8 days, 4.9 days, and 3 days, respectively. Our results suggest the potential importance of considering the seasonal SZA effect on VI time series prior to the retrieval of phenological metrics.

scholarly journals Estimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data

2021 ◽  
Vol 13 (21) ◽  
pp. 4426
Author(s):  
Ranran Yang ◽  
Lei Wang ◽  
Qingjiu Tian ◽  
Nianxu Xu ◽  
Yanjun Yang
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Time Series Data ◽  
Mixed Forest ◽  
Cluster Method ◽  
Series Data ◽  
Ratio Estimation ◽  
Mixed Forests ◽  
Area Index ◽  
Ndvi Time Series

Most natural forests are mixed forests, a mixed broadleaf-conifer forest is essentially a heterogeneously mixed pixel in remote sensing images. Satellite missions rely on modeling to acquire regional or global vegetation parameter products. However, these retrieval models often assume homogeneous conditions at the pixel level, resulting in a decrease in the inversion accuracy, which is an issue for heterogeneous forests. Therefore, information on the canopy composition of a mixed forest is the basis for accurately retrieving vegetation parameters using remote sensing. Medium and high spatial resolution multispectral time-series data are important sources for canopy conifer-broadleaf ratio estimation because these data have a high frequency and wide coverage. This paper highlights a successful method for estimating the conifer-broadleaf ratio in a mixed forest with diverse tree species and complex canopy structures. Experiments were conducted in the Purple Mountain, Nanjing, Jiangsu Province of China, where we collected leaf area index (LAI) time-series and forest sample plot inventory data. Based on the Invertible Forest Reflectance Model (INFORM), we simulated the normalized difference vegetation index (NDVI) time-series of different conifer-broadleaf ratios. A time-series similarity analysis was performed to determine the typical separable conifer-broadleaf ratios. Fifteen Gaofen-1 (GF-1) satellite images of 2015 were acquired. The conifer-broadleaf ratio estimation was based on the GF-1 NDVI time-series and semi-supervised k-means cluster method, which yielded a high overall accuracy of 83.75%. This study demonstrates the feasibility of accurately estimating separable conifer-broadleaf ratios using field measurement data and GF-1 time series in mixed broadleaf-conifer forests.

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