Remote sensing of canopy nitrogen at regional scale in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index

Abstract. Canopy nitrogen (N) concentration and content are linked to several vegetation processes. Therefore, canopy N concentration is a state variable in global vegetation models with coupled carbon (C) and N cycles. While there are ample C data available to constrain the models, widespread N data are lacking. Remotely sensed vegetation indices have been used to detect canopy N concentration and canopy N content at the local scale in grasslands and forests. Vegetation indices could be a valuable tool to detect canopy N concentration and canopy N content at larger scale. In this paper, we conducted a regional case-study analysis to investigate the relationship between the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) time series from European Space Agency (ESA) Envisat satellite at 1 km spatial resolution and both canopy N concentration (%N) and canopy N content (N g m−2, of ground area) from a Mediterranean forest inventory in the region of Catalonia, in the northeast of Spain. The relationships between the datasets were studied after resampling both datasets to lower spatial resolutions (20, 15, 10 and 5 km) and at the original spatial resolution of 1 km. The results at higher spatial resolution (1 km) yielded significant log–linear relationships between MTCI and both canopy N concentration and content: r2 = 0.32 and r2 = 0.17, respectively. We also investigated these relationships per plant functional type. While the relationship between MTCI and canopy N concentration was strongest for deciduous broadleaf and mixed plots (r2 = 0.24 and r2 = 0.44, respectively), the relationship between MTCI and canopy N content was strongest for evergreen needleleaf trees (r2 = 0.19). At the species level, canopy N concentration was strongly related to MTCI for European beech plots (r2 = 0.69). These results present a new perspective on the application of MTCI time series for canopy N detection.

Download Full-text

Regional detection of canopy nitrogen in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index

10.5194/bg-2017-228 ◽

2017 ◽

Cited By ~ 2

Author(s):

Yasmina Loozen ◽

Karin T. Rebel ◽

Derek Karssenberg ◽

Martin J. Wassen ◽

Jordi Sardans ◽

...

Keyword(s):

Spatial Resolution ◽

Mediterranean Forests ◽

N Content ◽

Foliar Nitrogen ◽

N Concentration ◽

Chlorophyll Index ◽

Vegetation Models ◽

Global Vegetation ◽

Canopy Nitrogen ◽

The Relationship

Abstract. Canopy nitrogen (N) concentration and content are linked to several vegetation processes and canopy N concentration is a state variable in global vegetation models with coupled carbon (C) and N cycles. While there is ample C data available to constrain the models, widespread N data are lacking. Remote sensing and vegetation indices have been used to detect canopy N concentration and canopy N content at the local scale in grasslands and forests. In this paper we conducted a regional case-study analysis investigating the relationship between the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) time series from ESA ENVISAT at 1 km spatial resolution and both canopy N concentration (%N) and canopy N content (g m−2) from a Mediterranean forests inventory in the region of Catalonia, NE of Spain. The relationships between the datasets were studied after resampling both datasets to lower spatial resolutions (20 km, 15 km, 10 km and 5 km) and at the initial higher spatial resolution of 1 km. The results at the higher spatial resolution yielded significant relationships between MTCI and both canopy N concentration and content, r2 = 0.32 and r2 = 0.17, respectively. We also investigated these relationships per plant functional type. While the relationship between MTCI and canopy N concentration was strongest for deciduous broadleaf and mixed plots (r2 = 0.25 and r2 = 0.47, respectively), the relationship between MTCI and canopy N content was strongest for evergreen needleleaf trees (r2 = 0.20). At the species level, canopy N concentration was strongly related to MTCI for European Beech plots (r2 = 0.71). These results present a new perspective on the application of MTCI time series for canopy N detection, ultimately leading towards the generation of canopy N maps that can be used to constrain global vegetation models. Keywords: vegetation index, MERIS, foliar nitrogen concentration, foliar nitrogen content, plant functional types, Mediterranean forest, remote sensing

Download Full-text

Thresholds definition in MOD13Q1 and VGT-S10 time series for coffee crop area estimation in Triângulo Mineiro/Alto Paranaíba

Theoretical and Applied Engineering ◽

10.31422/taae.v3i2.15 ◽

2019 ◽

Vol 3 (2) ◽

pp. 1-10

Author(s):

Michel Eustáquio Dantas Chaves ◽

Elizabeth Ferreira ◽

Antonio Augusto Aguilar Dantas

Keyword(s):

Land Use ◽

Time Series ◽

Large Scale ◽

Agricultural Research ◽

Regional Scale ◽

Vegetation Indices ◽

Seasonal Development ◽

Land Use Classification ◽

Crop Area ◽

The Time Domain

In the last decades, remote sensing application in agricultural research has intensified to evaluate phenological cycles. Vegetation indices time series have been used to obtain information about the seasonal development of agricultural vegetation on a large scale. The multitemporal approach increases the gain of information coming from orbital images, an important factor for analysis of its spatial distribution. The objective of this study was to test the application of vegetation indices of the MODIS and SPOT-VEGETATION sensors to estimate the areas destined for coffee crops in the Triângulo Mineiro/Alto Paranaíba mesoregion. The results show that the vegetation indices NDVI and EVI of the product MOD13Q1 were more adequate for the estimation of land use over the time domain, especially NDVI. The best minimum threshold varies between 0.39 - 0.42 and the best maximum threshold varies between 0.71 - 0.74. The contribution of this work is that these thresholds can serve as subsidies for land use classification studies on a regional scale and for estimating areas for planting.

Download Full-text

Evaluating the Effectiveness of Using Vegetation Indices Based on Red-Edge Reflectance from Sentinel-2 to Estimate Gross Primary Productivity

Remote Sensing ◽

10.3390/rs11111303 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1303 ◽

Cited By ~ 9

Author(s):

Shangrong Lin ◽

Jing Li ◽

Qinhuo Liu ◽

Longhui Li ◽

Jing Zhao ◽

...

Keyword(s):

Spatial Resolution ◽

Primary Productivity ◽

Carbon Flux ◽

High Spatial Resolution ◽

Vegetation Indices ◽

Gross Primary Productivity ◽

Flux Tower ◽

Red Edge ◽

The Relationship ◽

Sentinel 2

Gross primary productivity (GPP) is the most important component of terrestrial carbon flux. Red-edge (680–780 nm) reflectance is sensitive to leaf chlorophyll content, which is directly correlated with photosynthesis as the pigment pool, and it has the potential to improve GPP estimation. The European Space Agency (ESA) Sentinel-2A and B satellites provide red-edge bands at 20-m spatial resolution on a five-day revisit period, which can be used for global estimation of GPP. Previous studies focused mostly on improving cropland GPP estimation using red-edge bands. In this study, we firstly evaluated the relationship between eight vegetation indices (VIs) retrieved from Sentinel-2 imagery in association with incident photosynthetic active radiation (PARin) and carbon flux tower GPP (GPPEC) across three forest and two grassland sites in Australia. We derived a time series of five red-edge VIs and three non-red-edge VIs over the CO2 flux tower footprints at 16-day time intervals and compared both temporal and spatial variations. The results showed that the relationship between the red-edge index (CIr, ρ 783 ρ 705 − 1 ) multiplied by PARin and GPPEC had the highest correlation (R2 = 0.77, root-mean-square error (RMSE) = 0.81 gC∙m−2∙day−1) at the two grassland sites. The CIr also showed consistency (rRMSE defined as RMSE/mean GPP, lower than 0.25) across forest and grassland sites. The high spatial resolution of the Sentinel-2 data provided more detailed information to adequately characterize the GPP variance at spatially heterogeneous areas. The high revisit period of Sentinel-2 exhibited temporal variance in GPP at the grassland sites; however, at forest sites, the flux-tower-based GPP variance could not be fully tracked by the limited satellite images. These results suggest that the high-spatial-resolution red-edge index from Sentinel-2 can improve large-scale spatio-temporal GPP assessments.

Download Full-text

Regional-Scale Forest Mapping over Fragmented Landscapes Using Global Forest Products and Landsat Time Series Classification

Remote Sensing ◽

10.3390/rs12010187 ◽

2020 ◽

Vol 12 (1) ◽

pp. 187 ◽

Cited By ~ 3

Author(s):

Viktor Myroniuk ◽

Mykola Kutia ◽

Arbi J. Sarkissian ◽

Andrii Bilous ◽

Shuguang Liu

Keyword(s):

Time Series ◽

Spatial Resolution ◽

Forest Cover ◽

Regional Scale ◽

Forest Products ◽

Google Earth ◽

Tree Cover ◽

Landsat 8 ◽

Fragmented Landscapes ◽

Forest Mapping

Satellite imagery of 25–30 m spatial resolution has been recognized as an effective tool for monitoring the spatial and temporal dynamics of forest cover at different scales. However, the precise mapping of forest cover over fragmented landscapes is complicated and requires special consideration. We have evaluated the performance of four global forest products of 25–30 m spatial resolution within three flatland subregions of Ukraine that have different forest cover patterns. We have explored the relationship between tree cover extracted from the global forest change (GFC) and relative stocking density of forest stands and justified the use of a 40% tree cover threshold for mapping forest in flatland Ukraine. In contrast, the canopy cover threshold for the analogous product Landsat tree cover continuous fields (LTCCF) is found to be 25%. Analysis of the global forest products, including discrete forest masks Global PALSAR-2/PALSAR Forest/Non-Forest Map (JAXA FNF) and GlobeLand30, has revealed a major misclassification of forested areas under severe fragmentation patterns of landscapes. The study also examined the effectiveness of forest mapping over fragmented landscapes using dense time series of Landsat images. We collected 1548 scenes of Landsat 8 Operational Land Imager (OLI) for the period 2014–2016 and composited them into cloudless mosaics for the following four seasons: yearly, summer, autumn, and April–October. The classification of images was performed in Google Earth Engine (GEE) Application Programming Interface (API) using random forest (RF) classifier. As a result, 30 m spatial resolution forest mask for flatland of Ukraine was created. The user’s and producer’s accuracy were estimated to be 0.910 ± 0.015 and 0.880 ± 0.018, respectively. The total forest area for the flatland Ukraine is 9440.5 ± 239.4 thousand hectares, which is 3% higher than official data. In general, we conclude that the Landsat-derived forest mask performs well over fragmented landscapes if forest cover of the territory is higher than 10–15%.

Download Full-text

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

Remote Sensing ◽

10.3390/rs11192201 ◽

2019 ◽

Vol 11 (19) ◽

pp. 2201 ◽

Cited By ~ 6

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.

Download Full-text

UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health

Remote Sensing ◽

10.3390/rs10081216 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1216 ◽

Cited By ~ 26

Author(s):

Jonathan Dash ◽

Grant Pearse ◽

Michael Watt

Keyword(s):

Time Series ◽

Spatial Resolution ◽

Satellite Imagery ◽

Satellite Data ◽

Physiological Stress ◽

Abiotic Factors ◽

Vegetation Indices ◽

Detection Threshold ◽

Forest Trees ◽

Multispectral Imagery

The development of methods that can accurately detect physiological stress in forest trees caused by biotic or abiotic factors is vital for ensuring productive forest systems that can meet the demands of the Earth’s population. The emergence of new sensors and platforms presents opportunities to augment traditional practices by combining remotely-sensed data products to provide enhanced information on forest condition. We tested the sensitivity of multispectral imagery collected from time-series unmanned aerial vehicle (UAV) and satellite imagery to detect herbicide-induced stress in a carefully controlled experiment carried out in a mature Pinus radiata D. Don plantation. The results revealed that both data sources were sensitive to physiological stress in the study trees. The UAV data were more sensitive to changes at a finer spatial resolution and could detect stress down to the level of individual trees. The satellite data tested could only detect physiological stress in clusters of four or more trees. Resampling the UAV imagery to the same spatial resolution as the satellite imagery revealed that the differences in sensitivity were not solely the result of spatial resolution. Instead, vegetation indices suited to the sensor characteristics of each platform were required to optimise the detection of physiological stress from each data source. Our results define both the spatial detection threshold and the optimum vegetation indices required to implement monitoring of this forest type. A comparison between time-series datasets of different spectral indices showed that the two sensors are compatible and can be used to deliver an enhanced method for monitoring physiological stress in forest trees at various scales. We found that the higher resolution UAV imagery was more sensitive to fine-scale instances of herbicide induced physiological stress than the RapidEye imagery. Although less sensitive to smaller phenomena the satellite imagery was found to be very useful for observing trends in physiological stress over larger areas.

Download Full-text

Reconstruction of High-Temporal- and High-Spatial-Resolution Reflectance Datasets Using Difference Construction and Bayesian Unmixing

Remote Sensing ◽

10.3390/rs12233952 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3952

Author(s):

Lei Yang ◽

Jinling Song ◽

Lijuan Han ◽

Xin Wang ◽

Jing Wang

Keyword(s):

Time Series ◽

Spatial Resolution ◽

Land Surface ◽

High Spatial Resolution ◽

Vegetation Indices ◽

Vital Role ◽

Dynamic Monitoring ◽

Fusion Model ◽

Spatiotemporal Resolution ◽

Reconstruction Methods

High-temporal- and high-spatial-resolution reflectance datasets play a vital role in monitoring dynamic changes at the Earth’s land surface. So far, many sensors have been designed with a trade-off between swath width and pixel size; thus, it is difficult to obtain reflectance data with both high spatial resolution and frequent coverage from a single sensor. In this study, we propose a new Reflectance Bayesian Spatiotemporal Fusion Model (Ref-BSFM) using Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) surface reflectance, which is then used to construct reflectance datasets with high spatiotemporal resolution and a long time series. By comparing this model with other popular reconstruction methods (the Flexible Spatiotemporal Data Fusion Model, the Spatial and Temporal Adaptive Reflectance Fusion Model, and the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model), we demonstrate that our approach has the following advantages: (1) higher prediction accuracy, (2) effective treatment of cloud coverage, (3) insensitivity to the time span of data acquisition, (4) capture of temporal change information, and (5) higher retention of spatial details and inconspicuous MODIS patches. Reflectance time-series datasets generated by Ref-BSFM can be used to calculate a variety of remote-sensing-based vegetation indices, providing an important data source for land surface dynamic monitoring.

Download Full-text

A multi-sensor satellite method to spatial and temporal detection of landslides and flash floods in cloud-covered tropical environments: the western branch of the East African Rift

10.5194/egusphere-egu21-8909 ◽

2021 ◽

Author(s):

Axel Deijns ◽

François Kervyn ◽

Olivier Dewitte ◽

Wim Thiery ◽

Jean-Philippe Malet ◽

...

Keyword(s):

Remote Sensing ◽

Land Use ◽

Time Series ◽

Spatial Resolution ◽

Regional Scale ◽

Flash Floods ◽

Phase Coherence ◽

East African ◽

African Rift ◽

The Tropics

<p>Geomorphic hazards such as landslides and flash floods (hereafter called GH) often result from a combination of complex interacting physical and anthropogenic processes across multiple spatial and temporal scales. In many instances, landslides and flash floods occur very quickly, sometimes in a matter of a few hours occasionally leading to catastrophic impact on human lives. Given that they are mostly related to common meteorological events, landslides and flash floods frequently co-occur and interact, leading to more severe impacts. The tropics are environments where GH are under-researched while, in the meantime, GH disproportionately impact these regions. In addition, GH frequency and/or risks in the tropics are expected to increase in the future in response to increasing demographic pressure, climate change and land use/cover changes. To understand the role of climate and landscape (topographic and land use/cover) in controlling the spatio-temporal distribution of GH in the context of environmental changes, establishing a regional-scale inventory of GH events that are localised accurately in space and time is essential. Since the tropics are frequently cloud covered, an accurate characterization of the timing of GH at a regional scale can only be achieved through the combined use of optical and Synthetic Aperture Radar (SAR) remote sensing. Here, the objective is to present the first phase of the ongoing development of a remote sensing methodology that aims to identify accurately in space and time the GH events in the western branch of the East African Rift using a multi-temporal change analysis approach combining optical and SAR amplitude and phase coherence data. Copernicus Sentinel 1 (SAR imagery) and Sentinel 2 (optical imagery) are the key satellite products used. Next to being open access, they offer a very good trade-off between frequency of acquisition and spatial resolution. The detection methodology is calibrated and validated using information from three citizen observer networks and higher spatial resolution imagery. Preliminary results show clear changes in SAR amplitude and phase coherence time-series at the time of GH event occurence. Various change detection approaches (difference, log-ratio, normalized difference, correlation) are explored and provide ideas for detection of GH timing within the time-series. We present the ongoing method development with a specific focus on recent extreme GH events in the region.</p>

Download Full-text

Spraying Leaves of Pear Nursery Trees with Urea and Copper Ethylenediaminetetraacetic Acid Alters Tree Nitrogen Concentration without Influencing Tree Susceptibility to Phytophthora syringae

HortTechnology ◽

10.21273/horttech.20.2.331 ◽

2010 ◽

Vol 20 (2) ◽

pp. 331-342 ◽

Cited By ~ 1

Author(s):

S. Laywisadkul ◽

C.F. Scagel ◽

L.H. Fuchigami ◽

R.G. Linderman

Keyword(s):

Ethylenediaminetetraacetic Acid ◽

Nitrogen Concentration ◽

N Availability ◽

Soil N ◽

N Content ◽

Soil N Availability ◽

Susceptibility To Infection ◽

N Concentration ◽

Nursery Trees ◽

The Relationship

Recent field observations by growers suggest that increased nitrogen (N) content in nursery trees resulting from foliar sprays with urea in the autumn increases tree susceptibility to infection by Phytophthora syringae. We investigated the effects of soil N availability and spraying pear (Pyrus communis ‘OHF 97’) trees with combinations of urea, chelated copper ethylenediaminetetraacetic acid (CuEDTA), and phosphonate-containing fungicides on stem N concentration and susceptibility to infection by P. syringae. Increasing soil N availability increased susceptibility to P. syringae and increased N and amino acid concentration in stems. Spraying trees with urea in the autumn increased concentrations of N and amino acids in stems and had no significant effect on tree susceptibility when stems were inoculated with P. syringae before or after urea sprays. Spraying trees with CuEDTA decreased stem N concentrations and had no significant influence on tree susceptibility to P. syringae when stems were inoculated before or after CuEDTA sprays. These results suggest the relationship between tree susceptibility to P. syringae and tree N concentration may be specific to the form of N, delivery method, or timing of N applications. Trees had higher N concentrations in stems in November than in October and were more susceptible to P. syringae when inoculated in November, suggesting that environmental factors and increasing tree dormancy may be responsible for changes in susceptibility to the pathogen. Spraying trees with fungicides containing fosetyl-aluminum in October or November decreased tree susceptibility to P. syringae. The effects of fungicides containing fosetyl-aluminum on susceptibility were similar regardless of whether trees were sprayed or not with urea or CuEDTA, suggesting that these fungicides can be used in combination with urea or CuEDTA sprays for reducing disease severity caused by P. syringae without impacting growers' objective of increasing tree N content with urea or enhancing early defoliation with CuEDTA.

Download Full-text

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

Malaysian Journal of Sustainable Environment ◽

10.24191/myse.v7i1.8905 ◽

2020 ◽

Vol 7 (1) ◽

pp. 21

Author(s):

Faradina Marzukhi ◽

Nur Nadhirah Rusyda Rosnan ◽

Md Azlin Md Said

Keyword(s):

Oil Palm ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Satellite Image ◽

Vegetation Indices ◽

Soil Nutrient ◽

Oil Palm Plantation ◽

Sustainable Environment ◽

Moisture Analysis ◽

The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

Download Full-text