scholarly journals Characterizing Land Surface Phenology and Exotic Annual Grasses in Dryland Ecosystems Using Landsat and Sentinel-2 Data in Harmony

2020 ◽  
Vol 12 (4) ◽  
pp. 725 ◽  
Author(s):  
Neal J. Pastick ◽  
Devendra Dahal ◽  
Bruce K. Wylie ◽  
Sujan Parajuli ◽  
Stephen P. Boyte ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Land Surface ◽  
Western United States ◽  
Grass Cover ◽  
Annual Grass ◽  
Annual Grasses ◽  
Dryland Ecosystems ◽  
Exotic Annual Grass ◽  
Sentinel 2

Invasive annual grasses, such as cheatgrass (Bromus tectorum L.), have proliferated in dryland ecosystems of the western United States, promoting increased fire activity and reduced biodiversity that can be detrimental to socio-environmental systems. Monitoring exotic annual grass cover and dynamics over large areas requires the use of remote sensing that can support early detection and rapid response initiatives. However, few studies have leveraged remote sensing technologies and computing frameworks capable of providing rangeland managers with maps of exotic annual grass cover at relatively high spatiotemporal resolutions and near real-time latencies. Here, we developed a system for automated mapping of invasive annual grass (%) cover using in situ observations, harmonized Landsat and Sentinel-2 (HLS) data, maps of biophysical variables, and machine learning techniques. A robust and automated cloud, cloud shadow, water, and snow/ice masking procedure (mean overall accuracy >81%) was implemented using time-series outlier detection and data mining techniques prior to spatiotemporal interpolation of HLS data via regression tree models (r = 0.94; mean absolute error (MAE) = 0.02). Weekly, cloud-free normalized difference vegetation index (NDVI) image composites (2016–2018) were used to construct a suite of spectral and phenological metrics (e.g., start and end of season dates), consistent with information derived from Moderate Resolution Image Spectroradiometer (MODIS) data. These metrics were incorporated into a data mining framework that accurately (r = 0.83; MAE = 11) modeled and mapped exotic annual grass (%) cover throughout dryland ecosystems in the western United States at a native, 30-m spatial resolution. Our results show that inclusion of weekly HLS time-series data and derived indicators improves our ability to map exotic annual grass cover, as compared to distribution models that use MODIS products or monthly, seasonal, or annual HLS composites as primary inputs. This research fills a critical gap in our ability to effectively assess, manage, and monitor drylands by providing a framework that allows for an accurate and timely depiction of land surface phenology and exotic annual grass cover at spatial and temporal resolutions that are meaningful to local resource managers.

Restoration of Exotic Annual Grass-Invaded Rangelands: Importance of Seed Mix Composition

2014 ◽  
Vol 7 (2) ◽  
pp. 247-256 ◽  
Author(s):  
Kirk W. Davies ◽  
Dustin D. Johnson ◽  
Aleta M. Nafus
Keyword(s):  
Species Richness ◽  
Life Forms ◽  
Perennial Grasses ◽  
Annual Grass ◽  
Herbaceous Vegetation ◽  
Plant Life ◽  
Annual Grasses ◽  
Exotic Annual Grass ◽  
Resistance To Invasion ◽  
Plant Life Form

AbstractRestoration of exotic annual grass-invaded rangelands is needed to improve ecosystem function and services. Increasing plant species richness is generally believed to increase resistance to invasion and increase desired vegetation. However, the effects of species richness and individual plant life forms in seed mixes used to restore rangelands invaded by exotic annual grasses have not been investigated. We evaluated the effects of seeding different life forms and increasing species richness in seed mixes seeded after exotic annual grass control to restore desirable vegetation (perennial herbaceous vegetation) and limit exotic annual grasses at two sites in southeastern Oregon. We also investigated the effects of seeding two commonly used perennial grasses individually and together on plant community characteristics. Large perennial grasses, the dominant herbaceous plant life form, were the most important group to seed for increasing perennial herbaceous vegetation cover and density. We did not find evidence that greater seed mix species richness increased perennial herbaceous vegetation or decreased exotic annual grass dominance more than seeding only the dominant species. None of the seed mixes had a significant effect on exotic annual grass cover or density, but the lack of a measured effect may have been caused by low annual grass propagule pressure in the first couple of years after annual grass control and an unusually wet-cool spring in the third year post-seeding. Although our results suggest that seeding only the dominant plant life form will likely maximize plant community productivity and resistance to invasion in exotic annual grass-invaded northern Great Basin arid rangelands, seeding a species rich seed mix may have benefits to higher tropic levels and community stability. Clearly the dominant species are the most prudent to include in seed mixes to restore exotic annual grass-invaded plant communities, especially with finite resources and an increasingly large area in need of restoration.

scholarly journals Effects of high spatial and temporal resolution Earth observations on simulated hydrometeorological variables in a cropland (southwestern France)

2017 ◽  
Vol 21 (11) ◽  
pp. 5693-5708 ◽  
Author(s):  
Jordi Etchanchu ◽  
Vincent Rivalland ◽  
Simon Gascoin ◽  
Jérôme Cros ◽  
Tiphaine Tallec ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Surface ◽  
Agricultural Landscapes ◽  
Surface Model ◽  
Spatial And Temporal Variability ◽  
Area Index ◽  
The Impact ◽  
Sentinel 2

Abstract. Agricultural landscapes are often constituted by a patchwork of crop fields whose seasonal evolution is dependent on specific crop rotation patterns and phenologies. This temporal and spatial heterogeneity affects surface hydrometeorological processes and must be taken into account in simulations of land surface and distributed hydrological models. The Sentinel-2 mission allows for the monitoring of land cover and vegetation dynamics at unprecedented spatial resolutions and revisit frequencies (20 m and 5 days, respectively) that are fully compatible with such heterogeneous agricultural landscapes. Here, we evaluate the impact of Sentinel-2-like remote sensing data on the simulation of surface water and energy fluxes via the Interactions between the Surface Biosphere Atmosphere (ISBA) land surface model included in the EXternalized SURface (SURFEX) modeling platform. The study focuses on the effect of the leaf area index (LAI) spatial and temporal variability on these fluxes. We compare the use of the LAI climatology from ECOCLIMAP-II, used by default in SURFEX-ISBA, and time series of LAI derived from the high-resolution Formosat-2 satellite data (8 m). The study area is an agricultural zone in southwestern France covering 576 km2 (24 km  ×  24 km). An innovative plot-scale approach is used, in which each computational unit has a homogeneous vegetation type. Evaluation of the simulations quality is done by comparing model outputs with in situ eddy covariance measurements of latent heat flux (LE). Our results show that the use of LAI derived from high-resolution remote sensing significantly improves simulated evapotranspiration with respect to ECOCLIMAP-II, especially when the surface is covered with summer crops. The comparison with in situ measurements shows an improvement of roughly 0.3 in the correlation coefficient and a decrease of around 30 % of the root mean square error (RMSE) in the simulated evapotranspiration. This finding is attributable to a better description of LAI evolution processes with Formosat-2 data, which further modify soil water content and drainage of soil reservoirs. Effects on annual drainage patterns remain small but significant, i.e., an increase roughly equivalent to 4 % of annual precipitation levels with simulations using Formosat-2 data in comparison to the reference simulation values. This study illustrates the potential for the Sentinel-2 mission to better represent effects of crop management on water budgeting for large, anthropized river basins.

scholarly journals Effects of multi-temporal high-resolution remote sensing products on simulated hydrometeorological variables in a cultivated area (southwestern France)

2017 ◽  
Author(s):  
Jordi Etchanchu ◽  
Vincent Rivalland ◽  
Simon Gascoin ◽  
Jérôme Cros ◽  
Aurore Brut ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Land Surface ◽  
Agricultural Landscapes ◽  
Annual Precipitation ◽  
Surface Model ◽  
Area Index ◽  
The Impact ◽  
Sentinel 2

Abstract. Agricultural landscapes often include a patchwork of crop fields whose seasonal evolution is dependent on specific crop rotation patterns and phenologies. This temporal and spatial heterogeneity affects surface hydrometeorological processes as simulated by land surface and distributed hydrological models. Sentinel-2 mission satellite remote sensing products allow for the monitoring of land cover and vegetation dynamics at unprecedented spatial resolutions and revisit frequencies (20 m and 5 days, respectively) that are fully compatible with such heterogeneous agricultural landscapes. Here, we evaluate the impact of Sentinel-2-like remote sensing data on the simulation of surface water and energy flux via the ISBA-SURFEX land surface model. The study area is a 24 km by 24 km agricultural zone in southwestern France. An initial reference simulation was conducted from 2006–2010 using the ECOCLIMAP-II database. This global numerical land ecosystem database was created at a 1 km resolution and includes an ecosystem classification with a consistent set of land surface parameters required for the model, such as the Leaf Area Index (LAI) and albedo measures. The LAI of ECOCLIMAP is climatologic and derived from a 2000–2005 analysis of MODIS satellite products. This low resolution induces that several vegetation covers can be mixed in a model cell. The climatic construction of LAI dynamics also suggests that there is no interannual variability in the vegetation cycle. A second simulation was performed by forcing the same model with annual land cover maps and monthly LAI values derived from a series of 105 8 m-resolution Formosat-2 images for the same period. Both simulations were conducted at the parcel scale, i.e., a computation unit covers an area of connected pixels of the same vegetation type (a crop field, forest patch, etc.). To evaluate our simulations, we used in situ measurements of evapotranspiration and latent and sensible heat flux from two eddy covariance stations in the study area. Our results show that the use of Formosat-2 high-resolution products significantly improves simulated evapotranspiration results with respect to ECOCLIMAP-II, especially when a surface is covered with summer crops (the correlation coefficient with monthly measurements is increased by roughly 0.3 and the root mean square error is decreased by roughly 31 %). This finding is attributable to a better description of LAI evolution processes reflected by Formosat-2 data, which further modify soil water content and drainage levels of deep soil reservoirs. Effects on annual drainage patterns remain small but significant, i.e., an increase roughly equivalent to 4 % of annual precipitation levels from Formosat-2 data in comparison to reference values. In smaller proportions, runoff is also increased by roughly 1 % of annual precipitation when using Formosat-2 data. This study illustrates the potential for the Sentinel-2 mission to better represent effects of crop management on water budgeting for large, anthropized river basins.

scholarly journals Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data

2021 ◽  
Vol 13 (4) ◽  
pp. 818
Author(s):  
Sofia Junttila ◽  
Julia Kelly ◽  
Natascha Kljun ◽  
Mika Aurela ◽  
Leif Klemedtsson ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Sensor Data ◽  
Spatial And Temporal Variation ◽  
Model Parameters ◽  
Co2 Fluxes ◽  
Sentinel 2

Peatlands play an important role in the global carbon cycle as they contain a large soil carbon stock. However, current climate change could potentially shift peatlands from being carbon sinks to carbon sources. Remote sensing methods provide an opportunity to monitor carbon dioxide (CO2) exchange in peatland ecosystems at large scales under these changing conditions. In this study, we developed empirical models of the CO2 balance (net ecosystem exchange, NEE), gross primary production (GPP), and ecosystem respiration (ER) that could be used for upscaling CO2 fluxes with remotely sensed data. Two to three years of eddy covariance (EC) data from five peatlands in Sweden and Finland were compared to modelled NEE, GPP and ER based on vegetation indices from 10 m resolution Sentinel-2 MSI and land surface temperature from 1 km resolution MODIS data. To ensure a precise match between the EC data and the Sentinel-2 observations, a footprint model was applied to derive footprint-weighted daily means of the vegetation indices. Average model parameters for all sites were acquired with a leave-one-out-cross-validation procedure. Both the GPP and the ER models gave high agreement with the EC-derived fluxes (R2 = 0.70 and 0.56, NRMSE = 14% and 15%, respectively). The performance of the NEE model was weaker (average R2 = 0.36 and NRMSE = 13%). Our findings demonstrate that using optical and thermal satellite sensor data is a feasible method for upscaling the GPP and ER of northern boreal peatlands, although further studies are needed to investigate the sources of the unexplained spatial and temporal variation of the CO2 fluxes.

scholarly journals Combination of Sentinel-2 and PALSAR-2 for Local Climate Zone Classification: A Case Study of Nanchang, China

2021 ◽  
Vol 13 (10) ◽  
pp. 1902
Author(s):  
Chaomin Chen ◽  
Hasi Bagan ◽  
Xuan Xie ◽  
Yune La ◽  
Yoshiki Yamagata
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Grid Cell ◽  
Classification Model ◽  
Textural Features ◽  
Climate Zone ◽  
Local Climate ◽  
Dual Polarized ◽  
Local Climate Zone ◽  
Sentinel 2

Local climate zone (LCZ) maps have been used widely to study urban structures and urban heat islands. Because remote sensing data enable automated LCZ mapping on a large scale, there is a need to evaluate how well remote sensing resources can produce fine LCZ maps to assess urban thermal environments. In this study, we combined Sentinel-2 multispectral imagery and dual-polarized (HH + HV) PALSAR-2 data to generate LCZ maps of Nanchang, China using a random forest classifier and a grid-cell-based method. We then used the classifier to evaluate the importance scores of different input features (Sentinel-2 bands, PALSAR-2 channels, and textural features) for the classification model and their contribution to each LCZ class. Finally, we investigated the relationship between LCZs and land surface temperatures (LSTs) derived from summer nighttime ASTER thermal imagery by spatial statistical analysis. The highest classification accuracy was 89.96% when all features were used, which highlighted the potential of Sentinel-2 and dual-polarized PALSAR-2 data. The most important input feature was the short-wave infrared-2 band of Sentinel-2. The spectral reflectance was more important than polarimetric and textural features in LCZ classification. PALSAR-2 data were beneficial for several land cover LCZ types when Sentinel-2 and PALSAR-2 were combined. Summer nighttime LSTs in most LCZs differed significantly from each other. Results also demonstrated that grid-cell processing provided more homogeneous LCZ maps than the usual resampling methods. This study provided a promising reference to further improve LCZ classification and quantitative analysis of local climate.

scholarly journals Rapid Monitoring of the Abundance and Spread of Exotic Annual Grasses in the Western United States Using Remote Sensing and Machine Learning

AGU Advances ◽  
2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Neal J. Pastick ◽  
Bruce K. Wylie ◽  
Matthew B. Rigge ◽  
Devendra Dahal ◽  
Stephen P. Boyte ◽  
...  
Keyword(s):  
United States ◽  
Machine Learning ◽  
Remote Sensing ◽  
Western United States ◽  
Rapid Monitoring ◽  
Annual Grasses ◽  
Exotic Annual Grasses

scholarly journals Annual and 16-day rangeland production estimates for the western United States

2020 ◽  
Author(s):  
Matthew O. Jones ◽  
Nathaniel P. Robinson ◽  
David E. Naugle ◽  
Jeremy D. Maestas ◽  
Matthew C. Reeves ◽  
...  
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Decision Making ◽  
Remote Sensing Data ◽  
Western United States ◽  
Woody Encroachment ◽  
Annual Grass ◽  
The Face ◽  
Technological Barriers ◽  
Annual Time

AbstractRangeland production is a foundational ecosystem service and resource upon which livestock, wildlife, and people depend. Capitalizing on recent advancements in the use of remote sensing data across rangelands we provide estimates of herbaceous rangeland production from 1986-2019 at 16-day and annual time steps and 30m resolution across the western United States. Herbaceous aboveground biomass at this scale and resolution provide critical information applicable for management and decision-making, particularly in the face of annual grass invasion and woody encroachment of rangeland systems. These readily available data remove analytical and technological barriers allowing immediate utilization for monitoring and management.

scholarly journals A RADIOMETRIC NORMALIZATION METHOD OF CONTROLLING NO-CHANGED SET (CNCS) FOR DIVERSE LANDCOVER USING MULTI-SENSOR DATA

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 863-870
Author(s):  
L. T. Huang ◽  
W. L. Jiao ◽  
T. F. Long ◽  
C. L. Kang
Keyword(s):  
Remote Sensing ◽  
Linear Relationship ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Normalization Method ◽  
Sensor Data ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sensing Data ◽  
Sentinel 2

Abstract. The accurate acquisition of land surface reflectance (SR) data determines the accuracy of ground objects recognition, classification and land surface parameter inversion using remote sensing data, which is the basis of remote sensing data application. In this study, a Control No-Changed Set (CNCS) radiometric normalization method is proposed to realize spectral information transformation of multi-sensor data, which is based on the Iteratively Reweighted Multivariate Alteration Detection (IR-MAD), and includes automatic selection and step-by-step optimization of no-change pixels. The No-Changed set (NC) is obtained by selecting the original no-change pixels between the target image and the reference image according to the linear relationship. In the obtained original no-change regions, IR-MAD rules with iterative control are used to fix the final no-change pixels, after regression modeling and calculation, the normalized images are obtained. The method is tested on multi-images from multi-sensors in three groups of experiments (GF-1 WFV and Landsat-8 OLI, GF-1 PMS and Sentinel-2 MSI, and Landsat-8 OLI and Sentinel-2 MSI) with different landcover areas. The results of radiometric normalization are evaluated qualitatively and quantitatively. The data of the three groups of experiments have a high correlation (correlation coefficient r values > 0.85), indicating that they can be used together as complementary data. The Root Mean Squared Error (RMSE) values calculate from the NC between the reference and normalized target images are much smaller than those between the reference and original target images. The radiometric colour composition effects, and the typical ground objects spectral reflective curves of the reference and normalized target images are very similar after radiometric normalization. These results indicate that the CNCS method considers the linear relationship of the no-change pixels and is effective, stable, and can be used to improve the consistency of SR of multi-images from multi-sensors.

Plant silicon as a factor in medusahead (Taeniatherum caput-medusae) invasion

2020 ◽  
Vol 13 (3) ◽  
pp. 143-154
Author(s):  
Casey N. Spackman ◽  
Thomas A. Monaco ◽  
Clinton A. Stonecipher ◽  
Juan J. Villalba
Keyword(s):  
Control Strategies ◽  
Essential Element ◽  
Current Control ◽  
Western United States ◽  
Annual Grass ◽  
Feedback Cycle ◽  
Annual Grasses ◽  
Plant Characteristics ◽  
High Concentrations ◽  
And Function

AbstractMedusahead [Taeniatherum caput-medusae (L.) Nevski] is one of the most detrimental invasive annual grasses impacting the sustainability and function of rangeland in the western United States. This annual grass possesses high concentrations of tissue silicon (Si) that may facilitate invasion through key plant characteristics such as increased plant fitness, structure, and antinutritive qualities. These characteristics may affect known invasive processes such as increased plant productivity, slow litter decomposition, and decreased herbivory, facilitating a positive feedback cycle of invasion. However, Si is not considered an essential element and is often overlooked as a factor of T. caput-medusae invasion. Thus, this article provides a synthesis of plant Si, T. caput-medusae, and the self-reinforcing feedback cycle of invasion. We also discuss how current control strategies address plant characteristics determined by Si and suggest research avenues that may aid in novel or improved control strategies that target the T. caput-medusae–silica relationship.

scholarly journals Assessing damages of agricultural land due to flooding in a lagoon region based on remote sensing and GIS: case study of the Quang Dien district, Thua Thien Hue province, central Vietnam

2020 ◽  
Vol 12 (2) ◽  
pp. 100-107
Author(s):  
Ngoc Bich NGUYEN ◽  
Ngu Huu NGUYEN ◽  
Duc Thanh TRAN ◽  
Phuong Thi TRAN ◽  
Tung Gia PHAM ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Viet Nam ◽  
Enhanced Vegetation Index ◽  
Water Index ◽  
Land Surface Water ◽  
Central Vietnam ◽  
Sentinel 2

This study aims to create a flood extent map with Sentinel imagery and to evaluate impacts on agricultural land in the lagoon region of central Vietnam. In this study, remote sensing images, obtained from 2017 to 2019, were used to simultaneously map the land cover status of a flood in the Quang Dien district. This study highlights flooded areas from Sentinel-2 images by calculating some indicators such as the Land Surface Water Index (LSWI) and the Enhanced Vegetation Index (EVI). Comparisons between the floodplain samples (GPS point-based) and flood mapping results, with the ground-truth data, indicate that the overall accuracy and Kappa coefficients were 97.9% and 0.62 respectively for 2017; the values for 2019 were 95.7% and 0.77 for the same coefficients. Land use maps overlying the flood-affected maps show that approximately 11% of the agriculture land area was affected by floods in 2019 comparison to a 10% in 2017. Wet rice was the most affected crop with the flooded area accounting for more than 70% of the district under each flood event. The most affected communes are: Quang An, Quang Phuoc and Quang Thanh. This study provides valuable information for flood disaster planning, mitigation and recovery activities in Vietnam. Mục tiêu của nghiên cứu là lập bản đồ phân bố ngập lụt với hình ảnh vệ tinh Sentinel và đánh giá ảnh hưởng ngập lụt đến sử dụng đất nông nghiệp ở vùng đầm phá miền Trung, Việt Nam. Trong nghiên cứu này, ảnh viễn thám thu nhận giai đoạn 2017- 2019 được sử dụng để xây dựng bản đồ hiện trạng sử dụng đất tại thời điểm bị ngập nước trên địa bàn huyện Quảng Điền. Nghiên cứu đã xác định được vùng ngập lụt ở huyện Quảng Điền bằng phương pháp phân loại chỉ số mặt nước (Land Surface Water Index – LSWI) và chỉ số khác biệt thực vật (Enhanced Vegetation Index-EVI) từ ảnh Sentinel-2. Xác định vùng nước lũ bị che khuất bởi mây bằng mô hình số hóa độ cao (DEM). Kết quả phân loại vùng ngập lụt được so sánh với giá trị tham chiếu mặt đất cho thấy độ chính xác tổng thể và hệ số Kappa đạt được trong năm 2017 là 97,9% và 0,62; trong khi năm 2019 đạt 95,7% và 0.77. Bản đồ sử dụng đất chồng lên bản đồ lũ lụt cho thấy khoảng 11% diện tích đất nông nghiệp bị ảnh hưởng bởi lũ lụt năm 2019 so với 10% năm 2017. Cây lúa nước là cây trồng bị ảnh hưởng nặng nề nhất, với diện tích bị ngập lụt chiếm hơn 70% diện tích lúa của huyện. Các xã bị ngập lớn là xã Quảng An, Quảng Phước và Quảng Thành. Nghiên cứu này cung cấp thông tin có giá trị cho các hoạt động lập kế hoạch, giảm nhẹ và phục hồi thiên tai lũ lụt ở Việt Nam.

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