Yunnan Pine Forest Research Articles

Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR

Infestations of Tomicus spp. have caused the deaths of millions of Yunnan pine forests in Southwest China; consequently, accurate monitoring methods are required to assess the damage caused by these pest insects at an early stage. Considering the limited sensitivity of optical reflectance on the early stage of beetle stress, the potential of thermal infrared (TIR) can be exploited for monitoring forest health on the basis of the change of canopy surface temperature (CST). However, few studies have investigated the impact of the leaf area index (LAI) on the accuracy of TIR data-based SDR assessments. Therefore, the current study used unmanned airborne vehicle (UAV)-based TIR and light detection and ranging (LiDAR) data to assess the capacity of determining the potential for using TIR data for determining SDR under different LAI conditions. The feasibility of using TIR for monitoring SDRs at the tree level and plot scales were analyzed using the relationship between SDR and canopy temperature. Results revealed that: (1) prediction accuracy of SDR from CST is promising at high LAI values and decreases quickly with LAI, and is higher at the single tree scale (R2 = 0.7890) than at the plot scale (R2 = 0.5532); (2) at either single tree or plot scale, a significant negative correlation can be found between CST and LAI (−0.9121 at tree scale and −0.5902 at plot scale); (3) LAI affects the transmission paths of sunlight and sensor, which mainly disturbs the relationship between CST and SDR. This article evaluated the high possibility of using TIR data to monitor SDRs at both tree and plot levels and assessed the negative impact of a low LAI (<1) on the relationship between temperature and SDR. Accordingly, when measuring forest health using TIR data, additional data sources are required to eliminate the negative impact of low LAIs and to improve the monitoring accuracy.

Trend Analysis of Coverage Variation in Pinus yunnanensis Franch. Forests under the Influence of Pests and Abiotic Factors

The Yunnan pine (Pinus yunnanensis Franch.) is one of the primary forest vegetation types in Yunnan Province and has prominent economic value and ecological significance. Monitoring changes in Yunnan pine forests contributes to their health management. The normalized differential vegetation index (NDVI) is an essential indicator for studying regional vegetation change. Landsat images were used to analyze the NDVI changes of Yunnan pine forests in rainy and dry seasons as well as the NDVI changes of Yunnan pine forests from 2009 to 2020. The results showed that the NDVI in Yunnan pine forests showed opposite trends in the rainy and dry seasons, with an increase in the rainy season and a decrease in the dry season. The areas of NDVI decline during the rainy season are mainly located at the edges of the forest area, with very few within the forest area. The main reason for the decline in NDVI within the forest area was pests. From 2009 to 2020, the areas where NDVI increased and decreased were 92.23% and 7.77% of the pine forests, respectively. The decreasing areas were mainly located at the edges of the pine forests, with sporadic distribution within the forest areas. The drought led to a significant decline in NDVI in the forest area in 2010 and 2013. Due to the lack of protective measures, the forest area in Xijiekou Town was severely infested with pests, with complex NDVI changes and strong fluctuations, and areas of severe pest infestation could be detected by NDVI imagery. This study is meaningful for forest protection and explores the potential capability of Landsat imagery for pest detection.

Effects of stand and landscape level variables on shoot damage ratios caused by shoot beetles in Southwest China

The outbreaks of shoot beetles (Tomicus yunnanensis Kirkendall and Faccoli and Tomicus minor Hartig) have caused widespread tree mortality in Southwest China. However, multi-scale variables explaining the shoot damage ratio (SDR) caused by shoot beetles have never been studied. The objective of this study was to determine the effects of stand-level and landscape-level variables on SDR during outbreaks of shoot beetles in Yunnan pine forests. Sixty-five plots were generated during 2015–2017 based on beetle infestation maps derived from multi-data Landsat images and field survey. Nine explanatory variables were quantified to explain the SDR severity. The forest cover was obtained from a Worldview-3 high‐resolution image. We used the forest resource planning and design survey data to develop landscape context variables. The beetle pressure variable was calculated using beetle infestation maps. The relative importance of the explanatory variables was analyzed using multi-model inference. We established that SDR was higher in the plots that were closer to roads and severely damaged forest areas in the previous year. SDR was negatively affected by edge density (ED) and forest cover, but positively affected by the mean shape index (SHAPE MN) and aspect. Landscape-level variables are probably the more important predictor, stand-level variables also had a significant effect on the shoot beetle outbreak. The prediction models including stand-level variables and landscape-level variables were built. Identifying variables that drive beetle-caused SDR contributed to the improvement of the existing strategies for outbreak control.

Using the 3D model RAPID to invert the shoot dieback ratio of vertically heterogeneous Yunnan pine forests to detect beetle damage

As a significant forest disturbance factor, pine shoot beetles (PSB) have caused widespread tree mortality in Yunnan pine forests over southwestern China. Due to significant vertical heterogeneity of tree crowns, empirical remote sensing methods or one-dimensional (1D) radiative transfer (RT) models are not qualified enough to accurately retrieve the damage level of PSB damage. Therefore, three-dimensional (3D) RT models which have the ability to describe vertically complex heterogeneous canopies are more suitable for the damaged forests. In this study, shoot dieback ratio (SDR) was used to parameterize the Radiosity Applicable to Porous IndiviDual Objects (RAPID) model, in order to accurately estimate the PSB damage. Specifically, the vertically heterogeneous characteristics of the biophysical and optical properties within the canopy were carefully considered. First, we developed an approach to reconstruct damaged forest scenes through individual tree segmentation from UAV-lidar data and in situ measurements. Second, the simulated reflectance of forest scenes was compared with the actual hyperspectral and Sentinel-2 (S2) reflectance data to evaluate the performance of the parameterized RAPID. Then, the parameterized RAPID model was used to create a look-up table (LUT) containing the leaf chlorophyll content (Cab), effective leaf area index (LAIe), SDR, and dead needles LAIe (LAIdead, LAIe × SDR) as well as the corresponding simulated canopy reflectance. Furthermore, tree allometric equations were used to regularize the LUT with the intention to improve the estimation accuracy. Good agreement (RMSE: 0.015-0.031, index of agreement: 0.97-0.99) was found between the simulated reflectance and the remote sensing observations. The retrieval accuracy of the biochemical and biophysical parameters using S2 data yielded R2 = 0.55 (normalized RMSE (NRMSE) = 19%) for Cab, R2 = 0.70 (NRMSE = 15%) for the LAIe, R2 = 0.66 (NRMSE = 25%) for SDR, and R2 = 0.82 (NRMSE = 18%) for LAIdead. Using hyperspectral data with higher spatial and spectral resolution yielded better retrieval performance. This study demonstrated that 3D models (such as RAPID) have great potential to accurately invert SDR and LAIdead to assess PSB damage for the forests.

Evaluating the Potential of WorldView-3 Data to Classify Different Shoot Damage Ratios of Pinus yunnanensis

Tomicus yunnanensis Kirkendall and Faccoli and Tomicus minor Hartig have caused serious shoot damage in Yunnan pine (Pinus yunnanensis Faranch) forests in the Yunnan province of China. However, very few remote sensing studies have been conducted to detect the different shoot damage ratios of individual trees. The aim of the study was to evaluate the suitability of eight-band WorldView-3 satellite image for detecting different shoot damage ratios (e.g., “healthy”, “slightly”, “moderately”, and “severely”). An object-based supervised classification method was used in this study. The tree crowns were delineated on a 0.3 m pan-sharpened worldview-3 image as reference data. Besides the original eight bands, normalized two-band indices were derived as spectral variables. For classifying individual trees, three classifiers—multinomial logistic regression (MLR), a stepwise linear discriminant analysis (SDA), and random forest (RF)—were evaluated and compared in this study. Results showed that SDA classifier based on all spectral variables had the highest classification accuracy (78.33%, Kappa = 0.712). Compared to original eight bands of Worldview-3, normalized two-band indices could improve the overall accuracy. Furthermore, the shoot damage ratio was a good indicator for detecting different levels of individual damaged trees. We concluded that the Worldview-3 satellite data were suitable to classify different levels of damaged trees; therefore, the best mapping results of damaged trees was predicted based on the best classification model which is very useful for forest managers to take the appropriate measures to decrease shoot beetle damage in Yunnan pine forests.

Retrieving canopy closure of Yunnan pine forests by using stochastic radiative transfer model

Retrieving canopy closure of Yunnan pine forests by using stochastic radiative transfer model

Detection of Pine Shoot Beetle (PSB) Stress on Pine Forests at Individual Tree Level using UAV-Based Hyperspectral Imagery and Lidar

In recent years, the outbreak of the pine shoot beetle (PSB), Tomicus spp., has caused serious shoots damage and the death of millions of trees in Yunnan pine forests in southwestern China. It is urgent to develop a convincing approach to accurately assess the shoot damage ratio (SDR) for monitoring the PSB insects at an early stage. Unmanned airborne vehicles (UAV)-based sensors, including hyperspectral imaging (HI) and lidar, have very high spatial and spectral resolutions, which are very useful to detect forest health. However, very few studies have utilized HI and lidar data to estimate SDRs and compare the predictive power for mapping PSB damage at the individual tree level. Additionally, the data fusion of HI and lidar may improve the detection accuracy, but it has not been well studied. In this study, UAV-based HI and lidar data were fused to detect PSB. We systematically evaluated the potential of a hyperspectral approach (only-HI data), a lidar approach (only-lidar data), and a combined approach (HI plus lidar data) to characterize PSB damage of individual trees using the Random Forest (RF) algorithm, separately. The most innovative point is the proposed new method to extract the three dimensional (3D) shadow distribution of each tree crown based on a lidar point cloud and the 3D radiative transfer model RAPID. The results show that: (1) for the accuracy of estimating the SDR of individual trees, the lidar approach (R2 = 0.69, RMSE = 12.28%) performed better than hyperspectral approach (R2 = 0.67, RMSE = 15.87%), and in addition, it was useful to detect dead trees with an accuracy of 70%; (2) the combined approach has the highest accuracy (R2 = 0.83, RMSE = 9.93%) for mapping PSB damage degrees; and (3) when combining HI and lidar data to predict SDRs, two variables have the most contributions, which are the leaf chlorophyll content (Cab) derived from hyperspectral data and the return intensity of the top of shaded crown (Int_Shd_top) from lidar metrics. This study confirms the high possibility to accurately predict SDRs at individual tree level if combining HI and lidar data. The 3D radiative transfer model can determine the 3D crown shadows from lidar, which is a key information to combine HI and lidar. Therefore, our study provided a guidance to combine the advantages of hyperspectral and lidar data to accurately measure the health of individual trees, enabling us to prioritize areas for forest health promotion. This method may also be used for other 3D land surfaces, like urban areas.

Forest Leaf Area Index Inversion Based on Landsat OLI Data in the Shangri-La City

Leaf Area Index (LAI) is an important index that reflects the growth status of forest vegetation and land surface processes. It is of important practical significance to quantitatively and accurately estimate Leaf Area Index. We used the Landsat-8 operational land imager single-band images, and 15 vegetation indices that were extracted from the multi-band were combined with the LAI data measured from the CI-110 canopy digital imager to establish the LAI estimation model. Through the leave-one-out cross-validation method, the accuracy of various model estimation results was verified and compared, and the optimal estimation model was obtained to generate the LAI distribution map of Shangri-La City. The results show that: (1) the multivariable model method is better than the single-variable model method when estimating LAI, and its determination coefficient is the highest (R2 = 0.7903). (2) The full-sample dataset is divided into Alpine Pine forest, Oak forest, Spruce–fir forest, and Yunnan Pine forest for analysis. The coefficient of determination of the model simulation is improved to varying degrees, and the highest R2 increased by 0.1652, 0.1040, 0.1264, and 0.0079, respectively, over the full-sample. The corresponding best models are LAI–DVI (Difference Vegetation Index), LAI–NNIR (normalized near-infrared), LAI–NMDI (Normalized Multi-band Drought Index), and LAI–RVI (Ratio Vegetation Index). (3) The LAI values in Shangri-La City ranged from 0.9654 to 5.5145 and are mainly concentrated in high vegetation coverage areas; and the higher the vegetation coverage level, the higher the LAI value.

Using Sentinel-1 multi-temporal InSAR data to monitor the damage degree of shoot beetle in Yunnan pine forest

Using Sentinel-1 multi-temporal InSAR data to monitor the damage degree of shoot beetle in Yunnan pine forest

Estimating Individual Tree Height and Diameter at Breast Height (DBH) from Terrestrial Laser Scanning (TLS) Data at Plot Level

Abundant and refined structural information under forest canopy can be obtained by using terrestrial laser scanning (TLS) technology. This study explores the methods of using TLS to obtain point cloud data and estimate individual tree height and diameter at breast height (DBH) at plot level in regions with complex terrain. Octree segmentation, connected component labeling and random Hough transform (RHT) are comprehensively used to identify trunks and extract DBH of trees in sample plots, and tree height is extracted based on the growth direction of the trees. The results show that the topography, undergrowth shrubs, and forest density influence the scanning range of the plots and the accuracy of feature extraction. There are differences in the accuracy of the results for different morphological forest species. The extraction accuracy of Yunnan pine forest is the highest (DBH: Root Mean Square Error (RMSE) = 1.17 cm, Tree Height: RMSE = 0.54 m), and that of Quercus semecarpifolia Sm. forest is the lowest (DBH: RMSE = 1.22 cm, Tree Height: RMSE = 1.23 m). At plot scale, with the increase of the mean DBH or tree height in plots, the estimation errors show slight increases, and both DBH and height tend to be underestimated.

Detecting Shoot Beetle Damage on Yunnan Pine Using Landsat Time-Series Data

Tomicus yunnanensis and Tomicus minor have caused serious shoot damage in Yunnan pine forests in the Yunnan Province of China. However, very few remote sensing studies have estimated the shoot damage ratio (SDR). Thus, we used multi-date Landsat satellite imagery to quantify SDRs and assess the possibility of using spectral indices to determine the beetle outbreak time and spread direction. A new threshold-based classification method was proposed to identify damage levels (i.e., healthy, slightly to moderately infested, and severely infested forests) using time series of moisture stress index (MSI). Permanent plots and temporal field inspection data were both used as references for training and evaluation. Results show that a single threshold value can produce a total classification accuracy of 86.38% (Kappa = 0.80). Furthermore, time series maps detailing damage level were reconstructed from 2004 to 2016. The shoot beetle outbreak year was estimated to be 2013. Another interesting finding is the movement path of the geometric center of severe damage, which is highly consistent with the wind direction. We conclude that the time series of shoot damage level maps can be produced by using continuous MSI images. This method is very useful to foresters for determining the outbreak time and spread direction.

Feasibility of using mobile phone to estimate forest Leaf Area Index: a case study in Yunnan Pine

ABSTRACTRemotely-sensed Leaf Area Index (LAI) is vital to describe the vegetation canopy and assess plant growth condition and healthy status. However, sufficient instant ground LAI samples are pre-required for calibration or validation, which is generally difficult to collect. We proposed a method, LAI-Mobile, to use mobile phones with low-cost fisheye lens to take fisheye photos and to invert LAIs, which may be popularized for ordinary people to generate big volume of LAI sample data. The feasibility of LAI-Mobile was tested by comparing with LAI−2200 and GF-1 satellite data (GF = high resolution) in a pest-invaded Yunnan pine forest area in Yunnan province of China. Results show significant correlation between LAI-2200 and LAI-Mobile data for forest plots with coefficient of determination (R2) = 0.706 and Root Mean Square Error (RSME) = 0.241, and GF-1 satellite images (R2 = 0.659 and RMSE = 0.268). The linear regression shows a good agreement between the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product and the inverted GF-1 LAI, with R2 = 0.649, RMSE = 0.795. Despite larger uncertainty for single fisheye image than LAI-2200, LAI-mobile can provide fast and convenient method to collect large amount of LAI, which will support remote sensing inversion of LAI at large scale.

Early post-fire regeneration of a fire-prone subtropical mixed Yunnan pine forest in Southwest China: Effects of pre-fire vegetation, fire severity and topographic factors

Early stages of post-fire regeneration are sensitive to various factors, and can provide critical information for understanding forest responses to fire disturbance and projecting subsequent forest dynamics. Yunnan pine forests are the most widespread conifer forest type in Southwest China, and are frequently threatened by fire disturbance. Post-fire regeneration of this forest type, however, has not been previously studied. In this study, we investigated the early post-fire regeneration in mixed Yunnan pine forests five months after a severe burn in 2013 on Mt. Qinglongling, Yunnan Province, China. We found very active post-fire regeneration in the burned area likely facilitated by ample summer rainfall, with an average regeneration density of ∼105stemsha−1. Species composition of the post-fire regeneration was highly similar to the pre-fire community–the similarity between the pre- and post-fire communities was 0.530±0.222. Elevation, pre-fire community type, and slope position were the three primary factors in the variations in regeneration density and species composition, while the impact of fire severity was low. The regeneration density of Pinus species and evergreen broadleaf species showed contrasting patterns across the environmental gradients, and the results implied that Yunnan forests are generally resilient to fire disturbance. The controlling impacts of the pre-fire community on post-fire regeneration are driven by the efficient regenerating strategies of dominant species, and are a comprehensive reflection of habitat conditions, which are primarily mediated by topographic features. Species regeneration strategies and the high resilience of the community to fire disturbance should be taken into account in fire prevention and management approaches for this forest type.

Differential water and soil conservation capacity and associated processes in four forest ecosystems in Dianchi Watershed, Yunnan Province, China

The soil and water conservation capacity of forest ecosystems is closely associated with precipitation, runoff, sediment delivery, and the export of the nutrients nitrogen (N) and phosphorus (P) from forest soil. Research has investigated the potential for decreasing runoff pollution from watersheds by improving the function of forest ecosystems in conserving soil and water. Four typical forest ecological systems, including Pinus armandii forest, Yunnan pine forest, mixed broadleaf-conifer forest, and broad-leaved forest, were compared for vegetation community structure and species diversity; water-holding capacity in canopy, litter and soil layers; and the relationship with surface runoff and the export of sediment, N, and P during the 2013 rainy season. The species number and species richness were in the range of 10 to 22 and 4.68 to 7.94, respectively. The Shannon-Wiener Index, Simpson Index, and Pielou Index were 0.95 to 1.67, 0.43 to 0.68, and 0.50 to 1.04, respectively. Canopy interception rate increased with decreasing precipitation. The linear correlation equation of the two parameters was described as I = −2.768x + 64.318, where I = the canopy interception rate (mm) and x = rainfall amount (mm). The broad-leaved forest had the greatest interception rate because of its dense canopy. Litter storage in the four forest ecosystems ranged from 7.31 to 11.98 t ha−1 (6,527 to 10,698 lb ac−1); the highest litter yield was found in the broad-leaved forest. In this ecosystem, both the undecomposed and semidecomposed litter had the most water retention capacity (14.02 t ha−1 [12,519 lb ac−1] and 14.71 t ha−1 [13,136 lb ac−1], respectively) and effective water interception rate (8.18 t ha−1 [7,304 lb ac−1] and 9.01 t ha−1 [8,045 lb ac−1], respectively). In the 0 to 60 cm (0 to 23.6 in) soil layer, the broad-leaved forest had the greatest soil field capacity of 188.80 t ha−1 (168,598 lb ac−1) and 18.88 mm (0.74 in) water depth equivalent. The amount of surface runoff, sediment yield, and export of N and P increased with rainfall amounts, but the four forest ecosystems differed significantly in these properties. The ranges of sediment yield and export of total N and P were 13.1 to 344.2, 0.16 to 1.55, and 0.02 to 0.10 mg m−2, respectively. The broad-leaved forest formed an extremely dense canopy and had the highest maximum water-holding capacity, porosity, and soil field capacity. These properties made this ecosystem highly suitable for decreasing surface runoff and the export of sediments, N, and P from the forest soil.

Forest biomass estimation in Shangri-La based on the remote sensing

To provide a basis for analyzing the forest carbon cycle，carbon storage，and carbon changes，forest biomass，the main parameter for determining carbon cycle processes in terrestrial ecosystems，was estimated. Variables measured in the study site of Shangri-La，Yunnan Province，included the vegetation index，precipitation，accumulated temperature，total radiation，and elevation. Established models of regional forest biomass estimations for spruce-fir forests （Abies georgei and Picea likiangensis），oak forests （Quercus aquifolioides），Yunnan pine forests （Pinus yunnanensis），and alpine pine forests （Pinus densata） were tested by combining layers of remote sensing integrated factors，of geographic comprehensive factors，and of the above listed variables. Then，forest biomass was estimated. Results revealed a total biomass of 1.14 hundred million tons with the following forest type biomass estimates in hundred million tons：spruce fir forests was 0.71，oak forests 0.14，Yunnan pine forests 0.09，and Alpine pine forests 0.20. Also，for these forest types，altitude and slope influenced distribution of total biomass，but aspect did not.［Ch，1 fig. 7 tab. 10 ref.］

THE STUDY OF THE ECONOMIC THRESHOLD OF TOMICUS PINIPERDA (COLEOPTERA: SCOLYTIDEA) ON YUNNAN PINE

Abstract By careful and long period field investigation of the population density of Tomicus piniperda L. and the growth of Yunnan pine (Pinus yunnanensis French.), and improved regression analysis, the economic threshold of Tomicus piniperda on Yunnan pine is determined: When the death rate of the trees is not more than 0.52%, the natural control factors can prevent the density of the bark beetle from increasing and no control measure is needed. When the death rate of the trees reaches 0.5%, the corresponding rate of shoots attacked is 0.61% and the mean of invading holes per tree 0.66, the cost for control is equal to the economic loss saved. So, considering both the economic justification and the prevention of the pest density from increasing, when the death rate of the trees is more than 0.5%, the corresponding rate of shoots attacked more than 0.61% and the mean of invading holes per tree more than 0.66, control measure should be taken. The paper also gives formulae for different Yunnan pine forest areas to calculate their own economic threshold according to their special situations.

Preliminary observations on the trunk attacks by Tomicus piniperda (L.) (Col., Scolytidae) on Yunnan pine in Kunming, China

AbstractMass attack by the pine shoot beetle, Tomicus piniperda (L.), on the trunks of living trees was a main cause of Yunnan pine forest damages in Kunming, China, from the 1970s to 1990s. The present study, based on analysis of egg galleries, proposed that mass attack was initiated with the establishment of a few primary invaders and intensified as the partners followed. The attacking beetles increased with time in the first part of a mass attack. Dispersion of the population on the trunk surface was started from the positions the primary invaders colonized and spread to nearby locations. The population was mainly distributed in the upper and middle of trunk, but revealed the highest density in the middle.