Detection of Shoot Beetle Stress on Yunnan Pine Forest Using a Coupled LIBERTY2-INFORM Simulation

Yunnan pine shoot beetles (PSB), Tomicus yunnanensis and Tomicus minor have spread through southwestern China in the last five years, leading to millions of hectares of forest being damaged. Thus, there is an urgent need to develop an effective approach for accurate early warning and damage assessment of PSB outbreaks. Remote sensing is one of the most efficient methods for this purpose. Despite many studies existing on the mountain pine beetle (MPB), very little work has been undertaken on assessing PSB stress using remote sensing. The objective of this paper was to develop a spectral linear mixing model aided by radiative transfer (RT) and a new Yellow Index (YI) to simulate the reflectance of heterogeneous canopies containing damaged needles and quantitatively inverse their PSB stress. The YI, the fraction of dead needles, is a physically-explicit stress indicator that represents the plot shoots damage ratio (plot SDR). The major steps of this methods include: (1) LIBERTY2 was developed to simulate the reflectance of damaged needles using YI to linearly mix the green needle spectra with the dead needle spectra; (2) LIBERTY2 was coupled with the INFORM model to scale the needle spectra to the canopy scale; and (3) a look-up table (LUT) was created against Sentinel 2 (S2) imagery and inversed leaf chlorophyll content (LCC), green leaf area index (LAI) and plot SDR. The results show that (1) LIBERTY2 effectively simulated the reflectance spectral values on infested needles (mean relative error (MRE) = 1.4–18%), and the YI can indicate the degrees of needles damage; (2) the coupled LIBERTY2-INFORM model is suitable to estimate LAI (R2 = 0.73, RMSE = 0.17 m m−2, NRMSE = 11.41% and the index of agreement (IOA) = 0.92) and LCC (R2 = 0.49, RMSE = 56.24 mg m−2, NRMSE = 25.22% and IOA = 0.72), and is better than the original LIBERTY model (LAI: R2 = 0.38, RMSE = 0.43 m m−2, NRMSE = 28.85% and IOA = 0.68; LCC: R2 = 0.34, RMSE = 76.44 mg m−2, NRMSE = 34.23% and IOA = 0.57); and (3) the inversed YI is positively correlated with the measured plot SDR (R2 = 0.40, RMSE = 0.15). We conclude that the LIBERTY2 model improved the reflectance simulation accuracy of both the needles and canopies, making it suitable for assessing PSB stress. The YI has the potential to assess PSB damage.

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The Mountain Pine Beetle Epidemic in the Black Hills, South Dakota: The Consequences of Long Term Fire Policy, Climate Change and the Use of Remote Sensing to Enhance Mitigation

Journal of Geography and Geology ◽

10.5539/jgg.v10n1p69 ◽

2018 ◽

Vol 10 (1) ◽

pp. 69 ◽

Cited By ~ 2

Author(s):

Kyle Mullen ◽

Fei Yuan ◽

Martin Mitchell

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Ponderosa Pine ◽

Mountain Pine Beetle ◽

Black Hills ◽

Fire Policy ◽

Mountain Pine ◽

Pine Beetle ◽

Ponderosa Pine Forest

The recent and intense outbreak (first decade of 2000s) of the mountain pine beetle in the Black Hills of South Dakota and Wyoming, which impacted over 33% of the 1.2 million acre (486,000 ha) Black Hills National Forest, illustrates what can occur when forest management practices intersect with natural climatic oscillations and climate change to create the “perfect storm” in a region where the physical environment sets the stage for a plethora of economic activities ranging from extractive industries to tourism. This study evaluates the potential of WorldView-2 satellite imagery for green-attacked tree detection in the ponderosa pine forest of the Black Hills, USA. It also discusses the consequences of long term fire policy and climate change, and the use of remote sensing technology to enhance mitigation. It was found that the near-infrared one (band 7) of WorldView-2 imagery had the highest influence on the green-attack classification. The Random Forest classification produced the best results when transferred to the independent dataset, whereas the Logistic Regression models consistently yielded the highest accuracies when cross-validated with the training data. Lessons learned include: (1) utilizing recent advances in remote sensing technologies, most notably the use of WorldView-2 data, to assist in more effectively implementing mitigation measures during an epidemic, and (2) implementing pre-emptive thinning strategies; both of which can be applied elsewhere in the American West to more effectively blunt or preclude the consequences of a mountain pine beetle outbreak on an existing ponderosa pine forest.

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Hyperspectral Remote Sensing of Mountain Pine Beetle with an Emphasis on Previsual Assessment

Canadian Journal of Remote Sensing ◽

10.1080/07038992.2015.1065707 ◽

2015 ◽

Vol 41 (3) ◽

pp. 191-202 ◽

Cited By ~ 11

Author(s):

K. Olaf Niemann ◽

G. Quinn ◽

R. Stephen ◽

F. Visintini ◽

D. Parton

Keyword(s):

Remote Sensing ◽

Mountain Pine Beetle ◽

Hyperspectral Remote Sensing ◽

Mountain Pine ◽

Pine Beetle

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Evaluation and Hydrological Application of TRMM and GPM Precipitation Products in a Tropical Monsoon Basin of Thailand

Water ◽

10.3390/w11040818 ◽

2019 ◽

Vol 11 (4) ◽

pp. 818 ◽

Cited By ~ 2

Author(s):

Rui Li ◽

Jiancheng Shi ◽

Dabin Ji ◽

Tianjie Zhao ◽

Vichian Plermkamon ◽

...

Keyword(s):

Water Management ◽

Remote Sensing ◽

Tropical Rainfall Measuring Mission ◽

Microwave Remote Sensing ◽

Infiltration Capacity ◽

Runoff Simulation ◽

Hydrological Simulation ◽

Simulation Accuracy ◽

Area Index ◽

Dry Seasons

Watershed runoff is essential for water management. However, runoff materials are lacking in poorly gauged catchments and not always accessible. Microwave remote sensing offers emerging capabilities for hydrological simulation. In this study based on multi-satellite retrievals for Global Precipitation Measurement (IMERG), Tropical Rainfall Measuring Mission (TRMM) products, and World Meteorological Organization (WMO) interpolated precipitation data, we simulated runoff using a variable infiltration capacity (VIC) model and studied the differences among the results. Then, we analyzed the impacts of the runoff on a moderate-resolution imaging spectroradiometer vegetation leaf area index (LAI) during dry seasons. The results showed that (1) IMERG V5 and TRMM products are capable of monitoring the night-day rainfall diurnal cycle and have higher correlations than the WMO daily observation interpolations. However, the WMO shows less overestimation of total precipitation than remote-sensing precipitation; (2) in the downstream, the TRMM shows better runoff simulation accuracy in the tributaries, and the WMO shows better results in the mainstreams. Therefore, at basin outlets in mainstreams, the Nash–Sutcliffe efficiency coefficients of monthly runoff by the WMO are higher than the simulations by the TRMM; (3) for the whole basin during dry seasons, the LAI variation is correlated with the outlet runoff, which is similar to the correlation with three- to six-month accumulated precipitation. TRMM products can be used to depict both precipitation deficit and runoff deficit, which cause vegetation variations. Our research suggests the potential of microwave precipitation products for detailed watershed runoff simulations and water management.

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Enhancing forest inventories with mountain pine beetle infestation information

The Forestry Chronicle ◽

10.5558/tfc81149-1 ◽

2005 ◽

Vol 81 (1) ◽

pp. 149-159 ◽

Cited By ~ 9

Author(s):

M A Wulder ◽

R S Skakun ◽

S E Franklin ◽

J C White

Keyword(s):

Remote Sensing ◽

Forest Inventory ◽

Mountain Pine Beetle ◽

Landsat Imagery ◽

Data Sources ◽

Polygon Decomposition ◽

Forest Inventories ◽

Mountain Pine ◽

Pine Beetle ◽

Different Sources

Polygon decomposition is an approach for integrating different data sources within a GIS. We use this approach to understand the impacts associated with mountain pine beetle red attack. Three different sources of red attack information are considered: aerial overview sketch mapping, helicopter GPS surveys, and Landsat imagery. Existing inventory polygons are augmented with estimates of the proportion and area of red attack damage. Although differences are found in the area of the infestation, the affected forest stands have similar characteristics. Polygon decomposition adds value to existing forest inventories through update and the incorporation of new attributes applicable to forest management. Key words: polygon decomposition, forest inventory, GIS, mountain pine beetle, red attack, remote sensing, Landsat

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Challenges for the operational detection of mountain pine beetle green attack with remote sensing

The Forestry Chronicle ◽

10.5558/tfc85032-1 ◽

2009 ◽

Vol 85 (1) ◽

pp. 32-38 ◽

Cited By ~ 22

Author(s):

Michael A Wulder ◽

Joanne C White ◽

Allan L Carroll ◽

Nicholas C Coops

Keyword(s):

Remote Sensing ◽

Mountain Pine Beetle ◽

Survey Methods ◽

Remotely Sensed ◽

Attack Detection ◽

High Spectral Resolution ◽

Remotely Sensed Data ◽

Mountain Pine ◽

Pine Beetle ◽

Attack Trees

Mountain pine beetle infestations are spatially correlated; current (green) attack is often located near previous (red) attack. This spatial correlation between the green and red attack stages enables operational survey methods, as detection of red attack trees—typically from an airborne survey such as a helicopter GPS survey or aerial photography—guides the location of subsequent ground surveys for green attack trees. Forest managers, in an attempt to understand beetle movement and infestation patterns, hope to utilize remotely sensed data to detect and map green attack trees, with the expectation that the spatial extent, accuracy, and timeliness afforded by remotely sensed data will greatly improve the efficacy of beetle treatment and control. In this communication, we present the biological, logistical, and technological factors that limit the operational utility of remotely sensed data for green attack detection and mapping. To provide context for these limitations, we identify the operational information needs associated with green attack and discuss how these requirements dictate the characteristics of any potential remotely sensed data source (e.g., spatial, spectral, and temporal characteristics). Based upon our assessment, we conclude that the remote detection of green attack is not operationally viable, and is unlikely to become so unless the limiting factors we have identified are altered substantially or removed. Key words: green attack, remote sensing, operational, insect survey, high spatial resolution, high spectral resolution

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