scholarly journals Characterizing over Four Decades of Forest Disturbance in Minnesota, USA

Forests ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 362 ◽  
Author(s):  
Jody Vogeler ◽  
Robert Slesak ◽  
Patrick Fekety ◽  
Michael Falkowski
Keyword(s):  
Time Series ◽  
Spatial Information ◽  
Forest Disturbance ◽  
Temporal Trends ◽  
Land Protection ◽  
Commission Errors ◽  
Protection Information ◽  
Disturbed Forest ◽  
Agent Of Change

Spatial information about disturbance driven patterns of forest structure and ages across landscapes provide a valuable resource for all land management efforts including cross-ownership collaborative forest treatments and restoration. While disturbance events in general are known to impact stand characteristics, the agent of change may also influence recovery and the supply of ecosystem services. Our study utilizes the full extent of the Landsat archive to identify the timing, extent, magnitude, and agent, of the most recent fast disturbance event for all forested lands within Minnesota, USA. To account for the differences in the Landsat sensors through time, specifically the coarser spatial, spectral, and radiometric resolutions of the early MSS sensors, we employed a two-step approach, first harmonizing spectral indices across the Landsat sensors, then applying a segmentation algorithm to fit temporal trends to the time series to identify abrupt forest disturbance events. We further incorporated spectral, topographic, and land protection information in our classification of the agent of change for all disturbance patches. After allowing two years for the time series to stabilize, we were able to identify the most recent fast disturbance events across Minnesota from 1974–2018 with a change versus no-change validation accuracy of 97.2% ± 1.9%, and higher omission (14.9% ± 9.3%) than commission errors (1.6% ± 1.9%) for the identification of change patches. Our classification of the agent of change exhibited an overall accuracy of 96.5% ± 1.9% with classes including non-disturbed forest, land conversion, fire, flooding, harvest, wind/weather, and other rare natural events. Individual class errors varied, but all class user and producer accuracies were above 78%. The unmatched nature of the Landsat archive for providing comparable forest attribute and change information across more than four decades highlights the value of the totality of the Landsat program to the larger geospatial, ecological research, and forest management communities.

scholarly journals Mapping Forest Disturbance Due to Selective Logging in the Congo Basin with RADARSAT-2 Time Series

2021 ◽  
Vol 13 (4) ◽  
pp. 740
Author(s):  
Oleg Antropov ◽  
Yrjö Rauste ◽  
Jaan Praks ◽  
Frank Martin Seifert ◽  
Tuomas Häme
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Forest Disturbance ◽  
Selective Logging ◽  
Congo Basin ◽  
Optical Data ◽  
Textural Features ◽  
Before And After ◽  
Disturbed Forest ◽  
Log Ratio

Dense time series of stripmap RADARSAT-2 data acquired in the Multilook Fine mode were used for detecting and mapping the extent of selective logging operations in the tropical forest area in the northern part of the Republic of the Congo. Due to limited radiometric sensitivity to forest biomass variation at C-band, basic multitemporal change detection approach was supplemented by spatial texture analysis to separate disturbed forest from intact. The developed technique primarily uses multi-temporal aggregation of orthorectified synthetic aperture radar (SAR) imagery that are acquired before and after the logging operations. The actual change analysis is based on textural features of the log-ratio image calculated using two SAR temporal composites compiled of SAR scenes acquired before and after the logging operations. Multitemporal aggregation and filtering of SAR scenes decreased speckle and made the extracted textural features more prominent. The overall detection accuracy was around 80%, with some underestimation of the area of forest disturbance compared to reference based on optical data. The user’s accuracy for disturbed forest varied from 76.7% to 94.9% depending on the accuracy assessment approach. We conclude that change detection utilizing RADARSAT-2 time series represents a useful instrument to locate areas of selective logging in tropical forests.

scholarly journals Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping

2020 ◽  
Vol 12 (4) ◽  
pp. 727 ◽  
Author(s):  
Manuela Hirschmugl ◽  
Janik Deutscher ◽  
Carina Sobe ◽  
Alexandre Bouvet ◽  
Stéphane Mermoz ◽  
...  
Keyword(s):  
Time Series ◽  
Patch Size ◽  
Forest Disturbance ◽  
Optical Data ◽  
Landsat 8 ◽  
Data Sets ◽  
Disturbed Forest ◽  
Optical Time ◽  
The Individual ◽  
Sentinel 2

Frequent cloud cover and fast regrowth often hamper topical forest disturbance monitoring with optical data. This study aims at overcoming these limitations by combining dense time series of optical (Sentinel-2 and Landsat 8) and SAR data (Sentinel-1) for forest disturbance mapping at test sites in Peru and Gabon. We compare the accuracies of the individual disturbance maps from optical and SAR time series with the accuracies of the combined map. We further evaluate the detection accuracies by disturbance patch size and by an area-based sampling approach. The results show that the individual optical and SAR based forest disturbance detections are highly complementary, and their combination improves all accuracy measures. The overall accuracies increase by about 3% in both areas, producer accuracies of the disturbed forest class increase by up to 25% in Peru when compared to only using one sensor type. The assessment by disturbance patch size shows that the amount of detections of very small disturbances (< 0.2 ha) can almost be doubled by using both data sets: for Gabon 30% as compared to 15.7–17.5%, for Peru 80% as compared to 48.6–65.7%.

scholarly journals Satellite Imagery for Classification of Rice Growth Phase Using Freeman Decomposition in Indramayu, West Java, Indonesia

2018 ◽  
Vol 25 (3) ◽  
pp. 126
Author(s):  
Rian Nurtyawan ◽  
Asep Saepuloh ◽  
Agung Budi Harto ◽  
Ketut Wikantika ◽  
Akihiko Kondoh
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Temporal Trends ◽  
Rice Agriculture ◽  
Crop Monitoring ◽  
Rice Plants ◽  
Phase Development ◽  
Crop Type ◽  
Fine Mode

  Monitoring at every growth of rice plants is an important information for determining the grain pro-duction estimation of rice. Monitoring must to be have timely work on the rice plant development. However, timely monitoring and the high accuracy of information is a challenge in remote sensing based on rice agriculture monitoring and observation. With increased quality of synthetic aperture radar (SAR) systems utilizing polarimetric information recently, the development and applications of polarimetric SAR (PolSAR) are one of the current major topics in radar remote sensing. The ad-vantages provided by PolSAR data for agricultural monitoring have been extensively studied for applications such as crop type classification and mapping, crop phenology monitoring, productivity assessment based on the sensitivity of polarimetric parameters to indicators of crop conditions. Freeman and Durden successfully decomposed fully PolSAR data into three components: Single bounce, double bounce, and volume scattering. The three-component scattering provide features for distinguishing between different surface cover types. These sensitivities assist in the identification of growing phase. The observed growing phase development in time series, reflected in the consistent temporal trends in scattering, was generally in agreement with crop phenological development stages. Supervised classification was performed on repeat-pass Radarsat-2 images, with an overall classification accuracy of 77.27% achieved using time series Fine beam data. The study demonstrated that Radarsat-2 Fine mode data provide useful information for crop monitoring and classification of rice plants.

scholarly journals Time–frequency time–space LSTM for robust classification of physiological signals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Time Series ◽  
Network Architecture ◽  
Time Series Data ◽  
Short Term Memory ◽  
Physiological Signals ◽  
Series Data ◽  
Time Frequency ◽  
Time Space ◽  
Deep Recurrent Neural Network

AbstractAutomated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.

scholarly journals High-pass filtering artifacts in multivariate classification of neural time series data

2021 ◽  
Vol 352 ◽  
pp. 109080
Author(s):  
Joram van Driel ◽  
Christian N.L. Olivers ◽  
Johannes J. Fahrenfort
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Multivariate Classification ◽  
High Pass

scholarly journals The composition and diversity of natural regeneration of tree species in gaps under different intensities of forest disturbance

2021 ◽  
Author(s):  
Maame Esi Hammond ◽  
Radek Pokorný ◽  
Daniel Okae-Anti ◽  
Augustine Gyedu ◽  
Irene Otwuwa Obeng
Keyword(s):  
Tree Species ◽  
Natural Regeneration ◽  
Forest Disturbance ◽  
Forest Site ◽  
Gap Formation ◽  
Tree Species Composition ◽  
Forest Sites ◽  
Significant Difference ◽  
Shade Tolerant Species ◽  
Disturbed Forest

AbstractThe positive ecological interaction between gap formation and natural regeneration has been examined but little research has been carried out on the effects of gaps on natural regeneration in forests under different intensities of disturbance. This study evaluates the composition, diversity, regeneration density and abundance of natural regeneration of tree species in gaps in undisturbed, intermittently disturbed, and disturbed forest sites. Bia Tano Forest Reserve in Ghana was the study area and three gaps each were selected in the three forest site categories. Ten circular subsampling areas of 1 m2 were delineated at 2 m spacing along north, south, east, and west transects within individual gaps. Data on natural regeneration < 350 cm height were gathered. The results show that the intensity of disturbance was disproportional to gap size. Species diversity differed significantly between undisturbed and disturbed sites and, also between intermittently disturbed and disturbed sites for Simpson’s (1-D), Equitability (J), and Berger–Parker (B–P) indices. However, there was no significant difference among forest sites for Shannon diversity (H) and Margalef richness (MI) indices. Tree species composition on the sites differed. Regeneration density on the disturbed site was significantly higher than on the two other sites. Greater abundance and density of shade-dependent species on all sites identified them as opportunistic replacements of gap-dependent pioneers. Pioneer species giving way to shade tolerant species is a natural process, thus make them worst variant in gap regeneration.

Sign in / Sign up

Export Citation Format

Share Document