157 results about "Forest plantation" patented technology

The invention provides application of a Pto-C3H35 gene haplotype in evaluation of poplar lignin content, and belongs to the technical field of molecular markers. According to the invention, the haplotype of the Pto-C3H35 gene is determined according to the nucleotides of SNP1, InDel1, InDel2, InDel3 and SNP2. The invention provides a primer group and a kit for determining the gene haplotype as well as a method for determining the gene haplotype, the lignin content of the poplar can be accurately judged by determining the genotype combination of the five sites, and superior plants with low lignin content are accurately and efficiently screened in the early growth stage of the poplar; the method is applied to new variety breeding of industrial poplar artificial forests, and effectively shortens the breeding period.

The invention relates to a forest cultivation dynamic monitoring method and system based on a remote sensing technology, and the method comprises the steps: collecting point cloud and multi-source topographic data of a complex topographic region through the fusion of an airborne laser radar and a satellite radar technology, and generating a high-precision point cloud and topographic parameter matrix through denoising and registration; a segmentation threshold value is dynamically optimized in combination with topographic features, the canopy point cloud penetration rate of abrupt slope and valley areas is improved, and the problem of canopy missegmentation caused by uneven point cloud density under a complex terrain is solved; constructing a penetration rate compensation model driven by terrain influence factors, correcting laser radar point cloud deviation and inverting high-precision tree height, crown breadth and biomass parameters; and based on fusion analysis of the multi-temporal vegetation index and a machine learning model, early warning of diseases and insect pests and fire hazards and dynamic evaluation of a man-made forest cultivation effect are realized. According to the method, the bottleneck of complex terrain monitoring is broken through, the forest parameter inversion precision and the real-time response capability are remarkably improved, and technical support is provided for precise management of forestry resources.

The invention relates to the field of man-made forest carbon sequestration, and particularly discloses a man-made forest carbon sequestration monitoring report automatic generation system and method.The method comprises the steps that firstly, carbon dioxide values of different environment heights, needle-leaved man-made forest growth environment data and needle-leaved man-made forest ecological productivity text data collected by a carbon dioxide monitor are obtained; and then feature extraction and correlation analysis are performed on the three by using a deep learning technology, and finally a man-made forest carbon sink monitoring report is generated through a generator, so that more accurate and effective support is provided for man-made forest management and environmental protection.

The invention relates to the technical field of plant cultivation, in particular to a phoebe bournei compound management method for cultivating long phoebe bournei with short phoebe bournei, which comprises the following steps: removing all plants on a forest land, and marking a management mode of a planting belt as follows: at least one line of phoebe bournei and one line of melaleuca alternifolia are interplanted, the line spacing of phoebe bournei is 2-3m * 3-7m, and the plant line spacing of melaleuca alternifolia is 1-3m * 1-3m; after phoebe bournei seedlings are subjected to seedling strengthening treatment, the phoebe bournei seedlings and melaleuca alternifolia are subjected to afforestation together, the melaleuca alternifolia are subjected to stubble cutting from January to February of the second year of afforestation and field planting, the stubble height is 10-35 cm, branches and leaves are taken to extract melaleuca alternifolia essential oil, waste generated through extraction and animal waste are mixed according to the ratio of 1: (1-2) for composting, organic fertilizer is obtained, and the organic fertilizer is applied in winter. Compared with the prior art, after the phoebe bournei is subjected to seedling strengthening treatment, the phoebe bournei is interplanted with the melaleuca alternifolia, the plant height, the ground diameter and the maximum crown breadth of the phoebe bournei are increased, and the economic benefit of a man-made forest is increased by means of short cultivation and long cultivation.

The invention provides a method and device for evaluating the stability of a soil slope in a man-made forest area and electronic equipment, and relates to the technical field of slope protection. In the method, a multi-field coupling numerical model not only considers a hydrological effect and a mechanical effect, but also considers the comprehensive effect of the hydrological effect and the mechanical effect; and the comprehensive influence of the artificial forest on the slope stability under the real environment load can be accurately quantified and verified.

The invention discloses a man-made forest remote sensing extraction method based on satellite-borne laser waveform vertical structure complexity. Comprising the following steps: step 1, acquiring L1B geographic positioning waveform, L2A light spot scale global surface height and vegetation height and L2B light spot scale global crown coverage and vertical profile index product data of a satellite-borne full-waveform laser radar GEDI in a forest area; 2, carrying out the preprocessing of the obtained data, and obtaining a high-quality satellite-borne laser radar light spot in the region; 3, carrying out feature extraction, calculating the information entropy of the echo waveform of a single light spot and the waveform similarity of adjacent light spots from L1B geographic positioning waveform data, and extracting the canopy coverage and the leaf height diversity index from L2B light spot scale global crown coverage and vertical profile indexes; constructing a linear model of the height percentile in the L2A light spot scale global surface height and vegetation height data, and calculating the slope of the linear model as a feature; 4, inputting the five features, and performing classified extraction of artificial forests and natural forests by adopting a random forest model; and 5, evaluating the extraction precision and feature importance of the artificial forest. According to the method, a plurality of vertical structure characteristics of the forest are obtained through waveform information of the satellite-borne laser radar, the stand of the artificial forest is extracted in combination with the random forest model, and the artificial forest and the natural forest can be distinguished by fully utilizing the vertical structure characteristics of the satellite-borne laser waveform of the forest area.

The invention relates to the technical field of man-made forest directional reconstruction, in particular to a low-quality poplar man-made forest grading directional reconstruction method, which comprises the steps of deploying a plurality of sensors at different positions in a man-made forest area to construct a sensor network for data collection, and performing data processing on data collected by the sensors by utilizing a fusion algorithm; constructing a man-made forest degradation grading model, respectively extracting index data and sub-index data from the fused data, constructing a constraint function in a constraint layer for constraint, and constructing a stability index function and a degradation index function under an optimal weight coefficient; setting a non-degradation index threshold value in the target layer to realize grade division of artificial forest degradation; and a comprehensive transformation target function is constructed, directional transformation is performed according to the divided artificial forest degradation levels, and directional transformation of different degrees under different levels is realized by adjusting the adjustment coefficients under different levels.

The invention discloses a forest tree planting environment analysis and optimization method based on big data, and relates to the field of forest tree planting, and the method comprises the steps: obtaining high-resolution vegetation index data of a target region for continuous N years; eliminating fluctuation interference of clouds and seasons of the high-resolution vegetation index data to construct a pixel-by-pixel vegetation growth track; obtaining artificial forest tending data, meteorological observation data and topographic data of the target area for continuous N years; according to the pixel-by-pixel vegetation growth track, the artificial forest tending data, the meteorological observation data and the topographic data, carrying out region attribute division to obtain a region division result; and optimizing forest planting strategies corresponding to the fluctuation area and the degradation area in the area division result in combination with artificial forest tending data. According to the method, the problems of unstable remote sensing data, insufficient adaptive modeling, extensive optimization strategy and the like in the prior art are solved, and the method has remarkable ecological value and application prospect.

The invention relates to the technical field of ecological breeding of scylla paramamosain and protection and restoration of mangrove forests. The invention provides a method for culturing scylla paramamosain by using mangrove forest, which comprises the following steps: (1) carrying out simple engineering transformation on the basis of a coastal wetland traditional culture pond, constructing a planting island in the middle of the culture pond, and reserving an annular tidal creek between the planting island and the periphery of the culture pond; (2) planting mangrove plants in the planting island to form a man-made forest; (3) small aquatic plants are planted at the bottom of the artificial forest, and large algae are planted in the annular ditches; and (4) putting the young blue crabs into the culture pond, and carrying out daily management until the blue crabs are harvested. The shellfish is used for providing main food supply for the blue crabs, and meanwhile water is purified; excrement of the blue crabs can be fed back to promote growth of mangroves, algae, mussels and the like; large algae can perform photosynthesis at the bottom of the pond to increase dissolved oxygen in water, and small sea grass is placed in the planting island to make up for the defect of low coverage of newly planted mangrove forest plant seedlings.

The invention discloses a forest management method for improving quality and efficiency of a fir man-made forest, application and the fir man-made forest, and the forest management method for improving quality and efficiency of the fir man-made forest comprises the following steps: cutting off part of forests in the fir man-made forest to enable the retention density of the existing forest stand to be 100-1050 plants / hm < 2 >; precious and indigenous tree species are planted in forest stand gaps in a complementary mode, and the complementary plant number is 300 plants / hm < 2 > to 1260 plants / hm < 2 >; the complementary planting tree species are at least three of phoebe bournei, sweetgum, schima superba, camphor tree, cyclobalanopsis glauca, taxus chinensis var mairei, gingko, schima superba, mangnolia officinalis, melia azedarach and sapium discolor. According to the method, quality improvement / operation transformation measures such as stand density adjustment and tree species configuration regulation are carried out on the fir man-made forests in different age groups, precious and indigenous tree species are selected according to local conditions for complementary planting, different-age, multi-layer and mixed stand is cultivated, precious wood is stored, and the operation purpose of quality improvement and efficiency improvement of the fir man-made forests is achieved.

The invention relates to the technical field of image processing, and discloses a man-made forest remote sensing image segmentation method, electronic equipment and a computer readable storage medium, the method introduces MMF, performs joint modeling on vegetation indexes such as NDVI, EVI and the like and RGB images so as to improve the ability of a model to distinguish a man-made forest from other ground features, and introduces KAN-HGC so as to improve the efficiency of the model to distinguish the man-made forest from other ground features. A graph structure is adaptively constructed by combining a space structure and feature similarity, so that the recognition capability of the model on a boundary region is improved, and GIGA is introduced, and key region features are enhanced through multi-dimensional attention, so that background interference is effectively inhibited, and the recognition accuracy of the model in a complex scene is improved; besides, an FReSCO optimizer is provided, a boundary enhancement and spectrum adjustment mechanism is introduced in training, and the adaptability and cross-scene generalization ability of the model to spectrum differences are improved.

The present invention relates to the technical field of directional transformation of plantation forests, and specifically to a method for hierarchical directional transformation of low-quality poplar plantation forests, including deploying a plurality of sensors at different positions in the plantation forest area to construct a sensor network for data collection, and using a fusion algorithm to process the data collected by the sensors; constructing a model for classifying the degradation levels of plantation forests, respectively extracting index data and sub-index data from the fused data, constructing a constraint function in the constraint layer for constraint, and constructing a stability index function and a degradation index function under the optimal weight coefficient, and realizing the classification of the degradation levels of plantation forests by setting an undegraded index threshold in the target layer; constructing a comprehensive transformation objective function, and performing directional transformation according to the classified degradation levels of plantation forests, and realizing different degrees of directional transformation at different levels by adjusting the adjustment coefficients at different levels.

The invention relates to the technical field of image processing, in particular to a forest pest intelligent identification method based on unmanned aerial vehicle remote sensing, which comprises the following steps: analyzing the position distribution condition of pixel points in each suspected pest connected domain in an LAB color space coordinate system, and obtaining the green sensitivity of each suspected pest connected domain; dividing all suspected pest and disease connected domains into high green sensitivity connected domains and low green sensitivity connected domains based on green sensitivity; acquiring an enhancement coefficient of a remote sensing grayscale image of the to-be-detected man-made forest according to a grayscale difference condition of pixel points between the high green sensitivity connected domain and the low green sensitivity connected domain and the edge feature parameters; enhancing the remote sensing grayscale image of the to-be-detected man-made forest according to the enhancement coefficient to obtain an enhanced remote sensing image of the to-be-detected man-made forest; and performing intelligent forest disease and insect pest identification based on the enhanced remote sensing image of the to-be-detected man-made forest. According to the invention, the recognition degree of the forest regions with diseases and pests in the man-made forest is improved.

The invention discloses a method for extracting parameters of a (subtropical) eucalyptus man-made forest based on an unmanned aerial vehicle laser radar. The method comprises the following steps: investigating a sample plot, and measuring and calculating actual forest parameters such as average diameter, average height, sectional area, stand volume and the like; acquiring and preprocessing high-density unmanned aerial vehicle laser radar point cloud data, and extracting three types of feature variables of height, density and vertical structure; deducing a model structural formula on the basis of a stock different-speed growth equation, and screening variables according to rules; constructing a power model by using a regular exhaustion method; the optimal model is screened out through model parameter estimation, inspection, significance analysis and the like. According to the method, the variable synergistic effect is mined through the exhaustion method, the multi-dimensional test system is constructed, and the exclusive model is customized, so that the problems of one-sided variable screening, insufficient model reliability and poor specific forest stand adaptability in the prior art are solved, high-precision estimation of core forest parameters is realized, and the method is suitable for large-scale popularization and application. And scientific and effective technical support is provided for refined management and sustainable operation of eucalyptus man-made forest resources.

The invention discloses an application of a PadPGR5a gene in fast-growing high-quality high-yield poplar breeding, belongs to the technical field of genetic engineering, and particularly relates to the application of the PadPGR5a gene in fast-growing high-quality high-yield poplar breeding, and the nucleotide sequence of the PadPGR5a gene is shown as SEQ ID NO.1. The invention further discloses an application of the PadPGR5a gene in fast-growing high-quality high-yield poplar breeding. Through phenotypic analysis, the plant height of the PadPGR5a gene overexpressed transgenic poplar is obviously increased, the net photosynthetic rate is obviously improved, and the oxidative stress resistance is obviously enhanced. It is shown that the PadPGR5a gene is an important forest fast-growing improved gene resource, and a new excellent transgenic variety can be provided for construction of a fast-growing high-quality high-yield poplar man-made forest by means of a genetic engineering technology.

The invention relates to the technical field of electric digital data processing, and particularly discloses a poplar man-made forest soil structure optimization method based on combination of charcoal and organic fertilizer, which comprises the following steps: firstly, collecting soil initial characteristic data of a poplar man-made forest, and evaluating a soil structure improvement now force index to determine a soil optimization mode; and executing corresponding structure optimization measures according to the optimization mode, and sending a prompt to a preset display port. And then performing charcoal persistence evaluation and organic pollutant accumulation risk evaluation on the optimized poplar man-made forest area. And finally, updating the soil optimization configuration according to the evaluation results. The process realizes accurate optimization and continuous improvement of the soil structure of the artificial poplar forest, and ensures that the soil quality meets the growth requirements of poplar.

The invention provides a phoebe bournei needle-broadleaf mixed tending and improving method, and belongs to the technical field of forest cultivation and artificial forest management. Comprising the following steps that (1) phoebe bournei and cunninghamia bournei which are arranged in a spaced mode serve as a unit, a plurality of units are arranged, and phoebe bournei and cunninghamia bournei rows among the units are vertically arranged; (2) digging holes to transplant phoebe bournei seedlings and Chinese fir seedlings, applying a base fertilizer during soil returning, and starting to expand the holes, hilling and apply fertilizer in the second year after transplanting; (3) felling is started in the fourth year of afforestation, phoebe bournei is cultivated with cunninghamia bournei, and wound healing agents are adopted for treatment after the cunninghamia bournei According to the method, the phoebe bournei and the Chinese fir which are arranged in an interlaced mode serve as one unit, multiple units are arranged, so that forest stand arrangement is rationalized through the scheme that the phoebe bournei and the Chinese fir between the units are vertically arranged in a row mode, base fertilizer is accurately applied in the soil returning process, the tending frequency is increased, sanitary cutting is conducted, and tree body management is refined; and the double purposes of rapid growth of phoebe bournei, high-quality dry material cultivation and large-diameter material cultivation of Chinese fir are achieved.

This invention discloses a device for in-situ monitoring of surface runoff-interflow on plantation forest slopes, comprising a surface runoff ditch, a surface runoff collection well, an interflow pipe, and an interflow collection well. The surface runoff ditch is horizontally constructed on the slope, with a lower center and higher sides. The surface runoff collection well is connected at the center of the ditch. The interflow pipe is vertically inserted into the slope, with multiple interflow collection boxes on its side. A central pipe is located at the center of the interflow pipe, and the central pipe is connected to the interflow collection well. Compared with existing technologies, this invention can collect interflow in continuous slopes.

The present application relates to a kind of artificial forest multi-source remote sensing extraction method, belong to the field of remote sensing data detection, comprising: obtaining multi-source remote sensing data and pre-processing, obtain pre-processing multi-source remote sensing data;Respectively obtain training sample and verification sample, according to verification sample screening training sample to obtain first sample set;According to first sample set, respectively train several classifiers to obtain several first classifiers, and first sample set is classified to obtain first classification result;Sample enhancement is carried out to minority classification result in first classification result to obtain second sample set;According to second sample set, respectively train several first classifiers, corresponding to obtain several second classifiers;Through several second classifiers, respectively classify pre-processing multi-source remote sensing data, and the final classification result is obtained by decision fusion to second classification result.The present application is based on multi-source remote sensing data, according to sample enhancement and decision fusion of classification result, improve the recognition ability to minority class sample.

The present application provides a method for extracting short-time-span growth of eucalyptus plantations based on UAV data, and relates to the field of forest resource investigation and forestry quantitative remote sensing research. The method comprises the following steps: field investigation and UAV data collection are carried out on selected eucalyptus plantation sample plots; DEM and monthly DOM and DSM of the sample plot are obtained, and monthly CHM is obtained by subtracting monthly DSM from DEM; a training set is divided from a data set composed of 12-month CHM, each tree crown on the CHM is labeled to obtain a labeled training set, and single tree segmentation is carried out by using the training set data and deep learning algorithm to obtain a monthly single tree segmentation vector diagram; single tree height, single tree crown and DOM spectral reflectance index of the sample plot are extracted; a single tree volume inversion model of the sample plot is constructed; single tree volume of eucalyptus plantations of the sample plot is extracted every month, and the sum is obtained to obtain stand volume, so as to realize short-time-span growth extraction of single tree and stand of eucalyptus plantations.

The application relates to a desert artificial forest space configuration method and device based on water resource carrying capacity, equipment and medium. The method comprises the following steps: collecting environmental data of a target desert area; dividing the target area into multiple regional partitions based on the environmental data; calculating a water resource carrying capacity index of each regional partition according to a preset water resource carrying capacity evaluation index; for each regional partition, combining the environmental data and the water resource carrying capacity index, and screening suitable artificial forest tree types by simulating an ecological hydrological process under a preset stand structure configuration; based on the artificial forest tree types, combining the environmental data and the water resource carrying capacity index to determine a planting density, and matching the tree types and the density to generate an artificial forest regional configuration scheme; and integrating all the regional configuration schemes to form a desert artificial forest space configuration scheme. The method can realize scientific space layout of the desert artificial forest, optimize water resource carrying capacity matching degree, and improve afforestation survival rate and ecological restoration effect.

The invention discloses a device and a method for measuring the canopy interception of a tropical man-made forest. The measuring device comprises an in-forest rainfall measuring device, an out-forest rainfall measuring device and a trunk flow measuring device. The measurement method comprises the steps of equipment deployment, calculation of penetration rainfall and calculation of canopy interception. According to the method, the rainfall outside the forest, the penetration rainfall and the trunk flow data are obtained, the crown interception data can be obtained through simple calculation, and the tropical artificial forest water source conservation capability is conveniently researched; the in-forest rainfall measuring device has the advantages that sundries accumulated in the rain gauge and shelters at the opening of the rain gauge can be automatically cleaned, the accuracy of the measuring process is ensured, and the in-forest rainfall measuring device can be suitable for tropical artificial forests in rainstorm and typhoon weather and with many litters, tropical artificial forests in the repairing process and the like; the corrected value is adopted to replace abandoned penetrating rainfall data, the problem of inaccurate measurement caused by a shielding object at the opening of the rain gauge can be corrected, and the measurement precision is greatly improved.

The invention relates to the technical field of seedling cultivation, in particular to a pinus massoniana artificial forest seedling cultivation device which comprises a peripheral cylinder, an inner surrounding sleeve is arranged in the peripheral cylinder, an annular filling area is formed between the inner surrounding sleeve and the peripheral cylinder, a stress clamping part is arranged on a hanging piece, and the stress clamping part is connected with the inner surrounding sleeve. The stress clamping part is bent upwards and then clamped at the top end of the outer surrounding sleeve, a radial expansion part is arranged on the inner surrounding sleeve, a flexible bending part is arranged at the position, close to the stress clamping part, of the hanging piece, the inner surrounding sleeve is expanded in the radial direction through the radial expansion part, and the stress clamping part and the flexible bending part serve as core buffering elements. Mechanical stress transmitted by expansion of the inner encircling sleeve or external acting force can be effectively absorbed and buffered, the risk that the stem epidermis is scratched or crushed in the growth and device operation process is remarkably reduced, the device is composed of an inner layer main part and an outer layer main part, seedling soil lifting can be achieved through the simple hand lifting function, the structure is simple, and operation is convenient.

The invention discloses a man-made forest stand structure and function integrated monitoring system and method based on multi-source data fusion in the technical field of man-made forest monitoring, and the system comprises a movable ground monitoring box, one side of which is provided with a gap groove for accommodating single trees; the unmanned aerial vehicle is used for carrying out multi-view observation on a single tree crown; the two parking platforms are symmetrically distributed on the ground monitoring box, located on the two sides of the notch groove and used for allowing the unmanned aerial vehicle to take off and land, and the unmanned aerial vehicle takes off from one parking platform and flies around the single tree along a preset flight path in the vertical plane so as to collect stand structure data of the bottom, the top and the side of the crown of the single tree. According to the system, multi-source data acquisition is achieved through the unmanned aerial vehicle and the ground monitoring assembly, the parking platform and the ground monitoring assembly are driven to synchronously and accurately rotate through the orientation conversion mechanism, unmanned aerial vehicle multi-path canopy collection and ground circumferential all-dimensional measurement are achieved, and the integrity of canopy structure data and the three-dimensional reconstruction precision are remarkably improved.

This invention relates to the field of forestry remote sensing monitoring technology and discloses a leaf SPAD prediction method based on UAV multispectral imaging. The method involves acquiring images of the target forest area through low-altitude multispectral aerial photography using a UAV, and extracting spectral data based on the locations of measured ground sampling points. Enhanced feature engineering is applied to the data to construct a multi-source feature set including original bands, basic vegetation indices, red-edge vegetation indices, and texture features. An early fusion strategy is used to construct a high-dimensional fusion feature vector, which is then input into an ensemble learning regression model based on ExtraTrees for training and prediction. Finally, a spatial distribution map of leaf SPAD values ​​in the forest area is generated. This invention effectively overcomes the shortcomings of conventional multispectral imaging, such as limited band size and saturation. By fusing the red-edge index and the ensemble learning algorithm, it significantly improves the accuracy and robustness of SPAD inversion, providing an effective technical means for precise management and rapid nutrient diagnosis of plantations.

This invention discloses a method and system for identifying planted and natural forests based on multi-source remote sensing and spatiotemporal joint optimization, belonging to the fields of remote sensing image processing, earth observation, and dynamic monitoring of forest ecology. Addressing the problems of high label noise and disordered temporal logic in existing technologies, this invention obtains high-purity training samples through multi-stage iterative purification by fusing CCDC mutation history and deep semantic features; obtains pixel-level confidence through independent initial classification by geographic partitioning; constructs an HMM model integrating ecological prior constraints and dynamic adaptive emission probabilities for Viterbi decoding; and performs constrained spatial restoration with cross-dimensional temporal security checks. This invention effectively suppresses temporal pseudo-variables and salt-and-pepper noise, achieving high-resolution thematic mapping year by year with no temporal logical contradictions and regular spatial patches, suitable for fine-grained forest classification and compliance monitoring of zero-deforestation laws in tropical and subtropical regions.

The present application belongs to the poplar plantation technical field, and particularly relates to a poplar plantation structure optimization and sustainable management method and system. The method comprises the following steps: S1, constructing a growth model of poplar, and dividing the poplar into three stages of a juvenile stage, a middle-aged stage and a near-mature stage according to the growth model; S2, collecting site factor data; S3, dividing the research area into multiple site grade areas, and configuring differentiated growth parameters, rotation periods and felling intensities for the site grade areas; S4, building a multi-objective linear programming model, and the objective function covers maximization of total wood yield, minimization of management cost and maximization of ecological benefit; S5, linearly fitting the juvenile stage, the middle-aged stage and the near-mature stage to obtain the corresponding linear equations of the stages, embedding the linear equations of S2 into the multi-objective linear programming model as constraint conditions, and clearly defining the forest age range of each stage; and S6, solving the multi-objective linear programming model to obtain optimal felling area, rotation period and felling intensity parameters.

The present application provides a method for retrieving the age of eucalyptus plantations based on time series satellite images, which belongs to the technical field of forest resource investigation and quantitative remote sensing of forestry. The method can more quickly and accurately obtain the age data of eucalyptus plantations, making up for the shortcomings of traditional methods for obtaining the age of eucalyptus plantations, such as time-consuming, labor-intensive, high cost, and long data acquisition period. The use of long time series remote sensing image vegetation index before and after eucalyptus felling for age retrieval can make up for the shortcomings of the relationship between age and remote sensing spectral reflection to establish an age retrieval model to retrieve the age, and realize the accurate extraction of time series age. By using a spatio-temporal fusion algorithm to spatio-temporally fuse high spatial resolution satellite remote sensing images and low spatial resolution satellite remote sensing images, the missing image data that cannot be used due to cloud and rain in a month is reconstructed, a complete time series image is constructed, and the age of eucalyptus plantations is accurately retrieved to the monthly scale.

The application discloses a plantation remote sensing image segmentation and recognition method and system, relates to the technical field of remote sensing image processing, and comprises the following steps: acquiring multi-temporal remote sensing images and laser radar point cloud data and registering, extracting stand height information and crown structure information, using the crown structure information to constrain and reconstruct spectral characteristics to obtain structure spectral coupling characteristics; calculating spectral differences and texture changes, combining stand height time series changes to construct stand state evolution characteristics; fusing to obtain fusion characteristics; constructing a double-channel semantic segmentation model to train and perform pixel-level joint classification to obtain an initial classification result; based on the crown structure information, a matching constraint rule is established to modify the initial classification result to obtain a final classification result, and high-precision recognition of degraded forest land tree species is realized.

The invention discloses a man-made forest ecological function monitoring method based on meteorology and hydrology, and relates to the technical field of forestry ecological monitoring, and the method comprises the steps: obtaining the soil penetration resistance time sequence data of different soil layers of a target man-made forest area, calculating the soil physical structure toughness index of the soil maintenance pore structure and the compaction resistance through combining with soil auxiliary parameters, and calculating the soil physical structure toughness index; analyzing the historical evolution trend; collecting short-term meteorological data and long-term climate change data of the target artificial forest area, coupling the short-term meteorological data and the long-term climate change data with the soil physical structure toughness index, and establishing a meteorological-hydrological-soil structure coupling process model; quantitatively evaluating the water source conservation capability, the soil erosion risk and the climate adaptability of the artificial forest area in the target artificial forest area, and simulating the future evolution trajectory of the soil physical structure and the ecological function of the target artificial forest area by taking different climate scenes and management intervention scenes in the future as compensation factors; meteorological and hydrological dynamic monitoring and early warning of the ecological function of the artificial forest are realized. According to the invention, the accuracy of ecological function evolution prediction is improved.