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The red band and the near-infrared band are often used as sensitive bands for remote sensing retrieval of forest stock volume, however the red edge band between the two is often ignored. To investigate whether the red edge band is sensitive to the accuracy of forest stock volume retrieval, the wide imagery data of GF-6 remote sensing satellite (GF-6 WFV), the DEM, the second survey data of forest resources in 2014 of Xining city were used, and the multiple linear regression model (MLR), the random forest regression model (RF) were employed. The predicting variables were collected from spectral characteristic, vegetation index, topographic factor and image texture. These variables were divided into two groups, the one is no red edge band (No Red-Edge), and the other is red edge band added (Red-Edge Added). The results show that: (1) the selected principal components which were derived from the texture variables of two groups by PCA method mainly explained the texture features of red, near-infrared and red-edge 1 band of the image. (2) Regarding the spectral variables, the surface reflectance of red and near-infrared band were selected because of the high correlation with the forest stock volume in No Red-Edge group, and in Red-Edge Added group, the surface reflectance of the red-edge 1 band was selected. Regarding the vegetation index variables, the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were selected in No Red-Edge group, and in Red-Edge Added group, the MERIS Terrestrial Chlorophyll Index (MTCI) was selected. (3) In Red-Edge Added group, the R² of the RF model is better than that of the MLR model, 0.6719 and 0.5487, respectively, and the root mean square error (RMSE) of the RF model is smaller than that of the MLR model, 26.3m³·ha−1 and 20.8m³·ha−1, respectively. (4) After eliminating the effect of the model on the accuracy of forest stock volume retrieval, the R² between the retrieval and the observed values in Red-Edge Added group increased by 11.6%, and the RMSE in Red-Edge Added group decreased by 9.1% compared to No Red-Edge group. Our results suggested that the red-edge band significantly improved the accuracy of the coniferous forest stock volume retrieval in Xining city. This study has high potential value in the remote sensing retrieval of forest stock volume. [ABSTRACT FROM AUTHOR]

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In order to better apply the light energy utilization model in subtropical evergreen coniferous forest areas, take Qianyanzhou, Taihe County, Jiangxi Province as an example, using satellite remote sensing data（GLASS LAI leaf area index product data set） from 2003 to 2005, MODIS surface reflectance product data set（MOD09 A1） and MODIS GPP product（MOD17 A2）) and China Terrestrial Ecosystem Flux Research Network（ChinaFLUX） the flux observation data of evergreen coniferous forests in Qianyanzhou, fraction of absorbed photosynthetically active radiation（FPAR）, 7 kinds of moisture limitation factors（fθ） and 3 kinds of temperature limitation factors（ft） in the 13 different energy efficiency models were reconstructed. A better model combination was obtained by comparing the coefficient of determination and the root mean square error（RMSE） of the flux station observations with the estimated values of 273 model combinations. At the same time, the optimization model was used to estimate gross primary productivity（GPP） and conduct sensitivity analysis. The results showed that the optimal model combination obtained by optimizing: the photosynthetically active radiation absorption ratio expressed in EVI, the moisture influence factor fθ-3 PG in the 3 PG model, and the temperature influence factor ft-TEM in the TEM model（R²=0.86, RMSE=0.47 μmol/m²·s） had the best simulation effect. The GPP simulation values of the optimized model were better than MODIS terrestrial level four standard data products（MOD17 A2）. Sensitivity analysis resultsshowed that the maximum light quantum efficiency αmax,enhanced vegetation index（ EVI） and photosynthetically active radiation（ PAR） were the direct linear variables of the model and had the greatest impact. Other parameters were determined by the order from big to small was light and minimum temperature Tmin,photosynthetic optimum temperature Topt,monthly average temperature T,and photosynthetic maximum temperature Tmax. Therefore,the optimization model in this paper had strong practical significance and was of great significance to further improve the accuracy of GPP estimation. [ABSTRACT FROM AUTHOR]

Taking the typical coniferous forest(Larix gmelinii forest, Pinus sylvestris var. mongolica forest) in the north of Daxing’an Mountains as the research object, the activity of aggregate urease, invertase, acid phosphatase and the geometric mean of enzyme activity were measured and analyzed, in order to find out the difference of enzyme activity between the two main coniferous forests in this area, and the change characteristics of enzyme activity with the particle size of aggregates. The results showed that: ①Large aggregates dominated the 0-5 cm soil layer of Larix gmelinii forest and 0～10 cm soil layer of Pinus sylvestris var. mongolica forest. As the soil deepened, the content of large aggregates decreased, and the content of large aggregates in the middle growth season of the two forest types was higher than that in the early and late growth season. ②The enzyme activity of aggregates of the two types of forest showed a consistent rule of increasing with the decrease of particle size, with the enzyme activity of <0.25 mm microaggregates being the highest, and the enzyme activity of aggregates decreased with the deepening of the soil layer. The activity of the aggregate urease and invertase in Larix gmelinii forest was slightly lower than that in Pinus sylvestris var. mongolica forest, and the activity of acid phosphatase was higher that in Pinus sylvestris var. mongolica forest. The aggregate urease of the two types of forest was the highest at the beginning and end of the growing season, while the activity of invertase and acid phosphatase was the highest in the peak growing season from July to August. ③The geometric mean of the enzyme activity of soil aggregates showed that the Pinus sylvestris var. mongolica forest was higher than that of Larix gmelinii forest, which decreased with the decrease of soil layer, and the mean of enzyme activity decreased with the increase of particle size. The geometric mean of enzyme activity of the two forest types was higher in the middle growing season. [ABSTRACT FROM AUTHOR]

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In order to study the feasibility of inverting leaf water content from SAR images combined with optical images, the paper takes the moon lake national forest park in Changchun City, Jilin Province as the study area, and uses Sentinel-1B and Landsat 8 OLI remote sensing images and the leaf water content obtained through field investigation as the data source. Through the correlation analysis, the band combinations and vegetation indices with large correlation with the leaf water content are selected and the principal components are extracted. The linear, quadratic polynomial, cubic polynomial and exponential models between the principal components and the leaf water content are established. Finally, the paper uses the model with the highest precision to reverse the leaf water content of the moon lake national forest park. The results show that: Sentinel-1B VV polarization, VH / VV polarization ratio and OLI sensor short wave infrared 1 band, short wave infrared 2 band, the normalized difference water index (NDWI), the ratio vegetation index (RVI) have large correlation with leaf water content. The principal components extracted from the combination of Sentinel-1B and Landsat8 OLI data are more correlated with leaf water content than Landsat8 OLI data alone. The cubic polynomial model in the inversion model established using the extracted principal components and leaf water content has the highest fitting accuracy (R2=0.6299, RMSE=0.0358). It shows that Sentinel-1B combined with Landsat8 OLI data can better reverse the leaf water content of coniferous forest. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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The forest area of Tibet ranks among the top in China, and the forest resources in Tibet play an important role in water conservation and ecological service. Therefore, it is of great significance to assess the assets of forest natural resources in this region. However, existing products and statistical data related to forest cover fail to meet the demands for the assessment of forest natural resource assets in this region, and it is necessary to explore a fine - scale forest classification method suitable for this region. Based on the cloud computing platform Google Earth Engine ( GEE ), this study constructed the temporal, spatial, spectral, and auxiliary feature sets of the forest coverage in Motuo County using the Landsat8 remote sensing images of 2015 and 2020, as well as field survey data, and the basic geographic data. Then, it conducted forest classification using the random forest ( RF) and classification and regression tree ( CART) algorithms. As indicated by the accuracy evaluation of the assessment results obtained using the two algorithms, the forest classification results of 2015 and 2020 obtained using the RF algorithm had relatively high accuracy, with overall classification accuracy of 0. 88 and 0. 87, respectively and Kappa coefficients of both greater than 0.8. The analyses of the areal and spatio - temporal characteristics of forest classification results show that: 1 Motuo County had a total forest area of 34 000 km ² in 2015, with a forest cover rate of up to 84. 63%, which was 2% less than that in 2020; 2 The forest resources in Motuo County are dominated by broadleaved forests, which are mainly distributed in Yarlung Zangbo Grand Canyon and low - altitude areas and accounted for 72. 27% and 75. 37% of the total forest area in 2015 and 2020, respectively. Coniferous forests accounted for 25. 96% and 23. 19% of the total forest area in 2015 and 2020, respectively and are concentrated in high - altitude areas, such as the Namcha Barwa and Gyala Peri peaks. This study determined the spatio - temporal distribution of the forests in Motuo County in 2015 and 2020 by developing a spatio - temporal -spectral classification method. It can provide a reference method for calculating specific forest cover indices SDGs and fill the gap of forest data of smaH zones. The obtained monitoring data will provide data support for the natural asset assessment and ecological function evaluation in Motuo County. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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【Objective】 Exploring the spatio-temporal variation of fractional vegetation cover and its relationship with climate and topographic factors of southwest China can provide reference basis for monitoring regional ecological environment quality and evaluating forestry restoration project effect. 【Method】 In the study, based on MODIS normalized difference vegetation index (NDVI), in situ climate data, digital elevation model (DEM), and vegetation type data on the basis of dimidiate pixel model, Theil-Sen Median analysis, Mann-Kendall significance test, and partial correlation coefficient, the spatio-temporal variation of fractional vegetation cover in southwest China during 2000-2020 was studied, the partial correlation coefficient and the time-lag effect between fractional vegetation cover and precipitation and temperature were analyzed, and the relationship between fractional vegetation cover and topographic factors was explored. 【Result】 The fractional vegetation cover of the whole study area as well as all vegetation types showed a fluctuant increasing trend, and the changing trend exhibited obvious spatial heterogeneity with the increasing rate higher in the southeast and lower in the northwest of southwest China from 2000 to 2020.The increasing rate of broadleaf forest was higher than that of other vegetation types, and the fractional vegetation cover of cultivated vegetation was higher than that of other vegetation types. The fractional vegetation cover was positively associated with both precipitation and temperature, and the partial correlation coefficient between fractional vegetation cover and temperature was higher than that of precipitation, indicating that temperature dominated vegetation growth in southwest China. The fractional vegetation cover was maximally response to the variation of previous 0-2 months precipitation and temperature. The growth of coniferous forest, broadleaf forest, shrub and grassland was mainly affected by temperature, while cultivated vegetation and meadow were mainly affected by precipitation. In terms of slope, the improvement of vegetation coverage increased first and then decreased with the increase of slope, and both the improvement and degradation effects reached the maximum value between 2° and 6°.From the perspective of slope upward, the change trend of vegetation coverage on the plane was mainly degradation, while on other slopes it was mainly improvement. From the perspective of curvature, the vegetation coverage showed an improvement trend in the area with small curvature, and a degradation trend in the area with large curvature. In terms of elevation, vegetation coverage showed a trend of first improvement, then degradation and then improvement with the increase of elevation, and vegetation deterioration was the most serious at the height of 3000-4000 meters. The influence of elevation on vegetation coverage change was higher than other topographic factors. 【Conclusion】 The overall fractional vegetation cover had improved in southwest China from 2000 to 2020,and the changing trend varied on different topographic factors and different vegetation types. The fractional vegetation cover exhibited considerable correlation with precipitation and temperature, while the correlation and time lag effects between fractional vegetation cover and precipitation as well as temperature showed obvious regional differentiation. [ABSTRACT FROM AUTHOR]

[Objective] The present paper aimed to study the contribution of litter respiration to total Rs in Alpine coniferous broad-leaved natural forest, and the coniferous broad-leaved forest in SE Jila mountain, Southeast Tibet. [Method] The RS rate of coniferous and broadleaved forest was measured by li-8100 A (Li COR, Lincoln, NE, USA) open circuit soil carbon flux measurement system. [Result] (i) The contents of SOC, ROC and TN in the surface layer of coniferous forest were higher than that of broad-leaved forest, and coniferous forest was easier to make soil acid; (ii) There were differences among the total Rs measured on different dates, but within three days, the results showed that coniferous forest > broad-leaved forest, and the respiration rate of litter layer was broad-leaved forest > coniferous forest; within three days, the contribution rate of litter respiration to the total Rs rate of coniferous forest was lower than that of broad-leaved forest; (iii) The soil temperature of the litter layer and the removal of litter layer in broad-leaved forest was between 10. 80-15. 30 and 8. 30-15. 40 °C in the period of 8: 00-20 :00 in three days. It could be seen from the daily variation coefficient of litter layer and litter removal that the soil after litter removal was more easily disturbed by the change of external environmental conditions. The soil volume moisture content of the treatment of the litter layer and the removal of the litter layer was between 0. 013-0. 015 and 0. 13-0. 18 m3 • m-3, respectively. The soil volume moisture content of the treatment of the litter layer was significantly higher than that of the litter layer. The diurnal variation of soil temperature and humidity in coniferous forest was consistent with that in broad-leaved forest. (iv) In this study, there was an exponential correlation between Rs rate and soil temperature and humidity for three days. According to the bivariate regression model, the fitting degree between Rs and soil temperature and volume water content was quite different in different measurement dates, and the removal of litter layer was more susceptible to the change of soil temperature and volume water content. At the same time, Rs was more easily affected by soil temperature than the fitting degree of soil volume water content. [Conclusion] The litter layer in the lower layer of high forest is the main source of soil respiration, especially the broad-leaved forest, while retaining nutrients, conserving water resources and resisting external interference. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Objective] This paper aimed to promote the sustainable development of the truffle industry to make the truffle industry strong and strong, and the truffle industry was regarded as the pillar industry of the mountain farmers to get rich and poor. It was necessary to carry out the study on the climatic ecological suitability analysis and geographical distribution of the truffle resources in Panxi area. [Method] Using ground-based meteorological observations, 1981-2017, mathematical statistical analysis was used to establish a multivariate linear regression spatial model of meteorological elements, and factors such as elevation, temperature, vegetation, and soil that were closely related to the growth of truffle were selected as indicators, using the ArcGIS10.x spatial analysis module. The spatial distribution of truffle suitability determined by each index was calculated and analyzed, and the climatic zones with different degrees of suitability weredivided by the method of collectivity. [Result] The main factors affecting the yield of truffle in Panxi area were precipitation, and the main meteorological disasters were drought, high temperature, and heavy rain. The suitable area of bacteria in the west of Panxi was mainly distributed in the coniferous broad-leaved forest belts of 1500 meters to 26 000 meters and subalpine dark coniferous forest belts below 26 000 meters. The slope was 20. To 300 sandy soil, loam soil, purple soil, lime soil. The pH value was 6.0 to 6.5 slightly acidic environments. The upper layer of the irrigation layer was 0.5 to 0.7, the annual rainfall was 700 mm to 1600 mm, the average annual temperature was from 16.0°Cto 23.0°C, the soil layer thickness was≥50 cm, and the dry season and rainy season were distinct;The sub-suitable area was a coniferous broad-leaved forest belt with an elevation of 2600 meters to 3500 meters;The unsuitable area was in the spruce forest belt, fir forest belt and alpine shrub belt above 3000 meters above sea level. [Conclusion]The map showed that the special climate ecological environment in Panxi area was a good condition for the natural growth of truffle, which played a decisive role in the yield of truffle, and the ecological climate suitability was consistent with the geographical distribution, and the result was basically the same as the yield distribution of truffle main producing areas. [ABSTRACT FROM AUTHOR]

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To reveal the spatio-temporal variation characteristics of vegetation net primary productivity(NPP)in the Haihe River Basin and the driving mechanisms of its spatio-temporal heterogeneity, we studied the spatio-temporal pattern evolution characteristics of vegetation NPP in the Haihe River Basin from 2000 to 2020 using coefficient of variation, trend analysis, and Hurst index, based on MOD17A3HGF dataset. Driving factors from natural and human aspects and their influence were also quantitatively explored for vegetation NPP variations using correlation analysis and the Geodetector model. The results were as follows:(1)The temporal variation of vegetation NPP in the Haihe River Basin presented a significant growth trend form 2000 to 2020, with a rate of 1.73 Tg C·a-1, and the annual average NPP was 326.75 g C·m-2·a-1; the average NPP of coniferous forests, broad-leaved forest, shrub, grassland and farmland were 313.59, 385.28, 353.03, 320.12, 295.22 g C·m-2·a-1.(2)In terms of spatial distribution, the high-value areas of vegetation NPP were mainly located in the northwest mountains, and the low-value areas were mainly located in the southeast plain; the overall situation of vegetation NPP was relatively stable, with an average coefficient of variation of 0.17; the future trend of NPP was anti-continuity and might decrease.(3)The correlation analysis showed that vegetation NPP was negatively correlated with temperature, and positively correlated with precipitation that was the main climate factor; NPP showed an “increase-decrease” trend with elevation and slope increasing; the conversion of farmland to grassland resulted in the largest increment of NPP at 732.22×10-3 Tg C.(4)From 2000 to 2020, the average influence value of different drivers was 0.2, and the order was precipitation > elevation > slope > humidity > temperature > sunshine duration > land use > wind speed. Overall, the results indicated that vegetation NPP in the Haihe River Basin was improving during the study period; productivity varied among different vegetation types, with the strongest being broad-leaved forest and shrubland; the dominant factors affecting the spatial distribution of vegetation NPP were precipitation, elevation and slope, and the influence of human factors was lower than that of natural factors. The results of this study provide some scientific reference and decision basis for the treatment of ecological environment such as soil erosion and vegetation degradation in the Haihe River Basin. [ABSTRACT FROM AUTHOR]

Drought is the most common and complex meteorological disaster in the world, which weakens the carbon sink function of terrestrial ecosystem. Exploring the response of vegetation to drought and choosing sensitive factors for drought detection will be helpful to obtain the impact of drought on vegetation and to understand the response process and the mechanism of vegetation to drought stress. Based on the Solar-Induced chlorophyll Fluorescence (SIF) and the standardized precipitation evapotranspiration index (SPEI), a maximum correlation coefficient method was used to investigate the response of vegetation photosynthesis to meteorological drought in China during the growing season from 2000 to 2018. Sensitivity and response time scale of vegetation to drought was compared for different drought levels and different vegetation types. The results showed that: (1) about 75.05% of total areas of China had a significant positive correlation between SIF and SPEI. These areas were mainly distributed in the northeast, southwest and Qinghai Tibet Plateau of China. The response time scale of most regions to SPEI was mainly medium and short term. (2) The proportion of SIF to SPEI was the lowest in spring, the highest in summer, and slightly decreased in autumn. The response time scale to drought was mainly short-term in spring, while the region with long response time scale in summer was increased compared with in spring. (3) The semi-arid region was the most sensitive to drought, while the arid region was the weakest. The response time scale of different climatic regions to drought was mainly short-term. (4) The selected vegetation types responded to drought in a short time scale. Grassland was the most sensitive to drought, while woodland and cropland were relatively weak. Besides, broad-leaved forest was more sensitive to drought than coniferous forest. The results showed that under different drought gradients and different vegetation types, SIF could quickly reflect the impact of environmental stress on vegetation photosynthesis. [ABSTRACT FROM AUTHOR]

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