scholarly journals Monitoring Forest Infestation and Fire Disturbance in the Southern Appalachian Using a Time Series Analysis of Landsat Imagery

2020 ◽  
Vol 12 (15) ◽  
pp. 2412 ◽  
Author(s):  
Mahsa Khodaee ◽  
Taehee Hwang ◽  
JiHyun Kim ◽  
Steven P. Norman ◽  
Scott M. Robeson ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Vegetation Index ◽  
Temporal Dynamics ◽  
Landsat Imagery ◽  
Adelges Tsugae ◽  
Fire Disturbance ◽  
Aerial Images ◽  
Southern Appalachian ◽  
Hemlock Decline

The southern Appalachian forests have been threatened by several large-scale disturbances, such as wildfire and infestation, which alter the forest ecosystem structures and functions. Hemlock Woolly Adelgid (Adelges tsugae Annand, HWA) is a non-native pest that causes widespread foliar damage and eventual mortality, resulting in irreversible tree decline in eastern (Tsuga canadensis) and Carolina (T. caroliniana) hemlocks throughout the eastern United States. It is important to monitor the extent and severity of these disturbances over space and time to better understand their implications in the biogeochemical cycles of forest landscapes. Using all available Landsat images, we investigate and compare the performance of Tasseled Cap Transformation (TCT)-based indices, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Disturbance Index (DI) in capturing the spectral-temporal trajectory of both abrupt and gradual forest disturbances (e.g., fire and hemlock decline). For each Landsat pixel, the temporal trajectories of these indices were fitted into a time series model, separating the inter-annual disturbance patterns (low frequency) and seasonal phenology (high frequency) signals. We estimated the temporal dynamics of disturbances based on the residuals between the observed and predicted values of the model, investigated the performance of all the indices in capturing the hemlock decline intensity, and further validated the results with the number of individual dead hemlocks identified from high-resolution aerial images. Our results suggested that the overall performance of NDVI, followed by TCT wetness, was most accurate in detecting both the disturbance timing and hemlock decline intensity, explaining over 90% of the variability in the number of dead hemlocks. Despite the overall good performance of TCT wetness in characterizing the disturbance regime, our analysis showed that this index has some limitations in characterizing disturbances due to its recovery patterns following infestation.

scholarly journals The NDVI algorithm utilization on the google earth engine platform to monitor changes in forest density in mining area

2021 ◽  
Vol 886 (1) ◽  
pp. 012100
Author(s):  
Munajat Nursaputra ◽  
Siti Halimah Larekeng ◽  
Nasri ◽  
Andi Siady Hamzah
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Large Scale ◽  
Vegetation Index ◽  
Forest Degradation ◽  
Google Earth ◽  
Aerial Images ◽  
Forest Monitoring ◽  
Forest Density ◽  
Google Earth Engine

Abstract Periodic forest monitoring needs to be done to avoid forest degradation. In general, forest monitoring can be conducted manually (field surveys) or using technological innovations such as remote sensing data derived from aerial images (drone results) or cloud computing-based image processing. Currently, remote sensing technology provides large-scale forest monitoring using multispectral sensors and various vegetation index processing algorithms. This study aimed to evaluate the use of the Google Earth Engine (GEE) platform, a geospatial dataset platform, in the Vale Indonesia mining concession area to improve accountable forest monitoring. This platform integrates a set of programming methods with a publicly accessible time-series database of satellite imaging services. The method used is NDVI processing on Landsat multispectral images in time series format, which allows for the description of changes in forest density levels over time. The results of this NDVI study conducted on the GEE platform have the potential to be used as a tool and additional supporting data for monitoring forest conditions and improvement in mining regions.

scholarly journals Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery

2020 ◽  
Vol 12 (18) ◽  
pp. 3038
Author(s):  
Dhahi Al-Shammari ◽  
Ignacio Fuentes ◽  
Brett M. Whelan ◽  
Patrick Filippi ◽  
Thomas F. A. Bishop
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Google Earth ◽  
Growth Stages ◽  
Landsat 8 ◽  
Crop Type ◽  
Eastern Australia ◽  
Normalised Difference Vegetation Index ◽  
Cotton Fields

A phenology-based crop type mapping approach was carried out to map cotton fields throughout the cotton-growing areas of eastern Australia. The workflow was implemented in the Google Earth Engine (GEE) platform, as it is time efficient and does not require processing in multiple platforms to complete the classification steps. A time series of Normalised Difference Vegetation Index (NDVI) imagery were generated from Landsat 8 Surface Reflectance Tier 1 (L8SR) and processed using Fourier transformation. This was used to produce the harmonised-NDVI (H-NDVI) from the original NDVI, and then phase and amplitude values were generated from the H-NDVI to visualise active cotton in the targeted fields. Random Forest (RF) models were built to classify cotton at early, mid and late growth stages to assess the ability of the model to classify cotton as the season progresses, with phase, amplitude and other individual bands as predictors. Results obtained from leave-one-season-out cross validation (LOSOCV) indicated that Overall Accuracy (OA), Kappa, Producer’s Accuracies (PA) and User’s Accuracy (UA), increased significantly when adding amplitude and phase as predictor variables to the model, than prediction using H-NDVI or raw bands only. Commission and omission errors were reduced significantly as the season progressed and more in-season imagery was available. The methodology proposed in this study can map cotton crops accurately based on the reconstruction of the unique cotton reflectance trajectory through time. This study confirms the importance of phenological metrics in improving in-season cotton fields mapping across eastern Australia. This model can be used in conjunction with other datasets to forecast yield based on the mapped crop type for improved decision making related to supply chain logistics and seasonal outlooks for production.

scholarly journals Technical Note: Development of a Landsat Time Series for Application in Forest Status Assessment in the Inland Northwest United States

2008 ◽  
Vol 23 (1) ◽  
pp. 53-62 ◽  
Author(s):  
Russell N. Beck ◽  
Paul E. Gessler
Keyword(s):  
United States ◽  
Time Series ◽  
Vegetation Dynamics ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Satellite Image ◽  
Temporal Trends ◽  
Monitoring Cost ◽  
Small Pilot Study ◽  
Inland Northwest

Abstract The Inland Northwest United States contains extensive areas of complex, inaccessible terrain requiring significant resource expenditure for forest inventory, assessment, and monitoring. Cost-effective methods are necessary for annual broad-scale assessment of forest condition over complex terrain. Proficiency in the use of timely satellite image products along with spatial analysis tools such as geographic information systems can assist natural resource managers to understand regional dynamics and change within these landscapes. Satellite-derived vegetation indices such as the normalized difference vegetation index (NDVI) can effectively assess and monitor vegetation dynamics of large remote areas. This article presents a newly developed archive and example methods for monitoring forest dynamics through the creation of NDVI departure maps. The NDVI products were generated from a time series of Landsat imagery (1989–2004) to derive both density distributions and a long-term departure from average map for any year or series of years within the time series archive. A preliminary application of the data is demonstrated showing temporal trends of vegetation dynamics relating to harvesting and management within two small pilot study areas in north Idaho.

scholarly journals Generating annual estimates of forest fire disturbance in Canada: the National Burned Area Composite

2020 ◽  
Vol 29 (10) ◽  
pp. 878 ◽  
Author(s):  
R. J. Hall ◽  
R. S. Skakun ◽  
J. M. Metsaranta ◽  
R. Landry ◽  
R.H. Fraser ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat Imagery ◽  
Fire Disturbance ◽  
Automated System ◽  
Primary Data ◽  
Burned Area ◽  
Average Accuracy ◽  
Mapping System

Determining burned area in Canada across fire management agencies is challenging because of different mapping scales and methods. The inconsistent removal of unburned islands and water features from within burned polygon perimeters further complicates the problem. To improve the determination of burned area, the Canada Centre for Mapping and Earth Observation and the Canadian Forest Service developed the National Burned Area Composite (NBAC). The primary data sources for this tool are an automated system to derive fire polygons from 30-m Landsat imagery (Multi-Acquisition Fire Mapping System) and high-quality agency polygons delineated from imagery with spatial resolution ≤30m. For fires not mapped by these sources, the Hotspot and Normalized Difference Vegetation Index Differencing Synergy method was used with 250–1000-m satellite data. From 2004 to 2016, the National Burned Area Composite reported an average of 2.26 Mha burned annually, with considerable interannual variability. Independent assessment of Multi-Acquisition Fire Mapping System polygons achieved an average accuracy of 96% relative to burned-area data with high spatial resolution. Confidence intervals for national area burned statistics averaged±4.3%, suggesting that NBAC contributes relatively little uncertainty to current estimates of the carbon balance of Canada’s forests.

scholarly journals Assessing the Use of Sentinel-2 Time Series Data for Monitoring Cork Oak Decline in Portugal

2019 ◽  
Vol 11 (21) ◽  
pp. 2515 ◽  
Author(s):  
Ana Navarro ◽  
Joao Catalao ◽  
Joao Calvao
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Temporal Dynamics ◽  
Cork Oak ◽  
Series Data ◽  
Tree Level ◽  
Oak Decline ◽  
Sentinel 2

In Portugal, cork oak (Quercus suber L.) stands cover 737 Mha, being the most predominant species of the montado agroforestry system, contributing to the economic, social and environmental development of the country. Cork oak decline is a known problem since the late years of the 19th century that has recently worsened. The causes of oak decline seem to be a result of slow and cumulative processes, although the role of each environmental factor is not yet established. The availability of Sentinel-2 high spatial and temporal resolution dense time series enables monitoring of gradual processes. These processes can be monitored using spectral vegetation indices (VI) as their temporal dynamics are expected to be related with green biomass and photosynthetic efficiency. The Normalized Difference Vegetation Index (NDVI) is sensitive to structural canopy changes, however it tends to saturate at moderate-to-dense canopies. Modified VI have been proposed to incorporate the reflectance in the red-edge spectral region, which is highly sensitive to chlorophyll content while largely unaffected by structural properties. In this research, in situ data on the location and vitality status of cork oak trees are used to assess the correlation between chlorophyll indices (CI) and NDVI time series trends and cork oak vitality at the tree level. Preliminary results seem to be promising since differences between healthy and unhealthy (diseased/dead) trees were observed.

Assessment of different indices from Landsat time series in burned area mapping

2020 ◽  
Author(s):  
Jinxiu Liu
Keyword(s):  
Time Series ◽  
Burkina Faso ◽  
Land Surface ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Burned Area ◽  
Free Access ◽  
Landsat Images ◽  
Area Index ◽  
Burned Area Mapping

<p>Fire is recognized as an important land surface disturbance, as it influences terrestrial carbon cycle, climate and biodiversity. Accurate and efficient mapping of burned area is beneficial for social and environmental applications. Remote sensing plays a key role in detecting burned areas and active fires from reginal to global scales. Due to the free access to the Landsat archive, studies using dense time series of Landsat imagery for burned area mapping are appearing and increasing. However, the performance of Landsat time series when using different indices for burned area mapping has not been assessed. In this study, the objective was to identify which indices can detect burned area better when using Landsat time series in savanna area of southern Burkina Faso. We selected Burned Area Index (BAI), Normalized Burned Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Global Environmental Monitoring Index (GEMI) for comparison as they are commonly used indices for burned area detection. The algorithm was based on breakpoint identification and burned pixel detection using harmonic model fitting with different indices Landsat time series. It was tested in savanna area in southern Burkina Faso over 16 years with 281 Landsat images ranging from October 2000 to April 2016.The same reference data was used to evaluate the performance of burned area detection with different indices Landsat time series. The result demonstrated that BAI was the most accurate in burned area detection from Landsat time series, followed by NBR, GEMI and NDVI.</p>

scholarly journals Mapping Maize Cultivated Area Combining MODIS EVI Time Series and the Spatial Variations of Phenology over Huanghuaihai Plain

2020 ◽  
Vol 10 (8) ◽  
pp. 2667 ◽  
Author(s):  
Xueting Wang ◽  
Sha Zhang ◽  
Lili Feng ◽  
Jiahua Zhang ◽  
Fan Deng
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Regression Equations ◽  
Summer Maize ◽  
Temporal Shift ◽  
Modis Evi ◽  
Multi Temporal ◽  
Moderate Resolution Imaging Spectroradiometer

Crop phenology is a significant factor that affects the precision of crop area extraction by using the multi-temporal vegetation indices (VIs) approach. Considering the phenological differences of maize among the different regions, the summer maize cultivated area was estimated by using enhanced vegetation index (EVI) time series images from the Moderate Resolution Imaging Spectroradiometer (MODIS) over the Huanghuaihai Plain in China. By analyzing the temporal shift in summer maize calendars, linear regression equations for simulating the summer maize phenology were obtained. The simulated maize phenology was used to correct the MODIS EVI time series curve of summer maize. Combining the mean absolute distance (MAD) and p-tile algorithm, the cultivated areas of summer maize were distinguished over the Hunaghuaihai Plain. The accuracy of the extraction results in each province was above 85%. Comparing the maize area of two groups from MODIS-estimated and statistical data, the validation results showed that the R2 reached 0.81 at the city level and 0.69 at the county level. It demonstrated that the approach in this study has the ability to effectively map the summer maize area over a large scale and provides a novel idea for estimating the planting area of other crops.

scholarly journals A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland

2020 ◽  
Vol 57 (8) ◽  
pp. 1102-1124
Author(s):  
M. Mahdianpari ◽  
H. Jafarzadeh ◽  
J. E. Granger ◽  
F. Mohammadimanesh ◽  
B. Brisco ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Landsat Imagery ◽  
Google Earth ◽  
Scale Change ◽  
Large Scale Change ◽  
Google Earth Engine ◽  
Change Monitoring

scholarly journals Assessing the Use of Sentinel-2 Time Series for Monitoring Cork Oak Decline in Portugal

2019 ◽  
Author(s):  
Ana Navarro ◽  
João Catalão ◽  
João Calvão
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Dynamics ◽  
Vegetation Indices ◽  
Quercus Suber ◽  
Cork Oak ◽  
Tree Level ◽  
Oak Decline ◽  
Sentinel 2

In Portugal, cork oak (Quercus suber L.) stands cover 737 Mha, being the most predominant species of the montado agroforestry system, contributing for the economic, social and environmental development of the country. Cork oak decline is a known problem since the late years of the 19th century that has recently worsen. The causes of oak decline seem to be a result of slow and cumulative processes, although the role of each environmental factor is not yet established. The availability of Sentinel-2 high spatial and temporal resolution dense time series enables gradual processes monitoring. These processes can be monitored using spectral vegetation indices (VI) once their temporal dynamics are expected to be related with green biomass and photosynthetic efficiency. The Normalized Difference Vegetation Index (NDVI) is sensitive to structural canopy changes, however it tends to saturate at moderate-to-dense canopies. Modified VI have been proposed to incorporate the reflectance in the red-edge spectral region, which is highly sensitive to chlorophyll content while largely unaffected by structural properties. In this research, in-situ data on the location and vitality status of cork oak trees are used to assess the correlation between chlorophyll indices (CI) and NDVI time series trends and cork oak vitality at the tree level. Preliminary results seem to be promising since differences between healthy and unhealthy (diseased/dead) trees were observed.

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