scholarly journals Recurrence Analysis of Vegetation Indices for Highlighting the Ecosystem Response to Drought Events: An Application to the Amazon Forest

2020 ◽  
Vol 12 (6) ◽  
pp. 907 ◽  
Author(s):  
Teodoro Semeraro ◽  
Andrea Luvisi ◽  
Antonio O. Lillo ◽  
Roberta Aretano ◽  
Riccardo Buccolieri ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Vegetation Index ◽  
Southern Oscillation ◽  
Vegetation Indices ◽  
Atlantic Multidecadal Oscillation ◽  
Enso Event ◽  
Amazon Forest ◽  
Ecosystem Response ◽  
Water Index

Forests are important in sequestering CO2 and therefore play a significant role in climate change. However, the CO2 cycle is conditioned by drought events that alter the rate of photosynthesis, which is the principal physiological action of plants in transforming CO2 into biological energy. This study applied recurrence quantification analysis (RQA) to describe the evolution of photosynthesis-related indices to highlight disturbance alterations produced by the Atlantic Multidecadal Oscillation (AMO, years 2005 and 2010) and the El Niño-Southern Oscillation (ENSO, year 2015) in the Amazon forest. The analysis was carried out using Moderate Resolution Imaging Spectroradiometer (MODIS) images to build time series of the enhanced vegetation index (EVI), the normalized difference water index (NDWI), and the land surface temperature (LST) covering the period 2001–2018. The results did not show significant variations produced by AMO throughout the study area, while a disruption due to the global warming phase linked to the extreme ENSO event occurred, and the forest was able to recover. In addition, spatial differences in the response of the forest to the ENSO event were found. These findings show that the application of RQA to the time series of vegetation indices supports the evaluation of the forest ecosystem response to disruptive events. This approach provides information on the capacity of the forest to recover after a disruptive event and, therefore is useful to estimate the resilience of this particular ecosystem.

scholarly journals Pemetaan Pola Tanam dan Kalender Tanam Padi Sawah menggunakan Teknik Penginderaan Jauh

2018 ◽  
Vol 2017 (2) ◽  
Author(s):  
Rika Hernawati ◽  
Agung Budi Harto ◽  
Dewi Kania Sari
Keyword(s):  
Surface Water ◽  
Land Surface ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Paddy Rice ◽  
Growth Monitoring ◽  
Enhanced Vegetation Index ◽  
Water Index ◽  
Gaussian Filtering ◽  
Land Surface Water

ABSTRAKPemantauan dan prakiraan hasil tanam padi sawah penting untuk dilakukan antara lain dalam rangka menjaga ketahanan pangan nasional. Saat ini, pemantauan pertumbuhan tanaman padi sawah dapat dilakukan dengan mengaplikasikan teknologi pengindraan jauh, antara lain dengan mendeteksi fenologi tanaman padi sawah yang terekam pada setiap piksel citra yang selanjutnya dapat digunakan untuk pemetaan pola tanam dan kalender tanam padi sawah. Penelitian ini bertujuan untuk mengembangkan algoritma deteksi fenologi padi sawah dengan menggunakan indeks vegetasi Enhanced Vegetation Index (EVI) dan Land Surface Water Index (LSWI) berkala yang diturunkan dari data citra MODIS, dengan menerapkan proses penapisan Gaussian. Penerapan teknik penapisan Gaussian pada data indeks vegetasi tersebut diharapkan dapat meminimalisasi derau, sehingga akan meningkatkan ketelitian hasil pendeteksian fenologi tanaman padi sawah. Wilayah studi mencakup 3 Kabupaten di Provinsi Jawa Barat bagian utara, yaitu Kabupaten Subang, Kabupaten Karawang, dan Kabupaten Bekasi. Hasil penelitian menunjukkan bahwa penerapan penapisan Gaussian pada metode deteksi fenologi padi sawah berbasis indeks vegetasi EVI dan LSWI berkala telah dapat meningkatkan ketelitian hasil deteksi tanggal-tanggal fenologis padi sawah. Keakuratan hasil estimasi luas tanam dan luas panen padi sawah divalidasi menggunakan data statistik dari Dinas Pertanian Kabupaten.Kata Kunci: deteksi fenologi, EVI, LSWI, penapisan GaussianABSTRACTMonitoring and forecasting yields of paddy rice are important to do, in order to maintain national food security. The current paddy crop growth monitoring can be done by applying remote sensing technology by detecting paddy phenology to produce the date of planting and harvest dates, which were recorded at each pixel of the digital image of rice field and can then be used for cropping pattern and planting calendar mapping. This research aims to develop a detection algorithm phenology paddy using vegetation indices Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) periodic image data derived from MODIS, by applying a Gaussian filtering process. The application of Gaussian filtering techniques to the data of vegetation indeces, EVI and LSWI, are expected to minimize the noise, thereby increasing the precision of detection of paddy rice crop phenology. The study area covers three districts in the northern part of West Java Province, i.e. Subang, Karawang and Bekasi. The results showed that the application of Gaussian filtering on the detection method of paddy rice phenology based on multitemporal vegetation indices EVI and LSWI can improve the precision of the detection of paddy phenological dates. The accuracy of the estimation results of the planting and harvested area of paddy were validated using statistical data from the District Agricultural Office.Keywords: phenology detection, EVI, LSWI, Gaussian filtering

scholarly journals Influence of Varying Solar Zenith Angles on Land Surface Phenology Derived from Vegetation Indices: A Case Study in the Harvard Forest

2021 ◽  
Vol 13 (20) ◽  
pp. 4126
Author(s):  
Yang Li ◽  
Ziti Jiao ◽  
Kaiguang Zhao ◽  
Yadong Dong ◽  
Yuyu Zhou ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Mean Difference ◽  
Land Surface Phenology ◽  
Harvard Forest ◽  
Ndvi Time Series ◽  
Bidirectional Reflectance Distribution

Vegetation indices are widely used to derive land surface phenology (LSP). However, due to inconsistent illumination geometries, reflectance varies with solar zenith angles (SZA), which in turn affects the vegetation indices, and thus the derived LSP. To examine the SZA effect on LSP, the MODIS bidirectional reflectance distribution function (BRDF) product and a BRDF model were employed to derive LSPs under several constant SZAs (i.e., 0°, 15°, 30°, 45°, and 60°) in the Harvard Forest, Massachusetts, USA. The LSPs derived under varying SZAs from the MODIS nadir BRDF-adjusted reflectance (NBAR) and MODIS vegetation index products were used as baselines. The results show that with increasing SZA, NDVI increases but EVI decreases. The magnitude of SZA-induced NDVI/EVI changes suggests that EVI is more sensitive to varying SZAs than NDVI. NDVI and EVI are comparable in deriving the start of season (SOS), but EVI is more accurate when deriving the end of season (EOS). Specifically, NDVI/EVI-derived SOSs are relatively close to those derived from ground measurements, with an absolute mean difference of 8.01 days for NDVI-derived SOSs and 9.07 days for EVI-derived SOSs over ten years. However, a considerable lag exists for EOSs derived from vegetation indices, especially from the NDVI time series, with an absolute mean difference of 14.67 days relative to that derived from ground measurements. The SOSs derived from NDVI time series are generally earlier, while those from EVI time series are delayed. In contrast, the EOSs derived from NDVI time series are delayed; those derived from the simulated EVI time series under a fixed illumination geometry are also delayed, but those derived from the products with varying illumination geometries (i.e., MODIS NBAR product and MODIS vegetation index product) are advanced. LSPs derived from varying illumination geometries could lead to a difference spanning from a few days to a month in this case study, which highlights the importance of normalizing the illumination geometry when deriving LSP from NDVI/EVI time series.

scholarly journals A data-model fusion approach for upscaling gross ecosystem productivity to the landscape scale based on remote sensing and flux footprint modelling

2010 ◽  
Vol 7 (9) ◽  
pp. 2943-2958 ◽  
Author(s):  
B. Chen ◽  
Q. Ge ◽  
D. Fu ◽  
G. Yu ◽  
X. Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Model ◽  
Land Surface ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Consecutive Series ◽  
Fusion Model ◽  
Flux Footprint ◽  
Model Fusion ◽  
Water Index

Abstract. In order to use the global available eddy-covariance (EC) flux dataset and remote-sensing measurements to provide estimates of gross primary productivity (GPP) at landscape (101–102 km2), regional (103–106 km2) and global land surface scales, we developed a satellite-based GPP algorithm using LANDSAT data and an upscaling framework. The satellite-based GPP algorithm uses two improved vegetation indices (Enhanced Vegetation Index – EVI, Land Surface Water Index – LSWI). The upscalling framework involves flux footprint climatology modelling and data-model fusion. This approach was first applied to an evergreen coniferous stand in the subtropical monsoon climatic zone of south China. The EC measurements at Qian Yan Zhou tower site (26°44´48" N, 115°04´13" E), which belongs to the China flux network and the LANDSAT and MODIS imagery data for this region in 2004 were used in this study. A consecutive series of LANDSAT-like images of the surface reflectance at an 8-day interval were predicted by blending the LANDSAT and MODIS images using an existing algorithm (ESTARFM: Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model). The seasonal dynamics of GPP were then predicted by the satellite-based algorithm. MODIS products explained 60% of observed variations of GPP and underestimated the measured annual GPP (= 1879 g C m−2) by 25–30%; while the satellite-based algorithm with default static parameters explained 88% of observed variations of GPP but overestimated GPP during the growing seasonal by about 20–25%. The optimization of the satellite-based algorithm using a data-model fusion technique with the assistance of EC flux tower footprint modelling reduced the biases in daily GPP estimations from about 2.24 g C m−2 day−1 (non-optimized, ~43.5% of mean measured daily value) to 1.18 g C m−2 day−1 (optimized, ~22.9% of mean measured daily value). The remotely sensed GPP using the optimized algorithm can explain 92% of the seasonal variations of EC observed GPP. These results demonstrated the potential combination of the satellite-based algorithm, flux footprint modelling and data-model fusion for improving the accuracy of landscape/regional GPP estimation, a key component for the study of the carbon cycle.

scholarly journals Cross-Comparison between Landsat 8 (OLI) and Landsat 7 (ETM+) Derived Vegetation Indices in a Mediterranean Environment

2020 ◽  
Vol 12 (2) ◽  
pp. 291 ◽  
Author(s):  
Giuseppe Mancino ◽  
Agostino Ferrara ◽  
Antonietta Padula ◽  
Angelo Nolè
Keyword(s):  
Land Use ◽  
Land Surface ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Earth Observation ◽  
Landsat 8 ◽  
Mediterranean Environment ◽  
Water Index ◽  
Land Use Types ◽  
Landsat 7

Landsat 8 is the most recent generation of Landsat satellite missions that provides remote sensing imagery for earth observation. The Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images, together with Landsat-8 Operational Land Imager (OLI) and Thermal Infrared sensor (TIRS) represent fundamental tools for earth observation due to the optimal combination of the radiometric and geometric images resolution provided by these sensors. However, there are substantial differences between the information provided by Landsat 7 and Landsat 8. In order to perform a multi-temporal analysis, a cross-comparison between image from different Landsat satellites is required. The present study is based on the evaluation of specific intercalibration functions for the standardization of main vegetation indices calculated from the two Landsat generation images, with respect to main land use types. The NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), LSWI (Land Surface Water Index), NBR (Normalized Burn Ratio), VIgreen (Green Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and EVI (Enhanced Vegetation Index) have been derived from August 2017 ETM+ and OLI images (path: 188; row: 32) for the study area (Basilicata Region, located in the southern part of Italy) selected as a highly representative of Mediterranean environment. Main results show slight differences in the values of average reflectance for each band: OLI shows higher values in the near-infrared (NIR) wavelength for all the land use types, while in the short-wave infrared (SWIR) the ETM+ shows higher reflectance values. High correlation coefficients between different indices (in particular NDVI and NDWI) show that ETM+ and OLI can be used as complementary data. The best correlation in terms of cross-comparison was found for NDVI, NDWI, SAVI, and EVI indices; while according to land use classes, statistically significant differences were found for almost all the considered indices calculated with the two sensors.

scholarly journals Characterising Rice (Oryza indica) Ecology in East and Southern Eastern Plateau of Odisha using Multi Source Remote Sensing

2021 ◽  
pp. 37-47
Author(s):  
M. Satya Swarupa Rani ◽  
Anima Biswal ◽  
B. S. Rath
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Ecological Condition ◽  
Water Index ◽  
Normalised Difference Vegetation Index ◽  
Spectral Vegetation Indices ◽  
Block Level

Rice is the most important crop of Odisha occupying 41.24% of net sown area in Kharif season and contributing 65.85 % of total food grain production of Odisha state and this is being cultivated in various types environmental and ecological condition. Assessment of rice phenology is prime for management and yield prediction. In view of characterizing rice ecology in East and South Eastern Plateau from 2008 – 2018 to know the time series analysis , remote sensing tools were used . MODIS can0 acquire data over a wide area with high spatial and temporal resolutions easily providing regional scale information .In order to study the seasonal /annual as well as spatial variability of kharif rice vigour and wetness spectral vegetation indices like NDVI(Normalised Difference Vegetation Index),LSWI(Land surface water index) derived from 15 day composite 250 m data were analysed at block level for Odisha state. For studying the start of season variability, SASI index was used. The season maximum NDVI, LSWI were computed for the year 2008-2018 for kharif rice in East and Southern eastern coastal plain zone of Odisha and graphs were generated which shows the variability of the kharif rice vigour and wetness.

scholarly journals Correction of Directional Effects in VEGETATION NDVI Time-Series

2021 ◽  
Vol 13 (6) ◽  
pp. 1130
Author(s):  
Jonathan León-Tavares ◽  
Jean-Louis Roujean ◽  
Bruno Smets ◽  
Erwin Wolters ◽  
Carolien Toté ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Global Scale ◽  
Frequency Noise ◽  
Surface Reflectance ◽  
Computationally Efficient ◽  
Land Surface Reflectance

Land surface reflectance measurements from the VEGETATION program (SPOT-4, SPOT-5 and PROBA-V satellites) have led to the acquisition of consistent time-series of Normalized Difference Vegetation Index (NDVI) at a global scale. The wide imaging swath (>2000 km) of the family of VEGETATION space-borne sensors ensures a daily coverage of the Earth at the expense of a varying observation and illumination geometries between successive orbit overpasses for a given target located on the ground. Such angular variations infer saw-like patterns on time-series of reflectance and NDVI. The presence of directional effects is not a real issue provided that they can be properly removed, which supposes an appropriate BRDF (Bidirectional Reflectance Distribution Function) sampling as offered by the VEGETATION program. An anisotropy correction supports a better analysis of the temporal shapes and spatial patterns of land surface reflectance values and vegetation indices such as NDVI. Herein we describe a BRDF correction methodology, for the purpose of the Copernicus Global Land Service framework, which includes notably an adaptive data accumulation window and provides uncertainties associated with the NDVI computed with normalized reflectance. Assessing the general performance of the methodology in comparing time-series between normalized and directional NDVI reveals a significant removal of the high-frequency noise due to directional effects. The proposed methodology is computationally efficient to operate at a global scale to deliver a BRDF-corrected NDVI product based on long-term Time-Series of VEGETATION sensor and its follow-on with the Copernicus Sentinel-3 satellite constellation.

scholarly journals An Empirical Assessment of the MODIS Land Cover Dynamics and TIMESAT Land Surface Phenology Algorithms

2019 ◽  
Vol 11 (19) ◽  
pp. 2201 ◽  
Author(s):  
Stanimirova ◽  
Cai ◽  
Melaas ◽  
Gray ◽  
Eklundh ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Land Surface ◽  
Vegetation Index ◽  
Spatial Scales ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Data Set ◽  
Land Cover Dynamics ◽  
The Impact

Observations of vegetation phenology at regional-to-global scales provide important information regarding seasonal variation in the fluxes of energy, carbon, and water between the biosphere and the atmosphere. Numerous algorithms have been developed to estimate phenological transition dates using time series of remotely sensed spectral vegetation indices. A key challenge, however, is that different algorithms provide inconsistent results. This study provides a comprehensive comparison of start of season (SOS) and end of season (EOS) phenological transition dates estimated from 500 m MODIS data based on two widely used sources of such data: the TIMESAT program and the MODIS Global Land Cover Dynamics (MLCD) product. Specifically, we evaluate the impact of land cover class, criteria used to identify SOS and EOS, and fitting algorithm (local versus global) on the transition dates estimated from time series of MODIS enhanced vegetation index (EVI). Satellite-derived transition dates from each source are compared against each other and against SOS and EOS dates estimated from PhenoCams distributed across the Northeastern United States and Canada. Our results show that TIMESAT and MLCD SOS transition dates are generally highly correlated (r = 0.51-0.97), except in Central Canada where correlation coefficients are as low as 0.25. Relative to SOS, EOS comparison shows lower agreement and higher magnitude of deviations. SOS and EOS dates are impacted by noise arising from snow and cloud contamination, and there is low agreement among results from TIMESAT, the MLCD product, and PhenoCams in vegetation types with low seasonal EVI amplitude or with irregular EVI time series. In deciduous forests, SOS dates from the MLCD product and TIMESAT agree closely with SOS dates from PhenoCams, with correlations as high as 0.76. Overall, our results suggest that TIMESAT is well-suited for local-to-regional scale studies because of its ability to tune algorithm parameters, which makes it more flexible than the MLCD product. At large spatial scales, where local tuning is not feasible, the MLCD product provides a readily available data set based on a globally consistent approach that provides SOS and EOS dates that are comparable to results from TIMESAT.

scholarly journals Upscaling of gross ecosystem production to the landscape scale using multi-temporal Landsat images, eddy covariance measurements and a footprint model

2009 ◽  
Vol 6 (6) ◽  
pp. 11317-11345 ◽  
Author(s):  
B. Chen ◽  
Q. Ge ◽  
D. Fu ◽  
G. Liu ◽  
G. Yu ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Land Surface ◽  
Vegetation Index ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Co2 Flux ◽  
Landsat Images ◽  
Flux Footprint ◽  
Water Index ◽  
Multi Temporal

Abstract. In order to use the global available eddy-covariance (EC) flux dataset and remote sensing measurements to provide estimates of gross primary production (GPP) at landscape (101–102km2), regional (103–106km2) and global land surface scales, we developed a satellite-based GPP algorithm using Landsat data and an upscaling framework. The satellite-based GPP algorithm uses two improved vegetation indices (Enhanced Vegetation Index – EVI, Land Surface Water Index – LSWI). The upscalling framework involves flux footprint climatology modeling and data-model fusion. This approach was first applied to an evergreen coniferous stand in South China subtropical monsoon climatic zone. The EC measurements at Qian Yinzhou tower site (26° 44'48'' N, 115° 04'13'' E), which belongs to the Chinaflux network, and the Landsat images for this region in 2004 were used in this study. The seasonal dynamics of GPP predicted by the satellite-based algorithm agreed well with observed GPP in 2004 at this site. These results demonstrate the potential of combining of the satellite-based algorithm, flux footprint modeling and data-fusion, for scaling-up of GPP at the CO2 flux tower sites, a key component for the study of the carbon cycle at regional and global scales.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

scholarly journals Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges

2021 ◽  
Vol 13 (11) ◽  
pp. 2060
Author(s):  
Trylee Nyasha Matongera ◽  
Onisimo Mutanga ◽  
Mbulisi Sibanda ◽  
John Odindi
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Critical Role ◽  
Spectral Characteristics ◽  
Vegetation Indices ◽  
Machine Learning Algorithms ◽  
Special Focus ◽  
Land Surface Phenology ◽  
Spectral Vegetation Indices ◽  
Satellite Sensors

Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment. In the future, the development of machine learning algorithms that can effectively model and characterize the phenological cycles of vegetation would help to unlock the value of LSP information in the rangeland monitoring and management process. Precisely, deep learning presents an opportunity to further develop robust software packages such as the decomposition and analysis of time series (DATimeS) with the abundance of data processing tools and techniques that can be used to better characterize the phenological cycles of vegetation in rangeland ecosystems.

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