scholarly journals Diversity of Algorithm and Spectral Band Inputs Improves Landsat Monitoring of Forest Disturbance

2020 ◽  
Vol 12 (10) ◽  
pp. 1673 ◽  
Author(s):  
Warren B. Cohen ◽  
Sean P. Healey ◽  
Zhiqiang Yang ◽  
Zhe Zhu ◽  
Noel Gorelick
Keyword(s):  
Near Infrared ◽  
Forest Disturbance ◽  
Spectral Band ◽  
Decision Rules ◽  
Spatial And Temporal Scales ◽  
Detection Algorithms ◽  
Spectral Bands ◽  
Change Detection Algorithms ◽  
Visible Wavelengths ◽  
Disturbance Detection

Disturbance monitoring is an important application of the Landsat times series, both to monitor forest dynamics and to support wise forest management at a variety of spatial and temporal scales. In the last decade, there has been an acceleration in the development of approaches designed to put the Landsat archive to use towards these causes. Forest disturbance mapping has moved from using individual change-detection algorithms, which implement a single set of decision rules that may not apply well to a range of scenarios, to compiling ensembles of such algorithms. One approach that has greatly reduced disturbance detection error has been to combine individual algorithm outputs in Random Forest (RF) ensembles trained with disturbance reference data, a process called stacking (or secondary classification). Previous research has demonstrated more robust and sensitive detection of disturbance using stacking with both multialgorithm ensembles and multispectral ensembles (which make use of a single algorithm applied to multiple spectral bands). In this paper, we examined several additional dimensions of this problem, including: (1) type of algorithm (represented by processes using one image per year vs. all historical images); (2) spectral band choice (including both the basic Landsat reflectance bands and several popular indices based on those bands); (3) number of algorithm/spectral-band combinations needed; and (4) the value of including both algorithm and spectral band diversity in the ensembles. We found that ensemble performance substantially improved per number of model inputs if those inputs were drawn from a diversity of both algorithms and spectral bands. The best models included inputs from both algorithms, using different variants of shortwave-infrared (SWIR) and near-infrared (NIR) reflectance. Further disturbance detection improvement may depend upon the development of algorithms which either interrogate SWIR and NIR in new ways or better highlight disturbance signals in the visible wavelengths.

scholarly journals Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status

2021 ◽  
Vol 13 (3) ◽  
pp. 536
Author(s):  
Eve Laroche-Pinel ◽  
Mohanad Albughdadi ◽  
Sylvie Duthoit ◽  
Véronique Chéret ◽  
Jacques Rousseau ◽  
...  
Keyword(s):  
Near Infrared ◽  
Water Status ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Short Wave ◽  
Spatial And Temporal Scales ◽  
Spectral Bands ◽  
Main Challenge ◽  
Spectral Scale ◽  
Grape Varieties

The main challenge encountered by Mediterranean winegrowers is water management. Indeed, with climate change, drought events are becoming more intense each year, dragging the yield down. Moreover, the quality of the vineyards is affected and the level of alcohol increases. Remote sensing data are a potential solution to measure water status in vineyards. However, important questions are still open such as which spectral, spatial, and temporal scales are adapted to achieve the latter. This study aims at using hyperspectral measurements to investigate the spectral scale adapted to measure their water status. The final objective is to find out whether it would be possible to monitor the vine water status with the spectral bands available in multispectral satellites such as Sentinel-2. Four Mediterranean vine plots with three grape varieties and different water status management systems are considered for the analysis. Results show the main significant domains related to vine water status (Short Wave Infrared, Near Infrared, and Red-Edge) and the best vegetation indices that combine these domains. These results give some promising perspectives to monitor vine water status.

scholarly journals TOPOGRAPHIC EFFECT ON SPECTRAL VEGETATION INDICES FROM LANDSAT TM DATA: IS TOPOGRAPHIC CORRECTION NECESSARY?

2016 ◽  
Vol 22 (1) ◽  
pp. 95-107 ◽  
Author(s):  
Eder Paulo Moreira* ◽  
Márcio de Morisson Valeriano ◽  
Ieda Del Arco Sanches ◽  
Antonio Roberto Formaggio
Keyword(s):  
Near Infrared ◽  
Incidence Angle ◽  
Spectral Band ◽  
Vegetation Indices ◽  
Topographic Effect ◽  
Topographic Correction ◽  
Spectral Bands ◽  
Spectral Vegetation Indices ◽  
Current Availability ◽  
Elevation Model

The full potentiality of spectral vegetation indices (VIs) can only be evaluated after removing topographic, atmospheric and soil background effects from radiometric data. Concerning the former effect, the topographic effect was barely investigated in the context of VIs, despite the current availability correction methods and Digital elevation Model (DEM). In this study, we performed topographic correction on Landsat 5 TM spectral bands and evaluated the topographic effect on four VIs: NDVI, RVI, EVI and SAVI. The evaluation was based on analyses of mean and standard deviation of VIs and TM band 4 (near-infrared), and on linear regression analyses between these variables and the cosine of the solar incidence angle on terrain surface (cos i). The results indicated that VIs are less sensitive to topographic effect than the uncorrected spectral band. Among VIs, NDVI and RVI were less sensitive to topographic effect than EVI and SAVI. All VIs showed to be fully independent of topographic effect only after correction. It can be concluded that the topographic correction is required for a consistent reduction of the topographic effect on the VIs from rugged terrain.

scholarly journals Hypertemporal Imaging Capability of UAS Improves Photogrammetric Tree Canopy Models

2020 ◽  
Vol 12 (8) ◽  
pp. 1238 ◽  
Author(s):  
Andrew Fletcher ◽  
Richard Mather
Keyword(s):  
Forest Canopy ◽  
Near Infrared ◽  
Spectral Band ◽  
Point Clouds ◽  
Tree Canopy ◽  
Solar Elevation ◽  
Spectral Bands ◽  
Eucalyptus Forest ◽  
Dense Point ◽  
Solar Noon

Small uncrewed aerial systems (UASs) generate imagery that can provide detailed information regarding condition and change if the products are reproducible through time. Densified point clouds form the basic information for digital surface models and orthorectified mosaics, so variable dense point reconstruction will introduce uncertainty. Eucalyptus trees typically have sparse and discontinuous canopies with pendulous leaves that present a difficult target for photogrammetry software. We examine how spectral band, season, solar azimuth, elevation, and some processing settings impact completeness and reproducibility of dense point clouds for shrub swamp and Eucalyptus forest canopy. At the study site near solar noon, selecting near infrared camera increased projected tree canopy fourfold, and dense point features more than 2 m above ground were increased sixfold compared to red spectral bands. Near infrared (NIR) imagery improved projected and total dense features two- and threefold, respectively, compared to default green band imagery. The lowest solar elevation captured (25°) consistently improved canopy feature reconstruction in all spectral bands. Although low solar elevations are typically avoided for radiometric reasons, we demonstrate that these conditions improve the detection and reconstruction of complex tree canopy features in natural Eucalyptus forests. Combining imagery sets captured at different solar elevations improved the reproducibility of dense point clouds between seasons. Total dense point cloud features reconstructed were increased by almost 10 million points (20%) when imagery used was NIR combining solar noon and low solar elevation imagery. It is possible to use agricultural multispectral camera rigs to reconstruct Eucalyptus tree canopy and shrub swamp by combining imagery and selecting appropriate spectral bands for processing.

Variation in understory and canopy reflectance during stand development in Finnish coniferous forests

2015 ◽  
Vol 45 (8) ◽  
pp. 1077-1085 ◽  
Author(s):  
Nea Kuusinen ◽  
Pauline Stenberg ◽  
Erkki Tomppo ◽  
Pierre Bernier ◽  
Frank Berninger
Keyword(s):  
Norway Spruce ◽  
Scots Pine ◽  
Boreal Forests ◽  
Near Infrared ◽  
Spectral Band ◽  
Stand Development ◽  
Area Index ◽  
Spectral Bands ◽  
Site Fertility ◽  
Infrared Spectral

Inherent variability in the spectral properties of boreal forests complicates the retrieval of canopy properties such as canopy leaf area index from satellite images. Understanding the drivers of this variability could help provide better estimates of desired canopy cover properties. Field plot data from the Finnish National Forest Inventory and Landsat thematic mapper (TM) images were used to investigate the variation in canopy and understory reflectance during stand development in coniferous boreal forests. Spectral data for each plot were obtained from the Landsat pixel within which the plot center coordinates fell. Nonlinear unmixing was used to estimate the bidirectional reflectance factors (BRFs) of the “sunlit understory” and “canopy and shaded ground” components by site fertility and stand development classes. A forest albedo model was used to estimate the contribution of diffuse radiation reflected downwards from the canopy to the sunlit understory component. The sunlit understory BRF in the near-infrared spectral band decreased as the site fertility decreased and the forest matured, whereas the sunlit understory BRFs in the red and shortwave-infrared spectral bands concurrently increased. The BRFs of the canopy and shaded ground component decreased slightly during stand development, mostly in the near-infrared spectral band. Adding the diffuse contribution to the sunlit understory component changed the estimated component BRFs only a little (0.1%–1.7%) compared with those obtained using a linear mixing assumption. This effect was largest in the near-infrared spectral band and smallest in the red spectral band. For Norway spruce plots, the measured and estimated forest variables were well correlated with the BRFs in all of the studied spectral bands, but for the Scots pine plots, the correlations were notably weaker. Results show a greater importance of the fraction of visible sunlit understory on forest reflectance in Scots pine than in Norway spruce forests.

scholarly journals ON-LINE CHANGE MONITORING WITH TRANSFORMED MULTI-SPECTRAL TIME SERIES, A STUDY CASE IN TROPICAL FOREST

2016 ◽  
Vol XLI-B7 ◽  
pp. 987-989 ◽  
Author(s):  
Meng Lu ◽  
Eliakim Hamunyela
Keyword(s):  
Time Series ◽  
Tropical Forest ◽  
Satellite Data ◽  
Vegetation Index ◽  
Forest Disturbance ◽  
Spectral Band ◽  
Satellite Image ◽  
Spectral Bands ◽  
Normalised Difference Vegetation Index ◽  
Change Monitoring

In recent years, the methods for detecting structural changes in time series have been adapted for forest disturbance monitoring using satellite data. The BFAST (Breaks For Additive Season and Trend) Monitor framework, which detects forest cover disturbances from satellite image time series based on empirical fluctuation tests, is particularly used for near real-time deforestation monitoring, and it has been shown to be robust in detecting forest disturbances. Typically, a vegetation index that is transformed from spectral bands into feature space (e.g. normalised difference vegetation index (NDVI)) is used as input for BFAST Monitor. However, using a vegetation index for deforestation monitoring is a major limitation because it is difficult to separate deforestation from multiple seasonality effects, noise, and other forest disturbance. In this study, we address such limitation by exploiting the multi-spectral band of satellite data. To demonstrate our approach, we carried out a case study in a deciduous tropical forest in Bolivia, South America. We reduce the dimensionality from spectral bands, space and time with projective methods particularly the Principal Component Analysis (PCA), resulting in a new index that is more suitable for change monitoring. Our results show significantly improved temporal delay in deforestation detection. With our approach, we achieved a median temporal lag of 6 observations, which was significantly shorter than the temporal lags from conventional approaches (14 to 21 observations).

scholarly journals Modeling radiative transfer in tropical rainforest canopies: sensitivity of simulated albedo to canopy architectural and optical parameters

2011 ◽  
Vol 83 (4) ◽  
pp. 1231-1242 ◽  
Author(s):  
Sílvia N. M. Yanagi ◽  
Marcos H. Costa
Keyword(s):  
Radiative Transfer ◽  
Near Infrared ◽  
Tropical Rainforest ◽  
Spectral Band ◽  
Canopy Structure ◽  
Optical Parameters ◽  
Strong Response ◽  
Spectral Bands ◽  
Infrared Spectral ◽  
Integrated Biosphere Simulator

This study evaluates the sensitivity of the surface albedo simulated by the Integrated Biosphere Simulator (IBIS) to a set of Amazonian tropical rainforest canopy architectural and optical parameters. The parameters tested in this study are the orientation and reflectance of the leaves of upper and lower canopies in the visible (VIS) and near-infrared (NIR) spectral bands. The results are evaluated against albedo measurements taken above the K34 site at the INPA (Instituto Nacional de Pesquisas da Amazônia) Cuieiras Biological Reserve. The sensitivity analysis indicates a strong response to the upper canopy leaves orientation (x up) and to the reflectivity in the near-infrared spectral band (rNIR,up), a smaller sensitivity to the reflectivity in the visible spectral band (rVIS,up) and no sensitivity at all to the lower canopy parameters, which is consistent with the canopy structure. The combination of parameters that minimized the Root Mean Square Error and mean relative error are Xup = 0.86, rVIS,up = 0.062 and rNIR,up = 0.275. The parameterizations performed resulted in successful simulations of tropical rainforest albedo by IBIS, indicating its potential to simulate the canopy radiative transfer for narrow spectral bands and permitting close comparison with remote sensing products.

scholarly journals Identificación de perturbaciones en el bosque húmedo tropical colombiano usando series temporales de imágenes satelitales Landsat mediante el algoritmo Landtrendr

2019 ◽  
pp. 25
Author(s):  
L. Hurtado ◽  
I. Lizarazo
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Forest Disturbance ◽  
Spectral Response ◽  
Short Wave ◽  
Forest Vegetation ◽  
Landsat Images ◽  
Spectral Bands

<p>Time series analysis of satellite images for detection of deforestation and forest disturbances at specific dates has been a subject of research over the last few years. There are many limitations to identify the exact date of deforestation due mainly to the large volume of data and the criteria required for its correct characterization. A further limitation in the analysis of multispectral time series is the identification of true deforestation considering that forest vegetation may undergo different changes over time. This study analyzes deforestation in a zone within the Colombian Amazon using the Normalized Difference Vegetation Index (NDVI) based on semestral median mosaics generated from Landsat images collected from 2000 to 2017. Several samples representing trends of change over the time series were extracted and classified according to their degree of change and persistence in the series, using four categories: (i) deforestation, (ii) degradation, (iii) forest plantation, and (iv) regeneration. Specific deforestation samples were analyzed in the same way using the soil-adjusted vegetation index (SAVI) to reduce the effect of spectral response variations due to soil reflectance changes. It is concluded that the two indices used, together with the near infrared (NIR) and short-wave infrared (SWIR 1) spectral bands, allow to extract values and intervals where the change produced by deforestation on forest vegetation is identified with acceptable accuracy. The analysis of time series using the Landtrendr algorithm confirmed a reliable change detection in each of the forest disturbance categories.</p>

scholarly journals Investigation of Sediment-Rich Glacial Meltwater Plumes Using a High-Resolution Multispectral Sensor Mounted on an Unmanned Aerial Vehicle

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2405 ◽  
Author(s):  
Wójcik ◽  
Bialik ◽  
Osińska ◽  
Figielski
Keyword(s):  
Near Infrared ◽  
Spectral Band ◽  
Correlation Coefficients ◽  
Weather Conditions ◽  
Spectral Bands ◽  
Aerial Vehicle ◽  
High Turbidity ◽  
Unfavourable Weather ◽  
Harsh Climate ◽  
Surface Ice

A Parrot Sequoia+ multispectral camera on a Parrot Bluegrass drone registered in four spectral bands (green, red, red edge (RE), and near-infrared (NIR)) to identify glacial outflow zones and determined the meltwater turbidity values in waters in front of the following Antarctic glaciers: Ecology, Dera Icefall, Zalewski, and Krak on King George Island, Southern Shetlands was used. This process was supported by a Red-Green-Blue (RGB) colour model from a Zenmuse X5 camera on an Inspire 2 quadcopter drone. Additional surface water turbidity measurements were carried out using a Yellow Springs Instruments (YSI) sonde EXO2. From this research, it was apparent that for mapping low-turbidity and medium-turbidity waters (<70 formazinenephelometricunits (FNU)), a red spectral band should be used, since it is insensitive to possible surface ice phenomena and registers the presence of both red and white sediments. High-turbidity plumes with elevated FNU values should be identified through the NIR band. Strong correlation coefficients between the reflectance at particular bands and FNU readings (RGreen = 0.85, RRed = 0.85, REdge = 0.84, and RNIR = 0.83) are shown that multispectral mapping using Unmanned Aerial Vehicles (UAVs) can be successfully usedeven in the unfavourable weather conditions and harsh climate of Antarctica. Lastly, the movement of water masses in Admiralty Bay is briefly discussed and supported by the results from EXO2 measurements.

scholarly journals Landsat NIR band and ELM-FATES sensitivity to forest disturbances and regrowth in the Central Amazon

2019 ◽  
Author(s):  
Robinson I. Negrón-Juárez ◽  
Jennifer A. Holm ◽  
Boris Faybishenko ◽  
Daniel Magnabosco-Marra ◽  
Rosie A. Fisher ◽  
...  
Keyword(s):  
Near Infrared ◽  
Forest Succession ◽  
Forest Disturbance ◽  
Terrestrial Ecosystem ◽  
Stem Density ◽  
Central Amazon ◽  
Spectral Bands ◽  
Forest Regrowth ◽  
Landsat Satellite ◽  
Successional Pathways

Abstract. Forest disturbance and regrowth are key processes in forest dynamics but detailed information of these processes is difficult to obtain in remote forests as the Amazon. We used chronosequences of Landsat satellite imagery to determine the sensitivity of surface reflectance from all spectral bands to windthrow, clearcutting, and burning and their successional pathways of forest regrowth in the Central Amazon. We also assess whether the forest demography model Functionally Assembled Terrestrial Ecosystem Simulator (FATES) implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM), ELM-FATES, accurately represents the changes for windthrow and clearcut. The results show that all spectral bands from Landsat satellite were sensitive to the disturbances but after 3 to 6 years only the Near Infrared (NIR) band had significant changes associated with the successional pathways of forest regrowth for all the disturbances considered. In general, the NIR decreased immediately after disturbance, increased to maximum values with the establishment of pioneers and early-successional tree species, and then decreased slowly and almost linearly to pre-disturbance conditions with the dynamics of forest succession. Statistical methods predict that NIR will return to pre-disturbance values in about 39 years (consistent with observational data of biomass regrowth following windthrows), and 36 and 56 years for clearcut and burning. The NIR captured the observed successional pathways of forest regrowth after clearcut and burning that diverge through time. ELM-FATES predicted higher peaks of initial forest responses (e.g., biomass, stem density) after clearcuts than after windthrows, similar to the changes in NIR. However, ELM-FATES predicted a faster recovery of forest structure and canopy-coverage back to pre-disturbance conditions for windthrows compared to clearcuts. The similarity of ELM-FATES predictions of regrowth patterns after windthrow and clearcut to those of the NIR results suggest that the dynamics of forest regrowth for these disturbances are represented with appropriate fidelity within ELM-FATES and useful as a benchmarking tool.

scholarly journals EXTRACTION OF CLOUD HEIGHTS FROM SENTINEL-2 MULTISPECTRAL IMAGES

2021 ◽  
Vol V-1-2021 ◽  
pp. 17-23
Author(s):  
T. Krauß
Keyword(s):  
Near Infrared ◽  
Moving Objects ◽  
Spectral Band ◽  
Acquisition Time ◽  
Effect Analysis ◽  
Spectral Bands ◽  
High Resolution Imagery ◽  
Digital Elevation ◽  
Stereo Imagery ◽  
Sentinel 2

Abstract. Investigation of the focal plane assembly of the Sentinel-2 satellites show slight delays in the acquisition time of different bands on different CCD lines of about 0.5 to 1 second. This effect was already exploited in the detection of moving objects in very high resolution imagery as from WorldView-2 or -3 and also already for Sentinel-2 imagery. In our study we use the four 10-m-bands 2, 3, 4 and 8 (blue, green, red and near infrared) of Sentinel-2. In the level 1C processing each spectral band gets orthorectified separately on the same digital elevation model. So on the one hand moving objects on the ground experience a shift between the spectral bands. On the other hand objects not on the ground also show a slight shift between the spectral bands depending on the height of the object above ground. In this work we use this second effect. Analysis of cloudy Sentinel-2 scenes show small shifts of only one to two pixels depending on the height of the clouds above ground. So a new method based on algorithms for deriving dense digital elevation models from stereo imagery was developed to derive the cloud heights in Sentinel-2 images from the parallax from the 10-m-bands. After detailed description of the developed method it is applied to different cloudy Sentinel-2 images and the results are cross-checked using the shadows of the clouds together with the position of the sun at acquisition time.

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