| With the development of information technology,it is very important to establish an efficient forest resources survey and monitoring system,which can obtain fast and reliable information on forest resources and can promote the development of ecological forestry and the people's livelihood forestry.To study the critical techniques and methods of forest management inventory of collective forest in southern China,three study sites were selected as Jiande city in Zhejiang province(site 1),Baisha forest farm in Jishui county,Jiangxi province(site 2),Dongtai forest farm in Jiangsu province(site 3).These sites are all located in the subtropical region with broad leaved evergreen forest and mixed forest.There are good natural conditions for forest growth.Intensive monitoring and management of forest resources is of important significance for improving forest quality.The data acquired by forest management inventory are important basis for guiding and standardizing the scientific management of forests,which is the basis for improving forest quality.Advanced forest resources monitoring technologies are constantly emerging.New technologies such as Unmanned aerial vehicle(UAV)remote sensing,airborne laser scanning,and terrestrial laser scanning have been used in forest second type inventory.The wide application of remote sensing,GNSS,database management,and internet technology provides opportunities and challenges to forest management inventory to achieve high time-efficiency,high-accuracy and multi-scale.The research studied the critical techniques and methods of forest management inventory and the main contents of the thesis are as follows:1.The method to extract forest sub-compartment change based on the comprehensive similarity index(FSi)equationThe comprehensive similarity index describes the similarity between the eigenvalues for changed sub-compartment and the forest sub-compartment which is sampled.The bigger the FSi value is,the higher the possibility that the land cover of the sub-compartment may change.The FSi equation is:FSi=(?),Where,bzi is the value of sub-compartment z in characteristic wave band i.Respectively,m1 and5 i are mean and standard deviation of the characteristic wave band iof the sampled forest sub-compartments.N is the number of characteristic wave bands.FZi is the similarity index statistics and FSj is the comprehensive similarity index.For study site 1(Jiande city),the results of Landsat 8 OLI data(2013-2014)analysis showed a normal distribution for the value of band 2 and band 3 of sampled sub-compartmentforest,the difference of NDVI and the main component of PC1,FZi.Based on this characteristic,comprehensive similarity index(FSi)was calculated to extract land cover change for forest sub-compartments in 2013-2014.When forest sub-compartment types were not differentiated,detection rate,omission ratio,and the false detection rate were 86.79%,13.21%,and 84.91%,respectively.When forest sub-compartments were classified based on slope and aspect,detection rate was over 90%.The comprehensive similarity index calculated by combination of characteristic variables of remote sensing data has different effects on improving the detection accuracy of forest sub-compartment change.The method was applied to the forest sub-compartments in the same area(2014-2015)and detection accuracy was above 80%.The method can be used to detect land cover change of forest sub-compartment,thus to help annual forest resources monitoring and forest management inventory.2.The method to estimate forest volume with double regression sampling technique based on UAV high resolution image dataThe forest volume estimation was based on a double regression estimation method consisting of many plots from which both ground and UAV high resolution image data were obtained.UAV images were used to estimate forest volume using double regression procedure.The procedure requires that the auxiliary variable x(UVA images in this case)is observed in a larger phase 1 sample of size n while the primary variable y(forest volume in sample plots)is observed in a phase 2 sub-sample of size n.The paper proposed several estimation schemas for auxiliary variable in double-regression sampling technique.The result shows that the estimation accuracy for the five variable schemas is all above 90%and R2 for schema 1,3 and 5 is all above 0.68.The forest volume estimations of the five schemas were relatively consistent which confirms that the combination of obtaining auxiliary variable based on UAV Image with double-regression sampling technique is feasible.The research shows a new way to survey and monitor regional forest resources by UAV technology.Based on schema 1,the average forest volume of study site 3(Dongtai state forest farm)was estimated at 142.6m3 per hectare for poplar plantation,the estimation interval was 133.8 to 151.4 i3 per hectare and the total forest volume for study site was between 942,65.1 m3 and 106628.21m3 with an accuracy of 93.85%.Based on schema 5,corresponding values were 143.0 m3 per hectare,133.5 m3 to 152.4 m3 per hectare,94,031.8 m3 to 107371.0m3,and an accuracy of 93.26%.Thus it is practical to use UAV remote sensing data to estimate forest volume with double regression sampling method.3.The method to estimate forest biomass with double regression sampling technique based on UAV high resolution image dataForest biomass was estimated based on a double regression sampling method consisting of many plots from which both ground and UAV high resolution image data were obtained.The model was given as W = 0.0039Cw1.1153 h2 8713 and average crown diameter and mean height were used in the equation.UAV data were used to derive mean crown diameter and mean height of sample plot.The average individual-tree biomass was calculated using average crown diameter and mean height of sample plot.Then the biomass of sample plot was obtained by multiplying the number of trees with average individual-tree biomass.The aboveground biomass was estimated at 73098.5247 kg per hectare according to double-regression sampling technique.The total aboveground forest biomass of poplar plantation of the study site was estimated to be 5.1468 × 107 kg,the estimation interval was between 4.7985 × 107 and 5.4987 × 107 kg with an accuracy of 93.2%.The improvement in precision of regression estimates depends on the strength of correlations between forest biomass and UAV images.4.The method to estimate forest volume with ? PS sampling and stratified ? PS sampling for forest sub-compartmentThe Estimator for unequal probability sampling without replacement of subcompartment was studied in the paper.The equation to estimate forest volume based on ? PS sampling of sub-compartment was presented and the total forest volume was estimated for the poplar plantation in Dongtai forest farm(study site 3).The results showed that the estimated forest volume of poplar plantation was 98,114.40 m3,estimation interval was 86,348.08 m3?109,880.72 m3 with an accuracy of 88.00%when the ? PS sampling of sub-compartment is not stratified.The accuracy of forest volume estimation by stratified ? PS sampling was better.With stratified ? PS sampling of forest sub-compartment,the estimated forest volume of poplar plantation was 9899,327.15 m3 with an accuracy of 92.24%.With the same sample size,the accuracy of forest volume estimation by the stratified ? PS sampling of forest sub-compartment has higher accuracy than that by the un-stratified ? PS sampling of sub-compartment.This provides an improved method for sampling estimation with unequal probabilities for forest resources monitoring of different types of sub-compartment.The results showed that for forest volume estimation,the ? PS sampling of sub-compartment is feasible and efficient for forest farm.The stratified ? PS sampling of sub-compartment significantly improves sampling efficiency and accuracy and serves as a new method for sub-compartment sampling estimation with unequal probabilities.5.The method to estimate forest biomass with ? PS sampling and stratified ? PS sampling for forest sub-compartmentFor the poplar plantation in the study area,the forest biomass was estimated to be 51,945,846.68 Kg with stratified ? PS sampling of sub-compartment,and estimation interval was between 47,916,655.21 and 55,975,038.16 Kg,with an accuracy of 92.24%.With the same sample size,the accuracy of forest biomass estimation by the stratified ? PS sampling of sub-compartment is higher than that of un-stratified ? PS sampling of sub-compartment.In summary,based on the non-replacement sub-compartment sampling method with unequal probabilities(? PS sampling of sub-compartment),the accuracy of forest volume estimation,biomass estimation and total tree number estimation of poplar plantation is good and meets the accuracy requirement of forest management inventory.The research complemented the theory and method of sub-compartment sampling with unequal probabilities to form forest resources monitoring system of ? PS sampling of forest sub-compartment. |
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