scholarly journals Mapping Forest Composition with Landsat Time Series: An Evaluation of Seasonal Composites and Harmonic Regression

2020 ◽  
Vol 12 (4) ◽  
pp. 610 ◽  
Author(s):  
Bryce Adams ◽  
Louis Iverson ◽  
Stephen Matthews ◽  
Matthew Peters ◽  
Anantha Prasad ◽  
...  
Keyword(s):  
Time Series ◽  
Forest Type ◽  
Vegetation Indices ◽  
Feature Space ◽  
Forest Composition ◽  
Landsat 8 ◽  
Forest Types ◽  
Young Forest ◽  
Thematic Resolution

The Landsat program has long supported pioneering research on the recovery of forest information by remote sensing technologies for several decades, and efforts to improve the thematic resolution and accuracy of forest compositional products remains an area of continued innovation. Recent development and application of Landsat time series analysis offers unique opportunities for quantifying seasonality and trend components among different forest types for developing alternative feature sets for forest vegetation mapping. Within a large forested landscape in Southeastern Ohio, USA, we examined the use of harmonic metrics developed from time series of all available Landsat-8 observations (2013–2019) relative to seasonal image composites, including accompanying spectral components and vegetation indices. A reference dataset among three sources was integrated and used to categorize forest inventory data into seven forest type classes and gradient compositional response. Results showed that the combination of harmonic metrics and topographic variables achieved an accuracy agreement with the reference data of 74.9% relative to seasonal composites (71.6%) and spectral indices (70.3%). Differences in agreement were attributed to improved discrimination of three heterogeneous upland hardwood classes and an early-successional, young forest class, all forest types of primary interest among managers across the region. Variable importance metrics often identified the cosine and sine terms that quantify the seasonality in spectral values in the harmonic feature space, suggesting these aspects best support the characterization of forest types at greater thematic detail than seasonal compositing procedures. This study demonstrates how advanced time series metrics can improve forest type modeling and forest gradient quantifications, thus showcasing a need for continued exploration of such approaches across different forest types.

scholarly journals Systematic Water Fraction Estimation for a Global and Daily Surface Water Time-Series

2021 ◽  
Vol 13 (14) ◽  
pp. 2675
Author(s):  
Stefan Mayr ◽  
Igor Klein ◽  
Martin Rutzinger ◽  
Claudia Kuenzer
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Spatial Resolution ◽  
Pure Water ◽  
Feature Space ◽  
Observation Time ◽  
Temporal Information ◽  
Landsat 8 ◽  
Water Fraction ◽  
Classification Probability

Fresh water is a vital natural resource. Earth observation time-series are well suited to monitor corresponding surface dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on globally distributed inland surface water based on MODIS (Moderate Resolution Imaging Spectroradiometer) images at 250 m spatial resolution. Operating on this spatiotemporal level comes with the drawback of moderate spatial resolution; only coarse pixel-based surface water quantification is possible. To enhance the quantitative capabilities of this dataset, we systematically access subpixel information on fractional water coverage. For this, a linear mixture model is employed, using classification probability and pure pixel reference information. Classification probability is derived from relative datapoint (pixel) locations in feature space. Pure water and non-water reference pixels are located by combining spatial and temporal information inherent to the time-series. Subsequently, the model is evaluated for different input sets to determine the optimal configuration for global processing and pixel coverage types. The performance of resulting water fraction estimates is evaluated on the pixel level in 32 regions of interest across the globe, by comparison to higher resolution reference data (Sentinel-2, Landsat 8). Results show that water fraction information is able to improve the product’s performance regarding mixed water/non-water pixels by an average of 11.6% (RMSE). With a Nash-Sutcliffe efficiency of 0.61, the model shows good overall performance. The approach enables the systematic provision of water fraction estimates on a global and daily scale, using only the reflectance and temporal information contained in the input time-series.

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

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

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

Monthly Litterfall Dynamics in Three Different Forest Types in Jeju Island, South Korea

2018 ◽  
Author(s):  
Ji Young An ◽  
Si Ho Han ◽  
Woo Bin Youn ◽  
Sang Ick Lee ◽  
Afroja Rahman ◽  
...  
Keyword(s):  
Nutrient Cycling ◽  
Leaf Litter ◽  
Dominant Species ◽  
Forest Type ◽  
Jeju Island ◽  
Forest Composition ◽  
Forest Types ◽  
Major Pathway ◽  
Litterfall Production ◽  
High Precipitation

In a forest ecosystem, the major pathway for carbon and nutrient cycling is through litterfall, which has been influenced by physical and biological factors. The purpose of this study was to investigate monthly litterfall production in three forests in Jeju Island differentiated based on precipitation and forest composition: Chungsu (&lt;i&gt;Quercus glauca&lt;/i&gt; as the dominant species; low precipitation), Seonheul&lt;sub&gt;b&lt;/sub&gt; (&lt;i&gt;Q. glauca&lt;/i&gt; as the dominant species; high precipitation), and Seonheul&lt;sub&gt;m&lt;/sub&gt; (&lt;i&gt;Q. glauca&lt;/i&gt; and &lt;i&gt;Pinus thunbergii&lt;/i&gt; as the dominant species; high precipitation). Litterfall was collected monthly from April to December 2015 and divided into leaf litter, twig, bark, seeds, and unidentified materials. The amount of leaf litter by species varied by stand, but leaf litter and total litterfall were very similar among stands, ranging from 362 g m&lt;sup&gt;-2&lt;/sup&gt; to 375 g m&lt;sup&gt;-2&lt;/sup&gt; for leaf litter and 524 g m&lt;sup&gt;-2&lt;/sup&gt; to 580 g m&lt;sup&gt;-2&lt;/sup&gt; for total litterfall. However, oak leaf litter was the highest in May, but needle litter was the highest in December. Forest type and climate factor had no influence on the amount of litterfall in the studied forests while the litterfall production by species showed considerable seasonal variation, resulting in varying effects on carbon and nutrient cycling in these forests.

scholarly journals Estimation of rice phenology date using integrated HJ-1 CCD and Landsat-8 OLI vegetation indices time-series images

2015 ◽  
Vol 16 (10) ◽  
pp. 832-844 ◽  
Author(s):  
Jing Wang ◽  
Jing-feng Huang ◽  
Xiu-zhen Wang ◽  
Meng-ting Jin ◽  
Zhen Zhou ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Indices ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Time Series Images ◽  
Rice Phenology

scholarly journals USING LANDSAT 8 IMAGE TIME SERIES FOR CROP MAPPING IN A REGION OF CERRADO, BRAZIL

2016 ◽  
Vol XLI-B8 ◽  
pp. 845-850 ◽  
Author(s):  
H. Bendini ◽  
I. D. Sanches ◽  
T. S. Körting ◽  
L. M. G. Fonseca ◽  
A. J. B. Luiz ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Agricultural Land Use ◽  
Landsat 8 ◽  
Land Use Classification ◽  
Crop Mapping ◽  
Phenological Metrics

The objective of this research is to classify agricultural land use in a region of the Cerrado (Brazilian Savanna) biome using a time series of Enhanced Vegetation Index (EVI) from Landsat 8 OLI. Phenological metrics extracted from EVI time series, a Random Forest algorithm and data mining techniques are used in the process of classification. The area of study is a region in the Cerrado in a region of the municipality of Casa Branca, São Paulo state, Brazil. The results are encouraging and demonstrate the potential of phenological parameters obtained from time series of OLI vegetation indices for agricultural land use classification.

scholarly journals USING LANDSAT 8 IMAGE TIME SERIES FOR CROP MAPPING IN A REGION OF CERRADO, BRAZIL

2016 ◽  
Vol XLI-B8 ◽  
pp. 845-850
Author(s):  
H. Bendini ◽  
I. D. Sanches ◽  
T. S. Körting ◽  
L. M. G. Fonseca ◽  
A. J. B. Luiz ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Agricultural Land Use ◽  
Landsat 8 ◽  
Land Use Classification ◽  
Crop Mapping ◽  
Phenological Metrics

The objective of this research is to classify agricultural land use in a region of the Cerrado (Brazilian Savanna) biome using a time series of Enhanced Vegetation Index (EVI) from Landsat 8 OLI. Phenological metrics extracted from EVI time series, a Random Forest algorithm and data mining techniques are used in the process of classification. The area of study is a region in the Cerrado in a region of the municipality of Casa Branca, São Paulo state, Brazil. The results are encouraging and demonstrate the potential of phenological parameters obtained from time series of OLI vegetation indices for agricultural land use classification.

Crop phenology monitoring from Landsat 8 and Sentinel-2 green LAI time series at the Nile Delta

2021 ◽  
Author(s):  
Eatidal Amin ◽  
Santiago Belda ◽  
Luca Pipia ◽  
Zoltan Szantoi ◽  
Ahmed El Baroudy ◽  
...  
Keyword(s):  
Time Series ◽  
Nile Delta ◽  
Vegetation Indices ◽  
Farming Systems ◽  
Landsat 8 ◽  
Crop Phenology ◽  
Area Index ◽  
Double Cropping ◽  
Crop Type ◽  
Sentinel 2

&lt;p&gt;Monitoring of crop phenology significantly assists agricultural managing practices and plays an important role in crop yield predictions. Multi-temporal satellite-based observations allow analyzing vegetation seasonal dynamics over large areas by using vegetation indices or deriving biophysical variables. The Northern Nile Delta represents about half of all agricultural lands of Egypt. In this region, intensifying farming systems are predominant, which translates into a pressure on water supply demand. Moreover, double cropping rotations schemes are increasing, requiring a high temporal and spatial resolution monitoring for capturing successive crop growth cycles. This study presents a framework for crop phenological characterization based on high spatial and temporal resolution time series of green Leaf Area Index (LAI). Particularly, NASA's Harmonized Landsat 8 and Sentinel-2 (HLS) surface reflectance dataset was used. The HLS dataset provides seamless products from both satellites, enabling global land observations every 2-3 days at 30m. A green LAI retrieval model was originally trained using ground-based LAI measurements with Gaussian processes technique and validated for Sentinel-2 (R2: 0.7, RMSE= 0.67m2/m2) (Amin et al., 2020). Given the compatible spectral bands configuration of both sensors, a new model for Landsat 8 was adapted from the original one. Both models were implemented in an HLS image based automated retrieval chain obtaining therefore two different LAI time series, which were spatially averaged per crop parcel according to the ground data at disposal. The subsequent analysis was performed based on the time series phenological pre-processing and modelling implemented in the in-house developed scientific time series toolbox DATimeS (Belda et al., 2020). The proposed framework permitted to determine the crop patterns for four consecutive years (2016-2019), identifying one or two seasons per year, for single (e.g. grape, citrus) or double-cropping (e.g. maize-onion, maize-wheat, rice-clover), respectively. Alongside, each detected crop was characterized by retrieving a selected set of phenological parameters, which were contrasted with respect to the established crop type calendar (planting and harvesting dates) and for each crop type, the annual mean value was computed and the intra annual variability within the four years was assessed.&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Amin, E., Verrelst, J., Rivera-Caicedo, J. P., Pipia, L., Ruiz-Verd&amp;#250;, A., &amp; Moreno, J. (2020). Prototyping Sentinel-2 green LAI and brown LAI products for cropland monitoring. Remote Sensing of Environment, 112168.&lt;/p&gt;&lt;p&gt;Belda, S., Pipia, L., Morcillo-Pallar&amp;#233;s, P., Rivera-Caicedo, J. P., Amin, E., De Grave, C., &amp; Verrelst, J. (2020). DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection. Environmental Modelling &amp; Software, 104666.&lt;/p&gt;

scholarly journals Forest type mapping using object-based classification method in Kapilvastu district, Nepal

2016 ◽  
Vol 26 (1) ◽  
pp. 38-44
Author(s):  
A. K. Chaudhary ◽  
A. K. Acharya ◽  
S. Khanal
Keyword(s):  
Nearest Neighbor ◽  
Forest Type ◽  
Accuracy Assessment ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Systematic Sampling ◽  
Landsat 8 ◽  
Forest Types ◽  
Shorea Robusta ◽  
Object Based

In the recent years, object-based image analysis (OBIA) approach has emerged with an attempt to overcome limitations inherited in conventional pixel-based approaches. OBIA was performed using Landsat 8 image to map the forest types in Kapilvastu district of Nepal. Systematic sampling design was adopted to establish sample points in the field, and 70% samples were used for classification and 30% samples for accuracy assessment. Landsat image was pre-processed, and the slope and aspect derived from the ASTER DEM were used as additional predictors for classification. Segmentation was done using eCognition v8.0 with the scale parameter of 20, ratios of 0.1 and 0.9 for shape and color, respectively. Classification and Regression Tree (CART) and nearest neighbor classifier (k-NN) methods were used for object-based classification. The major forest types observed in the district were KS (Acacia catechu/ Dalbergia sissoo), Sal (Shorea robusta) and Tropical Mixed Hardwood. The k-NN classification technique showed higher overall accuracy than the CART method. The classification approach used in this study can also be applied to classify forest types in other districts. Improvement in classification accuracy can be potentially obtained through inclusion of sufficient samples from all classes.Banko JanakariA Journal of Forestry Information for NepalVol. 26, No. 1, Page: 38-44, 2016

scholarly journals Coupled Spatiotemporal Characterization of Monsoon Cloud Cover and Vegetation Phenology

2019 ◽  
Vol 11 (10) ◽  
pp. 1203 ◽  
Author(s):  
Daniel Sousa ◽  
Christopher Small ◽  
Andrew Spalton ◽  
Andy Kwarteng
Keyword(s):  
Time Series ◽  
Interannual Variability ◽  
Cloud Cover ◽  
Feature Space ◽  
Spatial Extent ◽  
Seasonal Precipitation ◽  
Vegetation Phenology ◽  
Elevation Gradients ◽  
Temporal Feature

In monsoonal ecosystems, vegetation phenology is generally modulated by the timing and intensity of seasonal precipitation. Seasonal precipitation is often characterized by substantial interannual variability in both space and time. A rigorous quantitative understanding of the ecology of the landscape requires spatially explicit information regarding the strength of the relationship between seasonal precipitation and vegetation phenology, as well as the interannual variability of the system. For this information to be accurately estimated, it must be based on spatially and temporally consistent measurements. The optical satellite image archive can provide these measurements. Satellite imagery offers observations of both a) atmospheric parameters such as the timing and spatial extent of monsoon cloud cover; and, b) phenological parameters, such as the timing and spatial extent of vegetation green-up and senescence. This work presents a method to capture both atmospheric and phenological parameters from an optical image time series. The method uses Empirical Orthogonal Function (EOF) analysis of a single spectral index for unified characterization of the spatiotemporal dynamics of both monsoon cloud cover and vegetation phenology. This is made possible by leveraging well-understood differences in the visible and near infrared reflectance of green vegetation, soil, and clouds. Image time series are transformed into a temporal feature space (TFS) that is comprised of low-order Principal Components. The structure of the temporal feature space reveals spatiotemporally distinct annual cycles of both cloud cover and vegetation phenology. In order to illustrate this technique, we apply it to the retrospective analysis of a seasonal cloud forest in the Dhofar Mountains of the southern Arabian Peninsula. Our results quantify known (but previously unmapped) local gradients in monsoon duration and vegetation community response. Individual ecological subsystems are also clearly distinguishable from each other, and consistent elevation gradients emerge within each subsystem. Novel observations also emerge, such as regreening/early greening events and spatial patterns in cloud duration. The method is conceptually straightforward and could be applied to characterize other monsoon environments anywhere on Earth.

scholarly journals Wheat Yield Forecasting Based on Landsat NDVI and SAVI Time Series

2020 ◽  
Author(s):  
Odunayo David Adeniyi ◽  
Andrea Szabo ◽  
János Tamás ◽  
Attila Nagy
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Wheat Yield ◽  
Image Data ◽  
Efficiency Index ◽  
Landsat 8 ◽  
Large Area ◽  
Yield Data

Due to increase demand of food grain in the world, assessment of yield before actual production is important in making policies and decisions in agricultural production system. For a large area, forecast models developed from vegetation indices derived from remote sensing satellite data possesses the potential to give quantitative and timely information on crops over large areas. Different vegetation indices are being made used for this purpose, however, their efficiency in estimating crop yield is needed to be certainly tested. In this study, wheat yield forecast was derived by regressing ground truthing yield data against time series of spatial vegetation indices for the 2013 to 2019 growing seasons. These spatial vegetation indices derived from Landsat 8 image data: Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were compared to evaluate the most appropriate index that performs better in forecasting wheat production at Karcag, Kunhegyes and Ecsegfalva settlements in J&aacute;sz-Nagykun-Szolnok county, in the Northern Great Plain region of central Hungary. The best time for making wheat yield prediction with Landsat 8- SAVI and NDVI was found to be the beginning of ripening period (160th day of the year) with higher correlation between the vegetation indices and the wheat yield. The validation results revealed that the model from SAVI provides more consistent and accurate forecasts yield compared to NDVI. The SAVI model forecast yield for the validation years, 2018 and 2019 were within 6.00% and 4.41% of the final reported values while that of NDVI model were within 8.31% and 6.27%. Nash-Sutcliffe efficiency index is positive with E1= 0.99 for the model from SAVI and for NDVI, E1=0.57, which connote that the forecasting method developed and evaluated performs acceptable forecast efficiency.

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