scholarly journals Phenology-Based Mapping of an Alien Invasive Species Using Time Series of Multispectral Satellite Data: A Case-Study with Glossy Buckthorn in Québec, Canada

2020 ◽  
Vol 12 (6) ◽  
pp. 922
Author(s):  
Joanie Labonté ◽  
Guillaume Drolet ◽  
Jean-Daniel Sylvain ◽  
Nelson Thiffault ◽  
Francois Hébert ◽  
...  
Keyword(s):  
Time Series ◽  
Plant Species ◽  
Vegetation Index ◽  
Invasive Plant ◽  
Alien Invasive Species ◽  
Landsat 8 ◽  
Eastern Canada ◽  
Evergreen Species ◽  
Glossy Buckthorn ◽  
Unmixing Model

Glossy buckthorn (Frangula alnus Mill.) is an alien species in Canada that is invading many forested areas. Glossy buckthorn has impacts on the biodiversity and productivity of invaded forests. Currently, we do not know much about the species’ ecology and no thorough study of its distribution in temperate forests has been performed yet. As is often the case with invasive plant species, the phenology of glossy buckthorn differs from that of other indigenous plant species found in invaded communities. In the forests of eastern Canada, the main phenological difference is a delay in the shedding of glossy buckthorn leaves, which occurs later in the fall than for other indigenous tree species found in that region. Therefore, our objective was to use that phenological characteristic to map the spatial distribution of glossy buckthorn over a portion of southern Québec, Canada, using remote sensing-based approaches. We achieved this by applying a linear temporal unmixing model to a time series of the normalized difference vegetation index (NDVI) derived from Landsat 8 Operational Land Imager (OLI) images to create a map of the probability of the occurrence of glossy buckthorn for the study area. The map resulting from the temporal unmixing model shows an agreement of 69% with field estimates of glossy buckthorn occurrence measured in 121 plots distributed over the study area. Glossy buckthorn mapping accuracy was limited by evergreen species and by the spectral and spatial resolution of the Landsat 8 OLI.

Responses of insect pests, pathogens, and invasive plant species to climate change in the forests of northeastern North America: What can we predict?This article is one of a selection of papers from NE Forests 2100: A Synthesis of Climate Change Impacts on Forests of the Northeastern US and Eastern Canada.

2009 ◽  
Vol 39 (2) ◽  
pp. 231-248 ◽  
Author(s):  
Jeffrey S. Dukes ◽  
Jennifer Pontius ◽  
David Orwig ◽  
Jeffrey R. Garnas ◽  
Vikki L. Rodgers ◽  
...  
Keyword(s):  
Climate Change ◽  
Plant Species ◽  
Invasive Plant ◽  
Insect Pests ◽  
Adelges Tsugae ◽  
Pest Species ◽  
Invasive Plant Species ◽  
Eastern Canada ◽  
Wide Range ◽  
Nuisance Species

Climate models project that by 2100, the northeastern US and eastern Canada will warm by approximately 3–5 °C, with increased winter precipitation. These changes will affect trees directly and also indirectly through effects on “nuisance” species, such as insect pests, pathogens, and invasive plants. We review how basic ecological principles can be used to predict nuisance species’ responses to climate change and how this is likely to impact northeastern forests. We then examine in detail the potential responses of two pest species (hemlock woolly adelgid ( Adelges tsugae Annand) and forest tent caterpillar ( Malacosoma disstria Hubner)), two pathogens (armillaria root rot ( Armillaria spp.) and beech bark disease ( Cryptococcus fagisuga Lind. + Neonectria spp.)), and two invasive plant species (glossy buckthorn ( Frangula alnus Mill.) and oriental bittersweet ( Celastrus orbiculatus Thunb.)). Several of these species are likely to have stronger or more widespread effects on forest composition and structure under the projected climate. However, uncertainty pervades our predictions because we lack adequate data on the species and because some species depend on complex, incompletely understood, unstable relationships. While targeted research will increase our confidence in making predictions, some uncertainty will always persist. Therefore, we encourage policies that allow for this uncertainty by considering a wide range of possible scenarios.

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 Evaluation of Sensor and Environmental Factors Impacting the Use of Multiple Sensor Data for Time-Series Applications

2018 ◽  
Vol 10 (11) ◽  
pp. 1678 ◽  
Author(s):  
Rajagopalan Rengarajan ◽  
John Schott
Keyword(s):  
Time Series ◽  
Forest Canopy ◽  
Vegetation Index ◽  
Deciduous Forest ◽  
Spectral Response ◽  
Sensor Data ◽  
Landsat 8 ◽  
Real World Data ◽  
Large Variability ◽  
Atmospheric Compensation

Many remote sensing sensors operate in similar spatial and spectral regions, which provides an opportunity to combine the data from different sensors to increase the temporal resolution for short and long-term trend analysis. However, combining the data requires understanding the characteristics of different sensors and presents additional challenges due to their variation in operational strategies, sensor differences and environmental conditions. These differences can introduce large variability in the time-series analysis, limiting the ability to model, predict and separate real change in signal from noise. Although the research community has identified the factors that cause variations, the magnitude or the effect of these factors have not been well explored and this is due to the limitations with the real-world data, where the effects of the factors cannot be separated. Our work mitigates these shortcomings by simulating the surface, atmosphere, and sensors in a virtual environment. We modeled and characterized a deciduous forest canopy and estimated its at-sensor response for the Landsat 8 (L8) and Sentinel 2 (S2) sensors using the MODerate resolution atmospheric TRANsmission (MODTRAN) modeled atmosphere. This paper presents the methods, analysis and the sensitivity of the factors that impacts multi-sensor observations for temporal analysis. Our study finds that atmospheric compensation is necessary as the variation due to the atmosphere can introduce an uncertainty as high as 40% in the Normalized Difference Vegetation Index (NDVI) products used in change detection and time-series applications. The effect due to the differences in the Relative Spectral Response (RSR) of the two sensors, if not compensated, can introduce uncertainty as high as 20% in the NDVI products. The view angle differences between the sensors can introduce uncertainty anywhere from 9% to 40% in NDVI depending on the atmospheric compensation methods. For a difference of 5 days in acquisition, the effect of solar zenith angle can vary between 4% to 10%, depending on whether the atmospheric attenuations are compensated or not for the NDVI products.

scholarly journals Phenological-metric algorithm for mapping soybean in savanna biome in Brazil

2019 ◽  
pp. 1456-1466
Author(s):  
Bernard S. de Oliveira ◽  
Manuel E. Ferreira ◽  
Alexandre C. Coutinho ◽  
Júlio C. D. M. Esquerdo
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Census Data ◽  
Pearson Correlation ◽  
Original Data ◽  
Landsat 8 ◽  
Interpolation Filter ◽  
Modis Evi ◽  
Terra Modis ◽  
Global Accuracy

Agricultural expansion in Brazil is still intense for commodities (such soybeans and corn), mostly cultivated over large portions of the Cerrado biome. Therefore, the development and application of techniques based on remote sensing to map crop areas at a regional level, in a dynamic and more precise way is urgently necessary. In this context, the objective of this study is the improvement of techniques for mapping soybean crops in Brazil, through an analysis of the Centro Goiano mesoregion of Goiás state (a core area of Cerrado), using a time series of Enhanced Vegetation Index (EVI) images provided by TERRA/MODIS orbital sensor, in a test period between 2002 and 2010. Despite their proven quality, MODIS EVI images already contain atmospheric interferences inherent to the acquisition process, such as the presence of clouds. Thus, a set of methods to minimize such artifacts was applied to the data of this study. In general, the methodological procedures comprise of (1) the application of the pixel reliability band aiming to remove pixels contaminated by clouds; (2) the use of contaminated pixel estimates (excluded from the time series); (3) application of an interpolation filter to fill the void pixels in each scene, obtaining continuous and smoothed spectral-temporal profiles for each land use classes; and (4) the classification of agricultural areas using a specific algorithm for crops in the Cerrado region of Goiás. The areas reconstituted in the images matched neighboring pixels, maintaining good coherence with the original data. Likewise, areas mapped with soybeans had a high correlation with official IBGE census data, with a global accuracy value of 78%, and Pearson Correlation coefficient of 0.64. The application of this technique to other imagery sensors (such as RapidEye, Landsat 8 and Sentinel 2) is highly encouraged due a better spatial and temporal resolution (when applied together in a temporal image cube), ensuring more efficient crop monitoring in Brazil.

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 The NDVI-CV Method for Mapping Evergreen Trees in Complex Urban Areas Using Reconstructed Landsat 8 Time-Series Data

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 139 ◽  
Author(s):  
Yingying Yang ◽  
Taixia Wu ◽  
Shudong Wang ◽  
Jing Li ◽  
Farhan Muhanmmad
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Series Data ◽  
Distribution Data ◽  
Landsat 8 ◽  
Oxygen Balance

Evergreen trees play a significant role in urban ecological services, such as air purification, carbon and oxygen balance, and temperature and moisture regulation. Remote sensing represents an essential technology for obtaining spatiotemporal distribution data for evergreen trees in cities. However, highly developed subtropical cities, such as Nanjing, China, have serious land fragmentation problems, which greatly increase the difficulty of extracting evergreen trees information and reduce the extraction precision of remote-sensing methods. This paper introduces a normalized difference vegetation index coefficient of variation (NDVI-CV) method to extract evergreen trees from remote-sensing data by combining the annual minimum normalized difference vegetation index (NDVIann-min) with the CV of a Landsat 8 time-series NDVI. To obtain an intra-annual, high-resolution time-series dataset, Landsat 8 cloud-free and partially cloud-free images over a three-year period were collected and reconstructed for the study area. Considering that the characteristic growth of evergreen trees remained nearly unchanged during the phenology cycle, NDVIann-min is the optimal phenological node to separate this information from that of other vegetation types. Furthermore, the CV of time-series NDVI considers all of the phenologically critical phases; therefore, the NDVI-CV method had higher extraction accuracy. As such, the approach presented herein represents a more practical and promising method based on reasonable NDVIann-min and CV thresholds to obtain spatial distribution data for evergreen trees. The experimental verification results indicated a comparable performance since the extraction accuracy of the model was over 85%, which met the classification accuracy requirements. In a cross-validation comparison with other evergreen trees’ extraction methods, the NDVI-CV method showed higher sensitivity and stability.

Phyto-Distribution of Alien Invasive Species in the Campus of Pune University by Using GPS Mapping and their Interaction

2017 ◽  
Vol 6 (4) ◽  
pp. 150-157
Author(s):  
N. A. Ghayal ◽  
C. P. Hase
Keyword(s):  
Plant Species ◽  
Invasive Plant ◽  
Negative Influence ◽  
Ecosystem Dynamics ◽  
Alien Invasive Species ◽  
Invasive Weeds ◽  
University Campus ◽  
Native Plant ◽  
Weed Species ◽  
The University

The invasive plant species hamper crops, human activities and become part of dynamic ecosystems, which grow in varied habitats and harsh ecological conditions and often invade the new ecosystems. The campus of Pune University is highly rich in phytodiversity of native and invasive weeds, which interact with each other. The invasive weeds like Cassia uniflora Mill. non Spreng, Alternanthera tenella Colla., Synedrella nodiflora (L) Gaertn, S. vialis Gray and native weed species like Achyranthes are showing dominance in the campus. The GPS mapping indicated that C. uniflora, S. nodiflora, . tenella, Blainvillea acmella, Euphorbia geniculata, Triumfetta rhomboidea and C. obtusifolia were dominant and occasionally forming pure stands in the campus reducing the phytodiversity of natives by substitution. The results on weed-weed interactions indicated that major associations were between Cassia and Achyranthes, Cassia and Bidens . Synedrella population was forming monothickets. The studies on weed-weed associations and interactions at all the sites indicated that native plants were substituted by the encroachment of invasive weeds due to negative interactions. The negative influence of Cassia and Synedrella was prominent through out the campus. The strong positive and negative associations of native and alien weeds in the university campus predicted changing Phytodiversity and ecosystem dynamics. The aggressive nature and invasiveness of C.uniflora and S.nodiflora was confirmed by their respective abundance such as 25.83 and 24.80 as compared to native weeds like Achyranthes (12.93). The investigation clearly proved the declining phytodiversityof native plant species in the university campus, which has perturbed the ecological balance through the release of allelochemicals / ecochemicals in the habitat. 

scholarly journals Fractional Cover Mapping of Invasive Plant Species by Combining Very High-Resolution Stereo and Multi-Sensor Multispectral Imageries

Forests ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 540 ◽  
Author(s):  
Siddhartha Khare ◽  
Hooman Latifi ◽  
Sergio Rossi ◽  
Sanjay Kumar Ghosh
Keyword(s):  
High Resolution ◽  
Plant Species ◽  
Spatial Resolution ◽  
Spectral Reflectance ◽  
Invasive Plant ◽  
Training Data ◽  
Landsat 8 ◽  
Invasive Plant Species ◽  
Fractional Cover ◽  
Very High

Invasive plant species are major threats to biodiversity. They can be identified and monitored by means of high spatial resolution remote sensing imagery. This study aimed to test the potential of multiple very high-resolution (VHR) optical multispectral and stereo imageries (VHRSI) at spatial resolutions of 1.5 and 5 m to quantify the presence of the invasive lantana (Lantana camara L.) and predict its distribution at large spatial scale using medium-resolution fractional cover analysis. We created initial training data for fractional cover analysis by classifying smaller extent VHR data (SPOT-6 and RapidEye) along with three dimensional (3D) VHRSI derived digital surface model (DSM) datasets. We modelled the statistical relationship between fractional cover and spectral reflectance for a VHR subset of the study area located in the Himalayan region of India, and finally predicted the fractional cover of lantana based on the spectral reflectance of Landsat-8 imagery of a larger spatial extent. We classified SPOT-6 and RapidEye data and used the outputs as training data to create continuous field layers of Landsat-8 imagery. The area outside the overlapping region was predicted by fractional cover analysis due to the larger extent of Landsat-8 imagery compared with VHR datasets. Results showed clear discrimination of understory lantana from upperstory vegetation with 87.38% (for SPOT-6), and 85.27% (for RapidEye) overall accuracy due to the presence of additional VHRSI derived DSM information. Independent validation for lantana fractional cover estimated root-mean-square errors (RMSE) of 11.8% (for RapidEye) and 7.22% (for SPOT-6), and R2 values of 0.85 and 0.92 for RapidEye (5 m) and SPOT-6 (1.5 m), respectively. Results suggested an increase in predictive accuracy of lantana within forest areas along with increase in the spatial resolution for the same Landsat-8 imagery. The variance explained at 1.5 m spatial resolution to predict lantana was 64.37%, whereas it decreased by up to 37.96% in the case of 5 m spatial resolution data. This study revealed the high potential of combining small extent VHR and VHRSI- derived 3D optical data with larger extent, freely available satellite data for identification and mapping of invasive species in mountainous forests and remote regions.

scholarly journals Analysis of the Spatial and Temporal Pattern of Changes in Abandoned Farmland Based on Long Time Series of Remote Sensing Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2549
Author(s):  
Zhonghui Wei ◽  
Xiaohe Gu ◽  
Qian Sun ◽  
Xueqian Hu ◽  
Yunbing Gao
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Index ◽  
Average Rate ◽  
Landsat 8 ◽  
Cultivated Land ◽  
Farmland Abandonment ◽  
Abandoned Farmland ◽  
Long Time Series ◽  
Long Time

With the rapid increase in the costs of rural labour and the adjustment of planting structures, the phenomenon of farmland abandonment has appeared in China. It is of great significance to promptly and accurately grasp the information on dynamic temporal and spatial changes in abandoned farmland to ensure national food security and the sustainable use of cultivated land. Luquan District in Hebei, China was selected as the research area based on multispectral images from Sentinel-2A, Landsat-7, and Landsat-8 combined with methods of random forest (RF) classification and vegetation index change detection. Rules for the identification of abandoned farmland were also developed, and remote sensing monitoring of the abandonment status of the cultivated land was also carried out in the study area. We also obtained the spatial distribution of abandoned and reclaimed farmland and analysed the frequency of farmland abandonment. The results show that the overall accuracy of the land-use time-series map ranged from 90.20% to 96.92% for the study period of 2010–2020. The average rate of farmland abandonment in the study area was 10.62%, with the lowest rate (5.83%) in 2020 and the highest (14.09%) in 2012. From 2011 to 2020, the maximum farmland abandonment area was 3906.02 hm2, and the minimum area was 1618.74 hm2. The farmland abandonment area showed a trend of first increasing and then decreasing. From 2012 to 2020, the maximum area of reclaimed farmland was 291.49 hm2, and the highest rate of reclamation was 14.26%. The overall reclamation rate was low. The abandonment frequency of most of the abandoned farmland was 1–3 years, covering an area of 8193.73 hm2, which comprised 79% of the total area of abandoned farmland. The frequency of abandonment was inversely proportional to the area of abandoned farmland. Farmland abandonment mainly occurred in hilly areas. We expect that our results can provide case studies for long time series in farmland abandonment research and can provide a reference for studying the driving factors, risk assessment, and policymaking with respect to abandoned farmland.

scholarly journals DEEP LEARNING-BASED RECONSTRUCTION OF SPATIOTEMPORALLY FUSED SATELLITE IMAGES FOR SMART AGRICULTURE APPLICATIONS IN A HETEROGENEOUS AGRICULTURAL REGION

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 249-254
Author(s):  
A. Htitiou ◽  
A. Boudhar ◽  
Y. Lebrini ◽  
T. Benabdelouahab
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Land Surface ◽  
Satellite Images ◽  
Vegetation Index ◽  
Data Availability ◽  
Series Data ◽  
Landsat 8 ◽  
Phenological Stages ◽  
Area Index

Abstract. Remote sensing offers spatially explicit and temporally continuous observational data of various land surface parameters such as vegetation index, land surface temperature, soil moisture, leaf area index, and evapotranspiration, which can be widely leveraged for various applications at different scales and contexts. One of the main applications is agricultural monitoring, where a smart system based on precision agriculture requires a set of satellite images with a high resolution, both in time and space to capture the phenological stages and fine spatial details, especially in landscapes with various spatial heterogeneity and temporal variation. These requirements sometimes cannot be provided by a single sensor due to the trade-off required between spatial and temporal resolutions and/or the influence of cloud cover. The data availability of new generation multispectral sensors of Landsat-8 (L8) and Sentinel-2 (S2) satellites offers unprecedented options for such applications. Given this, the current study aims to display how the synergistic use of these optical sensors can efficiently support such an application. Herein, this study proposes a deep learning spatiotemporal data fusion method to fill the need for predicting a dense time series of vegetation index with fine spatial resolution. The results show that the developed method creates more accurate fused NDVI time-series data that were able to derive phenological stages and characteristics in single-crop fields, while keeps more spatial details in such a heterogeneous landscape.

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