370 results about "Land cover" patented technology

A method and device for evaluating ecological cumulative effects of surface mining areas are provided. The method includes: constructing a surface mining areas eco-environmental evaluation index (SMAEEI) suitable for semi-arid grasslands; constructing a first eco-environmental quality condition adjustment coefficient R(Si,j,th) based on the actual land cover classification result and the SMAEEI; obtaining the actual ecosystem service value ESV per unit area of the area to be evaluated in several years through the unit area ecosystem service value coefficient VCif of the area to be evaluated and the first eco-environmental quality condition adjustment coefficient R(Si,j,th); based on the time-series trajectory integral value of the actual ecosystem service value ESV per unit area and the ideal ecosystem service value ESV′ per unit area undisturbed by human beings, performing residual analysis to obtain the ecosystem service value accumulation induced by anthropogenic factors (ESVA-AF).

The invention discloses a remote sensing image multi-scale semantic segmentation method based on a coding and decoding network, and relates to the technical field of computer vision and remote sensing image processing, and the method comprises the following steps: S1, obtaining image data, carrying out the preprocessing of an image, carrying out the normalization of the image to a specified size, and carrying out the data enhancement operation, the data enhancement operation comprises random rotation, overturning and zooming. According to the remote sensing image multi-scale semantic segmentation method based on the coding and decoding network, ResNeXt5032x4d is introduced to serve as a backbone network, grouping convolution is combined, multi-scale features are effectively extracted, the accurate recognition capacity of the model for the land cover type is improved, and through the fusion strategy of the self-adaptive feature cooperation module AFCM, the remote sensing image multi-scale semantic segmentation method based on the coding and decoding network is obtained. According to the method, local and global context information is fused, the segmentation capability of the model on a large target is enhanced through a parallel multi-scale convolution layer and cavity convolution, the accuracy and integrity of a segmentation result are ensured, and a segmented region is smoother and more complete.

The invention relates to the technical field of remote sensing recognition, in particular to a remote sensing recognition method and system applied to ecological system investigation. The method comprises the following steps: acquiring multi-temporal remote sensing image data of a target area; extracting a land cover type of the multi-temporal remote sensing image data, and performing dominant human activity area identification on the multi-temporal remote sensing image data according to the land cover type to generate dominant human activity area data; acquiring night light data of the target area; performing space-time registration on the night light data of the target area and the multi-temporal remote sensing image data to generate fused night light remote sensing data; performing boundary region extraction on the multi-temporal remote sensing image data through the land cover type to obtain edge region data; and calculating a vegetation index and a noctilucence index of the marginal region data based on the fused noctilucence remote sensing data. According to the method, through multi-temporal and multi-source data fusion and multi-index time sequence analysis, the accuracy and comprehensiveness of ecological system investigation remote sensing recognition are improved.

The invention discloses a high-precision topographic mapping system and method based on multi-source remote sensing data, and relates to the technical field of topographic mapping, and the method comprises the steps: obtaining an optical remote sensing image, a radar remote sensing image, LiDAR point cloud data and meteorological index data of a target region, and carrying out the data preprocessing of the optical remote sensing image, the radar remote sensing image and the LiDAR point cloud data; obtaining the surface feature types of the target area and the coverage area of each surface feature type, and carrying out the weighted fusion of the optical remote sensing image and the radar remote sensing image of the target area according to the surface feature types of the target area and the coverage area of each surface feature type, and obtaining a self-adaptive fusion remote sensing image; according to the method, points in LiDAR point cloud data are divided into distinguished ground points and non-ground points, a digital elevation model is constructed according to the ground points in the LiDAR point cloud data, three-dimensional modeling of the terrain is performed on the basis of the digital elevation model in combination with texture information of a self-adaptive fusion remote sensing image, a three-dimensional terrain model is generated, and the terrain surveying and mapping precision is remarkably improved.

The invention relates to the technical field of environment management, in particular to a land coverage change monitoring method based on fusion of a remote sensing image and a geographic information system, which comprises the following steps: S1, data acquisition: acquiring multi-temporal remote sensing and auxiliary data; s2, data preprocessing: preprocessing the collected remote sensing and auxiliary data; s3, land feature classification and land cover change identification: classifying land cover types, and performing land cover change identification in combination with the spatial texture features of the remote sensing image data; and S4, result output and analysis: performing fusion and analysis on the terrain classification and land cover change identification result and the existing land cover data in the geographic information system, and evaluating the change trend of land utilization. According to the method, different types of land coverage can be identified carefully, and particularly, when complex remote sensing data is processed, the classification precision and the accuracy of change identification are remarkably improved.

The invention discloses an image multi-modal feature extraction method, a ground feature classification and recognition method and a GIS image generation method. Comprising the following steps: firstly, extracting texture features from a target image by adopting a multi-directional statistical method fusing rotation invariant coding of a local binary pattern and a gray-level co-occurrence matrix, and extracting color features from the target image by adopting an LAB-HSV dual-color space collaborative analysis method to obtain features of different modes of the target image; and then according to the information values of the extracted color features and texture features, adjusting the weight ratio corresponding to the color features and the texture features so as to optimize the recognition precision of the classification model on complex ground features. And finally, according to a weight ratio corresponding to the color feature and the texture feature, performing weighted fusion on the color feature and the texture feature to obtain a corresponding multi-modal feature vector.

The invention discloses a depth space-time super-resolution mapping method fusing historical high-resolution land coverage data, and the method comprises the steps: constructing a depth space-time super-resolution mapping model structure which is formed by connecting a super-resolution adversarial network SRGAN structure and a MUNet network structure in series; mapping from low-resolution images to high-resolution land coverage category probabilities in combination with super-resolution reconstruction and semantic segmentation is carried out, a historical high-resolution land coverage category map of a target area is used as auxiliary information, and changed categories and unchanged categories are distinguished by comparing category information of the low-resolution images in a historical period and a target period; and a change detection-based sub-pixel category allocation optimization model fusing a historical high-resolution land cover classification map and a predicted high-resolution category probability map is constructed, so that the depth space-time super-resolution mapping of the low-resolution remote sensing image is completed. The method has the advantages of high simulation precision, stability and the like, and is suitable for earth surface information extraction work such as classification, land cover / utilization, change detection and the like when remote sensing images are insufficient.

The embodiment of the invention provides an AI analysis method and system applied to land resource investigation, and the method comprises the steps: obtaining the multi-source remote sensing image data of a target region, analyzing the spectrum and texture features of the multi-source remote sensing image data, generating a comprehensive land feature map, obtaining an initial land classification map through a land classification model, and obtaining the initial land classification map. And performing time sequence comparative analysis in combination with historical data to determine a dynamic area of land coverage change and generate a land resource change report. The method further comprises the functions of soil quality evaluation, change area visualization, abnormal area real-time monitoring, land classification model training, classification model adjustment according to user feedback and the like, and the efficiency and accuracy of land resource investigation are improved.

The invention provides a natural resource dynamic monitoring method and system based on multi-source data fusion, a medium and equipment. The method comprises the following steps: acquiring satellite remote sensing data, aerial remote sensing data and ground monitoring data; performing data fusion on the satellite remote sensing data and the aerial remote sensing data to obtain fused data; integrating the fusion data with the ground monitoring data to obtain comprehensive monitoring data; performing terrain classification on the comprehensive monitoring data based on deep learning to obtain terrain classification results including cultivated land, construction land, water, forest land and grassland; and optimizing the ground feature classification result based on a space-time logic consistency algorithm to obtain an optimized classification result and ground feature change information. According to the method, through fusion of the multi-source data and classification optimization of the natural resources, in combination with deep learning and a spatial-temporal logic consistency algorithm, dynamic monitoring of the natural resources is realized, the monitoring precision is improved, and reliable data support is provided for natural resource management and environmental protection.

The invention relates to the technical field of ecological environment monitoring and geographic information, in particular to a coastal wetland soil sample point quality evaluation method and system based on multi-source environmental data. According to the method, remote sensing, climate, terrain, ocean and historical land coverage data are fused, and indexes are extracted and standardized; an environment similarity score (weighted Markov / neighbor weighted Euclidean), a historical consistency score (multi-temporal coverage + transfer penalty) and a source reliability score (metadata integrity + anomaly detection + NDVI-SOC residual) are constructed respectively; comprehensive scores are obtained through self-adaptive weight fusion, A / B / C grades are divided, and a report and a high-quality sample point set are output. The system comprises a data access module, a scoring engine module and a visualization module, and the precision and robustness of soil mapping and carbon storage evaluation can be remarkably improved.

The invention discloses a remote sensing image cloud removal method and system based on geographic coordinate embedding, and the system employs a geographic coordinate system embedding module to encode pixel longitude and latitude information into high-dimensional features, and achieves the joint modeling of an absolute position and a relative space relation through spherical coordinate mapping and a rotation matrix. And in combination with a multi-head self-attention mechanism and residual connection, the position sensitivity of the feature space is enhanced. Meanwhile, by using a multi-level feature extraction network and a cloud distortion compensation module, through a residual block and convolution operation, cloud interference features are extracted, a position sensing correction vector is generated, nonlinear offset of a cloud coverage area is corrected, and earth surface textures are recovered. According to the method, the adaptability of a cross-regional scene is remarkably improved, texture distortion and information loss of a large-scale cloud region are effectively reduced, the method has the advantages of high precision and high robustness in the fields of remote sensing image preprocessing, agricultural monitoring, land cover change analysis and the like, and a new scheme is provided for high-quality application of optical remote sensing data.

The invention discloses a multi-temporal remote sensing image automatic change detection method based on deep learning, and the method comprises the following steps: collecting remote sensing image data obtained in different periods in a target region, carrying out the standardization processing of the remote sensing image data, and obtaining a standardized multi-temporal remote sensing image data set; based on the standardized multi-temporal remote sensing image data set, carrying out ground feature category segmentation by adopting a semantic segmentation network to obtain a ground feature semantic priori graph; inputting the standardized multi-temporal remote sensing image data set into the improved U-shaped twin network, and outputting a change detection probability graph and a binary change mask; based on the change detection probability graph and the binary change mask, attribute discrimination is carried out on the change area in combination with the ground feature semantic prior graph, and a change area result is output; and integrating the change area result to a geographic information service platform. The improved U-shaped twin network is adopted, and automatic change detection of the multi-temporal remote sensing image is achieved.

The disclosure provides an improved canopy height correction method. The method includes: obtaining GEDI LiDAR data, airborne canopy height data, GDEM with high resolution and land cover product within the selected target area and timeframe; performing quality filtering and spatial-scale filtering on GEDI footprints; extracting laser pointing parameters and waveform parameters; extracting reference canopy height from airborne data for each footprint; extracting laser pointing parameters and waveform parameters; preprocessing the GDEM and calculating topographic parameters, including topographic variability index (TVI); constructing the Laser Pointing and Topographic Index (LPTI) according to the 3D forest-ground geometry model; inputting the waveform parameters, even topographic parameters, TVI and LPTI as independent variables, and the reference canopy height as the dependent variable to modeling an improved forest canopy height extraction, and utilizing the improved canopy height extraction model to correct the twofold influence of topographic on GEDI canopy height extraction.

The invention provides a method for improving flood inundation simulation precision based on fusion of VGI data and optimization of DEM, and the method comprises the steps: selecting HEC-RAS as a flood inundation simulation model, and inputting data including DEM data, river flow data, precipitation data and land coverage type data; according to a Manning coefficient range table provided by the land coverage database, setting corresponding Manning coefficients for different coverage types, and preliminarily setting the Manning coefficients as the median of each coverage type; the Manning coefficient setting is iteratively adjusted by comparing the difference between the simulation result and the actual water depth data; establishing a relation chain between the extracted water depth information and the submerged simulated water depth; height measurement data of a land observation satellite is combined with the COPDEM to estimate a residual error between the COPDEM at a water depth point position and a real elevation, and a conversion relation is established between extracted water depth and simulated water depth of a relatively optimized DEM according to an elevation difference value between the DEM and the COPDEM. Based on the technical scheme of the invention, the simulation precision is obviously improved, the VGI data is successfully combined with the optimized DEM, and the accurate correction of the Manning coefficient is realized.

The invention discloses a large-scale forest land carbon reserve estimation method and system based on an active and passive remote sensing technology. The method comprises the following steps: acquiring satellite-borne laser radar data of different sensors in a research area, SAR data of an L wave band and a C wave band, multispectral remote sensing image data, airborne laser radar data, topographic data and land coverage category data containing forest land; discontinuous combined spaceborne laser radar canopy height products are obtained by using spaceborne laser radar data of different sensors; improving a geographic weighted regression model to estimate the canopy height of a continuous scale; extracting polarization parameters by using C-band SAR data, extracting vegetation indexes and texture indexes by using multispectral remote sensing image data, constructing characteristic variables together with topographic data, and screening the characteristic variables based on a variance reduction criterion; and constructing an overground carbon reserve inversion model, and carrying out large-scale forest land carbon reserve estimation. According to the method, the forest land carbon reserve estimation precision of the large-scale regional broken plot is improved.

The invention discloses a forest degeneration degree, degeneration type and degeneration process identification method, and relates to the field of forest resource monitoring and ecological environment evaluation.The method comprises the steps that multi-source data obtained in a research area is preprocessed, and the preprocessed multi-source data is determined; selecting multi-dimensional indexes from three aspects of forest degradation structure, composition and function, and constructing a multi-dimensional index system according to the preprocessed multi-source data; the multi-dimensional indexes comprise forest coverage rate, crushing degree, tree variety diversity, aboveground biomass and net primary productivity; generating a forest degradation index according to each index in the multi-dimensional index system; determining a forest degeneration degree according to the forest degeneration index, generating a forest degeneration type graph in combination with land coverage data to reveal degeneration differences of different forest types, analyzing a dynamic change track of a forest degeneration region in combination with a normalized combustion index time sequence, and identifying a forest degeneration process; according to the method, the multi-dimensional characteristics of degradation can be comprehensively revealed.

The invention discloses a regional thermal environment analysis implementation method and device and a storage medium, and relates to the technical field of thermal environment analysis. According to the regional thermal environment analysis implementation method, the change trends corresponding to all adjacent positions of different land cover types in remote sensing data can be mined by lengthening the time dimension, and the weight parameter corresponding to each land cover is determined by combining all the change trends; the weight parameter can measure the influence degree of the earth surface coverage on the thermal environment of the target area. And finally, analyzing the thermal environment corresponding to the target area according to all the land surface coverage and the weight parameter corresponding to each land surface coverage. Therefore, the effective content of the remote sensing data can be fully mined, so that the accuracy and reliability of regional thermal environment analysis are improved.

The invention discloses a drought disaster risk quantitative assessment method fusing an irrigation disaster reduction effect, and belongs to the field of disaster risk assessment, and the method comprises the steps: obtaining different meteorological data of a target region in a three-dimensional space grid scale, and calculating a drought index; a drought event is identified by using an ST-KMeans clustering method; drought events are screened and combined, the space overlapping degree of the clustered drought region and the historical disaster region is calculated, and risk loss data docking is carried out; generating a drought event characteristic dynamic data set, and measuring the drought event; and in combination with the land coverage and utilization data of the target area and the farmland irrigation area data, extracting irrigation response information serving as a drought adaptability measure, and carrying out irrigation disaster reduction capability quantification and risk assessment. According to the method, irrigation information can be integrated on the basis of drought event identification, the drought slow release effect of irrigation in different time and space scales is quantitatively evaluated, and a scientific basis is provided for drought disaster risk management and agricultural adaptability policies.

The invention relates to a remote sensing image semantic change detection method based on difference feature guidance, and the method comprises the steps: obtaining difference features through employing a pixel-level subtraction method, employing a guidance segmentation branch to only focus on the segmentation of a change region, finally generating an accurate land coverage map, and guiding a change branch to generate a clear edge of the change region. According to the method, semantic segmentation is more accurate, and the edge of a change region is clearer.

The invention provides a method for improving urban green land biological source emission based on a high-resolution global mode, and provides a technical scheme for optimizing a biological source emission list by using high-resolution land coverage data aiming at the problem of urban green land emission deficiency in a current numerical model. The technologies comprise a weak supervision global land coverage mapping method, spectral information of images can be fused, and features extracted by a visual large model are used for drawing high-resolution land coverage images; the vegetation function type matching method is used for converting the high-resolution land coverage data into vegetation function types; and preparing a high-resolution numerical mode input file, converting the file into an input format suitable for a high-resolution earth system mode, carrying out numerical simulation, and estimating global biological source emission, especially urban green space emission.

The invention belongs to the technical field of soil improvement, and particularly provides a photovoltaic power generation field soil improvement method suitable for a high and cold gobi area. According to the method, the base material is laid on the pretreated soil firstly, then corresponding plant combinations are selected for mixed sowing according to habitat types of different areas, and finally regular irrigation and maintenance are performed, so that the water and fertilizer retention performance of the soil of the photovoltaic power generation field in the alpine and dry gobi desert area of the Tibet Plateau can be effectively improved; through comprehensive application of biological blankets, organic matter addition, microbial agent regulation and land covering technologies, the water and fertilizer retention performance of soil is improved, soil improvement and ecological restoration are promoted, and collaborative development of a power generation system and the ecological environment is achieved.

The invention discloses a land coverage classification method based on inference segmentation, and relates to the technical field of remote sensing image inference segmentation and deep learning. Comprising the steps of training sample set establishment, multi-scale feature extraction submodule design, cross-modal feature fusion module design, land coverage classification model structure design based on reasoning segmentation, land coverage classification model training based on reasoning segmentation, model performance evaluation and index analysis. According to the method, effective combination of remote sensing image features and semantic information is realized, and the differentiated cognitive ability for different ground object targets is improved. Meanwhile, a remote sensing image land cover intelligent classification technology with practical value is obtained, the professional technical threshold is remarkably reduced, the intelligence and universality of remote sensing image interpretation are realized, and a convenient remote sensing information acquisition method is provided for users in various fields.

The invention provides a multi-label remote sensing scene classification method and system based on a state space model, and belongs to the technical field of image label classification. The linear multi-scale interaction enhancement model enhances the representation of visual features by mining long-distance context information of different scales. Therefore, the model can fully explore land coverage targets of various scales from the remote sensing image. The linear semantic relationship establishment model aims at establishing semantic association among different labels through guidance of visual information. The method can improve the ability of an efficient network based on a state space model to understand the complex content of a remote sensing image by revealing the semantic relationship between different types of land coverage. Through cooperation between the two modules, local information, global context, multi-scale clues and semantic association can be learned from a remote sensing image, which is very useful for a multi-label remote sensing scene classification task.

The invention relates to an edge-guided cross-modal interactive fusion land cover classification method, which is particularly suitable for collaborative semantic segmentation of an optical image and a synthetic aperture radar (SAR) image. The method comprises the following steps: constructing a pseudo twin multi-stream encoder to respectively extract multi-scale features of optical and SAR images, embedding a cross-modal adaptive feature interaction module between encoding stages, realizing dynamic alignment and re-calibration of features between modals through a channel-level and space-level interaction mechanism, and relieving fusion deviation caused by heterogeneity and spatial dislocation. Neuron-level attention and multi-scale depth separable convolution are further fused through a lightweight feature fusion module, noise is suppressed, and context information is aggregated. And meanwhile, an edge auxiliary module is introduced to extract multi-scale boundary information from low-level features, and the multi-scale boundary information and semantic features are jointly decoded, so that the boundary detail and small target recognition capability is improved. According to the method, the precision and robustness of land coverage segmentation in a complex scene are remarkably improved on the premise of ensuring the calculation efficiency.

The invention relates to the technical field of geographic information measurement, in particular to a landform surveying method and system for territorial space planning, and the method comprises the steps: 1, carrying out the fusion preprocessing of multi-source data; 2, constructing a planning constraint dynamic rule base; 3, space-time constraint coupling analysis: taking the digital elevation model as a reference, and correcting the space offset of the land cover map through a thin-plate spline function; comparing the planning threshold value with the current landform parameter in real time, and marking a conflict coordinate set of gradient overrun or settlement overspeed; 4, intelligently segmenting the landform units; and 5, planning suitability output: setting a dynamic buffer distance according to the fault activity grade and the rock-soil body type, and generating an early warning layer when a town unit exists in a buffer area. According to the method, remote sensing images, laser radar point clouds and geological survey data are fused, space-time dislocation among various data sources is eliminated, and the precision of space analysis is improved.

The invention provides a coastal zone land coverage type classification method based on a random forest and a topology rule, and belongs to the technical field of coastal zone land coverage type classification. The method comprises the following steps: based on a GEE cloud platform, integrating a multispectral remote sensing image and elevation data, extracting spectral features and geographic features, and realizing pixel-level classification of random forest machine learning; generating a clustering unit through a simple non-iterative clustering method, and performing object-oriented special classification on the water body and the aquaculture pond in combination with spectral and morphological characteristics of the clustering unit; spatial filtering is introduced to remove salt and pepper noise, a coastal zone ground feature topology rule base is established to carry out spatial logic verification, and coastal zone land coverage classification with high precision and reasonable spatial layout is realized. According to the method, the multi-source remote sensing data and the multi-level classification strategy are fused, the problems of coastal zone ground feature spectrum confusion, boundary fuzziness and space logic inconformity are effectively solved, the classification precision and practicability are remarkably improved, and the method is suitable for land coverage monitoring and management of the complex coastal zone environment.

The invention discloses a farmland protection forest area windproof effect evaluation method based on a land-gas coupling model, and relates to the technical field of agricultural science, high-precision topographic data of a target farmland protection forest area is collected and preprocessed, and spatial distribution information of a farmland protection forest is obtained by combining remote sensing image interpretation; on the basis of topographic data and spatial distribution information, topographic relief and earth surface coverage features are analyzed, and a refined underlying surface classification system is constructed; and configuring high-resolution grid parameters of a WRF mode, and coupling a Noah-MP land surface process model. According to the farmland protection forest area wind-proof effect evaluation method based on the land-gas coupling model, through combination of high-resolution topographic data and a multi-source remote sensing image, topographic relief and surface heterogeneity of a farmland protection forest area can be accurately depicted, and a WRF mode is coupled with a Noah-MP land surface process model; fine simulation of complex terrains and various underlying surfaces is realized, the limitation of traditional uniform underlying surface hypothesis is overcome, and the accuracy of windproof effect evaluation is remarkably improved.

The invention belongs to the technical field of remote sensing image processing, computer vision and deep learning, and particularly relates to a remote sensing image erosion gully semantic segmentation method based on improved OfficientNet-UNet. Comprising the following steps of 1, data preparation and data preprocessing; 2, constructing and enhancing a data set; step 3, model construction and strategy training; and 4, performing contrast experiment and result evaluation. According to the method, detail features of ground features can be more accurately captured, an overfitting phenomenon caused by too high model complexity is reduced, so that the classification precision and boundary recognition accuracy of land coverage data are effectively improved, weights of different types of samples can be automatically adjusted in the training process, and the training efficiency is improved. Particularly, the contribution of background pixels to a loss function is reduced, so that the problem of dominant training of the background pixels is effectively relieved; the method has good expansibility.

The invention discloses a land cover classification model for multi-scale remote sensing images. The land cover classification model comprises a ViT encoder, a low-rank fine tuning module, a CNN encoder, a decoder, a scale encoder and a position encoder. A multi-resolution remote sensing image is processed by a ViT encoder block coding layer and then enters a Transform layer, interactive fusion is carried out on the multi-resolution remote sensing image and CNN features processed by a CNN encoder, fused visual features, scale codes and position codes are fused through an attention mechanism and then enter a decoder, the decoder obtains the category probability corresponding to each pixel through multi-step operation, and the category probability corresponding to each pixel is calculated through the corresponding category probability. And finally, determining a ground feature category through the semantic segmentation head. According to the method, single-model cross-resolution automatic interpretation is realized, and multi-element ground feature classification of remote sensing images with the resolution of 2-10m is supported. A visual pre-training large model is introduced to improve the interpretation stability; global context and local detail information are considered through ViT and CNN double branches; and semantic alignment up-sampling is introduced in the decoding process, so that the segmentation accuracy is improved.

The invention provides a global biodiversity hot spot area threat condition remote sensing monitoring method and system, and relates to the technical field of ecological environment monitoring, spatial analysis and remote sensing and information processing, and the method comprises the steps: obtaining multi-temporal global land coverage data and auxiliary GIS data of a biodiversity hot spot area, and carrying out the preprocessing; extracting forest coverage layers of a reference period and a monitoring period from global land coverage data, and extracting pixels converted from a forest to a non-forest through a layer difference method to generate a forest loss layer; based on the forest loss map layer and the preprocessed auxiliary GIS data, calculating various ecological pressure indexes including a forest loss rate, a protection failure rate and a population pressure index, and performing normalization; and calculating a comprehensive threat index based on each ecological pressure index, and determining an ecological threat level. According to the method, automatic identification, quantitative evaluation and grading of the ecological threats of the hot spot region are realized, and technical support is provided for global ecological protection planning and management.