10 results about "Secondary forest" patented technology

The invention discloses a secondary forest development stage division method based on forest stand state characteristics, and belongs to the technical field of forest development stage division. Comprising the following steps: selecting an initial index set, performing index forward and standardization processing on sample plot survey data, and forming a data set; classifying the data by adopting a system clustering analysis method, checking and analyzing a state index influencing a classification result, and selecting plt; the index of 0.05 constitutes a secondary forest development stage division index system; carrying out weight assignment, then correcting the weight by adopting an entropy evaluation method, and determining the comprehensive weight of the index by utilizing a Lagrange multiplier method; the sum of the dimension-removed value of the division index and the comprehensive weight value product is a development stage value; according to an equidistant method, the development stage of the secondary forest is divided into a forest gap stage, a renewal stage, a differentiation stage, a built-up stage and a stable stage. Depending on forest age is avoided, and the method is suitable for multi-tree mixed secondary forests; the indexes are easy to obtain, and the division result can accurately guide forest management.

The invention relates to a method for secondary afforestation under forest gaps of low-quality artificial pure forests and degenerated secondary forests, and relates to the technical field of forest cultivation, comprising the following steps: (1) selecting afforestation tree species; (2) configuration of afforestation tree species; (3) full life cycle design; (4) afforestation operation. According to the method, the forest stand value is remarkably increased, meanwhile, the development of soil microorganisms is promoted, and the overall quality of forest soil and an ecological system is improved. Compared with a pure forest, the method has the advantages that the accumulation growth amount of the operation forest stand reaches 19.7 m < 3 > / hm < 2 >. A, compared with a pure forest, the growth amount is increased by 3.14 m < 3 > / hm < 2 >. A, the growth amount of a single forest tree is increased by more than 3 times compared with that of a contrast forest, and under the condition that the sustainable operation basic principle that the yield is smaller than the growth amount on the forest stand level is met, the standing tree yield of 17.39 m < 3 > / hm < 2 > can be obtained every year, and the pure income is 7600 yuan / hm < 2 >. A.

The invention relates to the field of forest ecological monitoring and operation management, and provides a method for quantifying the complexity of a three-dimensional structure of a forest canopy based on LiDAR and fractal dimension. Aiming at the key bottleneck that traditional canopy structure quantitative indexes are difficult to represent cross-scale three-dimensional space heterogeneity, high-precision three-dimensional point cloud data are acquired by using foundation and tower footing LiDAR, and precise individual tree segmentation is realized in combination with a machine learning clustering algorithm and tree structure features. According to the method, the three-dimensional void rate of the forest stand canopy is calculated, and the fractal dimension of a single tree is calculated by adopting a box counting method. The weight of a single tree is determined by comprehensively considering a single tree structure and a spatial distribution pattern of the single tree structure, the fractal dimension of a stand scale is calculated, a canopy structure complexity index is constructed in combination with a stand three-dimensional void rate, and scale-free quantification of the forest canopy three-dimensional structure complexity is achieved. According to the method, typical temperate zone secondary forest monitoring data is used for simulation and verification, and technical support is provided for forest accurate operation (cutting and complementary planting), carbon sink evaluation, biodiversity protection and the like.

The invention discloses a natural secondary forest tree species classification method based on multi-modal data fusion. The method comprises the following steps: S1, building a tree species space-spectrum feature extraction module; s2, building a tree canopy global context sensing fusion module; s3, building a tree perception detail optimization module; s4, building a tree species classification network based on multi-modal data fusion; s5, after the tree species classification network with multi-modal data fusion is built, corresponding hyperspectral and laser radar data sets are used for training and testing; according to the method, the problems of fuzzy crown boundary, difficulty in distinguishing tree species with similar structures and low classification precision caused by difficulty in considering long-distance global dependence modeling and fine-grained local detail reservation when an existing tree species classification technology is used for processing a complex forest stand environment are solved.

The invention discloses a natural secondary forest succession stage quantitative division method, and relates to the technical field of forestry, and the method comprises a forest stand investigation step, a forest stand factor calculation and measurement step, and a stage determination factor calculation and succession stage determination step. The method is limited in determination such as tree species composition, forest tree size, under-forest renewal condition and the like. In addition to common forest tree size related factors, factors such as dominance degree of top-level tree species, mingling degree and soil total phosphorus which are difficult to directly and visually inspect are also included. Through the comprehensive factor consideration mode, the actual state and succession stage of the natural secondary forest can be reflected more accurately, and judgment errors caused by one-sidedness of a single factor or a part of factors are avoided.

The invention discloses a method for quickly constructing a choerospondias axillaris mixed forest with a near-natural community structure, which comprises the following steps: by taking an existing natural secondary forest as an operating object, simulating a local top-level forest mode, selecting forest land, selecting tree species, constructing a construction mode and cultivating and managing. The method comprises the following steps: properly thinning and tending, complementarily planting choerospondias axillaris in a forest window, effectively maintaining the soil fertility and quickly recovering a community structure by utilizing the characteristics that the choerospondias axillaris grows quickly and has a large amount of fallen leaves, and performing organic matter coverage and community density regulation and control on target broad-leaved trees. The problems that traditional secondary forest transformation is large in risk, poor in structural stability, low in economic benefit and the like are solved. A local top-level forest mode is simulated, a community composed of an arbor layer I mainly comprising a deciduous plant choerospondias axillaris and an arbor layer II mainly comprising a lauraceae plant evergreen broadleaf plant is rapidly built, the optimal community recovery mode of a natural forest is achieved, the purposes of short recovery period, large annual growth and stable community growth are achieved, the production cost is reduced, and the economic benefit is increased. Wide production and application prospects are realized.

This invention relates to the field of ecological afforestation technology, specifically to a rapid secondary forest construction system and method based on vegetation restoration. The system includes: a geomorphic fractal quantification module, a soil physical property inversion module, a site suitability screening module, an ecological fatigue calculation module, and a succession regulation decision module. In this invention, by analyzing the fractal characteristics of surface micro-geomorphology, a mapping relationship between surface roughness and underground soil physical properties is established. A depth attenuation model is used to invert soil physical state indicators at specific depths. Under the premise of non-destructive surface detection, suitable underground growth spaces that meet root penetration resistance requirements are accurately located. A non-Markov memory effect algorithm is combined to process historical environmental monitoring time-series data, quantifying the cumulative ecological fatigue caused by extreme climate events to the site environment. The fatigue index is then integrated into the nonlinear adjustment process of the forest growth succession probability matrix.

The present application relates to a kind of microbial inoculant, specifically to a kind of microbial inoculant for improving subtropical secondary forest soil quality, belong to the field of biotechnology.The microbial inoculant includes A component, B component and C component;The A component includes Bacillus subtilis, Bacillus amyloliquefaciens and Bacillus mucilaginosus;The B component includes Shewanella putrefaciens, Podospora sp. And Burkholderia sp.;The C component includes superphosphate, adhesive, soil bulking agent and water.The microbial inoculant is generated by the microbial growth-metabolism-death process of microbial residue carbon and nutrient, green, efficiently improve subtropical secondary forest soil organic matter content, phosphorus-solubilizing nitrogen-fixing, improve soil microbial community structure, promote secondary forest positive succession.

This invention relates to the field of plant tending technology, specifically to a method for promoting the regeneration of secondary forests of Albizia julibrissin. The method includes: S1, measuring the area where Albizia julibrissin pods or seeds fall after pod splitting, and delineating areas with many rocks and poor soil quality as the population propagation zone; S2, treating the soil in the population propagation zone; S3, when the Albizia julibrissin in the population propagation zone grows to 0.4-0.6m, removing weeds and shrubs within 40cm-50cm around it, calculating the shading of the population propagation zone, and determining whether to prune or selectively cut down the trees around the population propagation zone based on the total shading Stotal of the population propagation zone. Compared with the prior art, the present invention promotes the regeneration of Albizia julibrissin secondary forest populations by determining the population propagation zone and applying specific microorganisms and water-retaining agents that promote the cracking of Albizia julibrissin seed coat structure to the soil of the population propagation zone. Furthermore, the invention promotes the vigorous growth of Albizia julibrissin seedlings under the secondary forest by determining the pruning or selective felling of trees around the population propagation zone based on the total shade of the population propagation zone.

The invention discloses a supervised classification-based method for extracting a secondary forest window from a remote sensing image, and relates to the technical field of forest window remote sensing identification, and the method comprises the steps: firstly collecting and preprocessing a high-precision remote sensing image, then setting a random sample belt, recording coordinates, importing the image, and then interpreting the image through a supervised classification tool, a test algorithm is constructed to continuously adjust a classification system, finally post-processing is performed on a classification result to complete forest window identification and vectorization output, the secondary forest window is accurately and efficiently extracted from a remote sensing image, important data support is provided for research and monitoring of secondary forests, and the structure and dynamic change of a secondary forest ecosystem can be known.