US-20260127334-A1 - Automatic and Accurate Estimation Method for Gross Primary Productivity of Forest Ecosystem Based on Remote Sensing Technology

US20260127334A1US 20260127334 A1US20260127334 A1US 20260127334A1US-20260127334-A1

Abstract

The automatic and accurate estimation method for gross primary productivity of a forest ecosystem based on a remote sensing technology includes: the real-time position signals of a remote sensing collection device is acquired, the forest map is acquired and the real-time position signals is displayed on the map; the meteorological parameters and forest characteristic parameters of the forest are collected by a remote sensing collection device, and the preliminary estimation on the GPP of the forest ecosystem is performed by a ground estimation model; the geomorphological features of the map are collected, it is judged whether to correct the preliminary estimation result according to the geomorphological features, and if the correction is needed, the preliminary estimation result is adjusted; and the adjusted estimation result is stored, and the dynamic fluctuation chart of the adjusted estimation result is displayed on a visual device.

Inventors

Huai Yang
Li Ding
XiangHua Yue
Junhao QIU
Xin Tan
Shirong Liu
Biao HUANG
JiaLin Fu
Shuangjia Fu
Kai BIAN
JunWei Luan
Daochun Qin
ChunJu Cai

Assignees

Sanya Research Base, International Center for Bamboo and Rattan

Dates

Publication Date: 20260507
Application Date: 20250423
Priority Date: 20241105

Claims (10)

1 . An automatic and accurate estimation method for gross primary productivity of a forest ecosystem based on a remote sensing technology, comprising: acquiring real-time position signals of a remote sensing collection device, acquiring a forest map to be estimated from a geographic information database, and displaying the real-time position signals on the map; collecting meteorological parameters and forest characteristic parameters of a forest to be estimated by a remote sensing collection device, and performing a preliminary estimation on gross primary productivity of a forest ecosystem by a ground estimation model according to the meteorological parameters and the forest characteristic parameters, wherein the meteorological parameters comprise daily temperature, a daily precipitation and daily sunshine duration; and the forest characteristic parameters comprise a normalized difference vegetation index, a leaf area index and plant coverage density; collecting geomorphological features of the map, judging whether to correct a preliminary estimation result according to the geomorphological features, and if the correction is needed, adjusting the preliminary estimation result according to the geomorphologic features, wherein the geomorphologic features comprise topographic slope, a topographic aspect, an altitude, concavity-convexity and a vegetation height; and storing an adjusted estimation result, and displaying a dynamic fluctuation chart of the adjusted estimation result on a visual device.
2 . The automatic and accurate estimation method for gross primary productivity of a forest ecosystem based on a remote sensing technology according to claim 1 , wherein the step of acquiring real-time position signals of a remote sensing collection device, acquiring a forest map to be estimated from a geographic information database, and displaying the real-time position signals on the map comprises: configuring a position module on the remote sensing collection device, communicating the position module with the ground estimation model, and acquiring position signals of the position module; dividing the forest map to be estimated evenly into a plurality of unit grids with equal area based on coordinates, matching the position signals with the divided forest map, and assigning a unique number or identification to each unit grid; receiving and analyzing the position signals from the remote sensing collection device through the ground estimation model, and extracting real-time longitude information, latitude information and altitude information; matching the received position signals with a grid unit divided on the map by using a coordinate conversion and matching algorithm, and determining grid units where the position signals are located according to longitude and latitude of the position signals; and updating a position of the remote sensing collection device dynamically on the map, marking the grid unit where the remote sensing collection device is located, and displaying a current coordinate, a current altitude and the correspond grid unit number of the device on the map.
3 . The automatic and accurate estimation method for gross primary productivity of a forest ecosystem based on a remote sensing technology according to claim 2 , wherein the step of collecting meteorological parameters and forest characteristic parameters of a forest to be estimated by a remote sensing collection device, and performing a preliminary estimation on the gross primary productivity of a forest ecosystem by a ground estimation model according to the meteorological parameters and the forest characteristic parameters comprises: estimating the gross primary productivity of the forest ecosystem preliminarily by a following equation: GPP = ( S × D × 0.24 ) × ( α × NDVI + β ) × ϵmax × { 1 ⁢ / [ 1 + e - k ⁡ ( T - Topt ) ] } × [ P / ( P + P ⁢ 0 ) ] × [ LAI / ( 1 + LAI ) ] × CD ; ϵmax = ϵ ⁢ c × ϵ ⁢ a × ϵ ⁢ b ; in the above equation, GPP is a preliminary estimated gross primary productivity of the forest ecosystem, unit: gCm 2 d −1 ; S is a solar radiation constant, S=1361W/m 2 ; D is the daily sunshine duration, unit: hour; 0.24=1/2*0.48, wherein 1/2 represents 50% of the total solar radiation converted to photosynthetically active radiation, and 0.48 represents a coefficient for converting W/m 2 to MJ/m 2 ; α and β are empirical coefficients, α=1.2, β=−0.1; NDVI is the normalized difference vegetation index; ϵmax is a maximum value of light energy utilization efficiency, ϵc is maximum light energy utilization efficiency under astronomical conditions, with a value range of [1.5, 2.5], unit: gCMJ −1 ; ϵa is a correction coefficient of the utilization efficiency under the daily temperature and daily moisture, with a value range of [0.2, 1.0]; ϵb is a correction coefficient under vegetation conditions, with a value range of [0.5, 1.0]; T is the daily temperature, unit: degree Celsius; Topt is optimal temperature for photosynthesis, with Topt=25 degrees Celsius; k is a temperature sensitivity coefficient, k=0.05; P is the daily precipitation, unit: millimeter; P0 is the water saturation constant, with a value range of [5, 10], unit: millimeter; LAI is the leaf area index; CD is the plant coverage density.
4 . The automatic and accurate estimation method for gross primary productivity of a forest ecosystem based on a remote sensing technology according to claim 3 , wherein the step of judging whether to correct a preliminary estimation result according to the geomorphological features comprises: judging whether to correct the preliminary estimation result according to the topographic slope, the topographic aspect, the altitude, the concavity-convexity and the vegetation height in turn; and correcting the preliminary estimation result according to the topographic slope, the topographic aspect, the altitude, the concavity-convexity and the vegetation height in turn according to a judgment result.
5 . The automatic and accurate estimation method for gross primary productivity of a forest ecosystem based on a remote sensing technology according to claim 4 , wherein the step of judging whether to correct a preliminary estimation result according to the geomorphological features, and if the correction is needed, adjusting the preliminary estimation result according to the geomorphologic features also comprises: if the topographic slope is less than or equal to 5 degrees, the preliminary estimation result is not adjusted, and the preliminary estimation result is taken as a first estimation value GPP1; if the topographic slope is greater than 5 degrees, the preliminary estimation result is adjusted, and the adjusted value is taken as a first estimation value GPP1; the equation for correcting the preliminary estimation result according to the topographic slope is as follows: GPP ⁢ 1 = GPP × ( 1 + CF ⁢ 1 ) ; in the above equation, GPP1 is a value after a primary correction of the preliminary estimated gross primary productivity of the forest ecosystem, i.e., a first estimation value; GPP is the preliminary estimated the gross primary productivity of the forest ecosystem; CF1 is a correction coefficient determined according to a slope type, wherein a value range of CF1 is [0, 0.3]; if the topographic slope is greater than or equal to 5 degrees and less than 15 degrees, the preliminary estimation result is adjusted, and a value range of CF1 is [0.05, 0.15], and for every 1 degree increase in slope, CF1 increases by 0.01; and if the topographic slope is greater than or equal to 15 degrees, the preliminary estimation result is adjusted, and a value range of CF1 is (0.15, 0.3], and for every 1 degree increase in slope, CF1 increases by 0.03.
6 . The automatic and accurate estimation method for gross primary productivity of a forest ecosystem based on a remote sensing technology according to claim 5 , wherein the step of judging whether to correct a preliminary estimation result according to the geomorphological features, and if the correction is needed, adjusting the preliminary estimation result according to the geomorphologic features also comprises: adjusting the first estimation value GPP1 according to the topographic aspect to obtain a second estimation value GPP2, and a calculation equation of the second estimation value is as follows: south ⁢ aspect : GPP ⁢ 2 = GPP ⁢ 1 × ( 1 + CFsouth ) ; north ⁢ aspect : GPP ⁢ 2 = GPP ⁢ 1 × ( 1 + CFnorth ) ; east ⁢ aspect : GPP ⁢ 2 = GPP ⁢ 1 × ( 1 + CFeast ) ; west ⁢ aspect : GPP ⁢ 2 = GPP ⁢ 1 × ( 1 + CFwest ) ; in the above equation, GPP2 is a value after a secondary correction of the preliminary estimated gross primary productivity of the forest ecosystem, i.e., a second estimation value; GPP1 is the first estimation value; CFsouth is a correction coefficient of the south aspect, with a value range of [0.10, 0.15]; CFnorth is a correction coefficient of the north aspect, with a value range of [−0.10,−0.15]; CFeast is a correction coefficient of the east aspect, with a value range of [0.05, 0.10]; and CFwest is a correction coefficient of the west aspect, with a value range of [0.05, 0.10]; if the topographic aspect is between two directions, taking average of values after the secondary correction of the preliminary estimated gross primary productivity of the forest ecosystem at the two directions as a second estimation value GPP2; and if there are multiple topographic aspects in a topographic area, taking average of the values after the secondary correction of the preliminary estimated gross primary productivity of the forest ecosystem at several aspects as a second estimation value GPP2.
7 . The automatic and accurate estimation method for gross primary productivity of a forest ecosystem based on a remote sensing technology according to claim 6 , wherein the step of judging whether to correct a preliminary estimation result according to the geomorphological features, and if the correction is needed, adjusting the preliminary estimation result according to the geomorphologic features also comprises: presetting a first preset altitude A1 and a second preset altitude A2, and the first preset altitude A1 is lower than the second preset altitude A2; if the altitude is less than the first preset altitude A1, the second estimation value GPP2 is not adjusted, and the second estimation value GPP2 is taken as a third estimation value GPP3; if the altitude is greater than or equal to the first preset altitude A1 and less than or equal to the second preset altitude A2, the second estimation value GPP2 is corrected by a first adjustment coefficient to obtain a third estimation value GPP3; and if the altitude is greater than or equal to the second preset altitude A1, the second estimation value GPP2 is corrected by a second adjustment coefficient to obtain a third estimation value GPP3; wherein the first adjustment coefficient is smaller than the second adjustment coefficient.
8 . The automatic and accurate estimation method for gross primary productivity of a forest ecosystem based on a remote sensing technology according to claim 7 , wherein the step of judging whether to correct a preliminary estimation result according to the geomorphological features, and if the correction is needed, adjusting the preliminary estimation result according to the geomorphologic features also comprises: presetting first preset concavity-convexity B1 and second preset concavity-convexity B2, and the first preset concavity-convexity B1 is smaller than the second preset concavity-convexity B2; if the concavity-convexity is smaller than the first preset concavity-convexity B1, the third estimation value GPP3 is not adjusted, and the third estimation value GPP3 is taken as a fourth estimation value GPP4; if the concavity-convexity is greater than or equal to the first preset concavity-convexity B1 and smaller than or equal to the second preset concave-concave B2, the third estimation value GPP3 is corrected by a first adjustment coefficient to obtain a fourth estimation value GPP4; and if the concavity-convexity is greater than or equal to the second preset concavity-convexity B2, the third estimation value is corrected by a second adjustment coefficient to obtain a value after four corrections to obtain a fourth estimation value GPP4; wherein the first adjustment coefficient is smaller than the second adjustment coefficient.
9 . The automatic and accurate estimation method for gross primary productivity of a forest ecosystem based on a remote sensing technology according to claim 8 , wherein the step of judging whether to correct a preliminary estimation result according to the geomorphological features, and if the correction is needed, adjusting the preliminary estimation result according to the geomorphologic features also comprises: presetting a first vegetation height value C1, if the vegetation height is lower than or equal to the first vegetation height value C1, the fourth estimation value GPP4 is not adjusted, and the fourth estimation value GPP4 is taken as final gross primary productivity; and if the vegetation height is higher than the first vegetation height value C1, the fourth estimation value GPP4 is adjusted, and the adjusted fourth estimation value GPP4 is taken as final gross primary productivity.
10 . The automatic and accurate estimation method for gross primary productivity of a forest ecosystem based on a remote sensing technology according to claim 9 , wherein the step of if the vegetation height is higher than the first vegetation height value C1, the fourth estimation value GPP4 is adjusted, and the adjusted fourth estimation value GPP4 is taken as final gross primary productivity comprises: adjusting the fourth estimation value GPP4 according to the vegetation height, and the adjusted fourth estimation value GPP4 is taken as final gross primary productivity, which is calculated by the following equation: GPP ⁢ 5 = GPP ⁢ 4 × ( 1 + CF ⁢ 3 ) ; in the above equation, GPP5 is a value after five corrections of the preliminary estimated gross primary productivity of the forest ecosystem, i.e., a fifth estimation value; GPP4 is the fourth estimation value; CF3 is a correction coefficient determined according to the vegetation height, wherein a value range of CF5 is [−0.2, 0]; presetting a second vegetation height value C2; if the vegetation height is less than or equal to the first vegetation height value C1, the fourth estimation value GPP4 is not adjusted, that is, CF5=0; if the vegetation height is greater than the first vegetation height value C1 and less than or equal to the second vegetation height value C2, the fourth estimation value GPP4 is adjusted, and a value range of CF3 is [−0.05,−0.10]; and if the vegetation height is greater than the second vegetation height value C2, the fourth estimation value GPP4 is adjusted, and a value range of CF3 is (−0.10,−0.20].

Description

CROSS-REFERENCE TO RELATED APPLICATION This patent application claims the benefit and priority of Chinese Patent Application No. 202411568894.4, entitled “AUTOMATIC AND ACCURATE ESTIMATION METHOD FOR GROSS PRIMARY PRODUCTIVITY OF FOREST ECOSYSTEM BASED ON REMOTE SENSING TECHNOLOGY” filed with the China National Intellectual Property Administration on Nov. 5, 2024, the disclosure of which is incorporated by reference herein in its entirety as part of the present application. TECHNICAL FIELD The present disclosure relates to the field of forest gross primary productivity estimation technology, in particular to an automatic and accurate estimation method for gross primary productivity of a forest ecosystem based on a remote sensing technology. BACKGROUND The remote sensing technology is a method to obtain information about an object or environment through detection devices (such as satellites, unmanned aerial vehicles, aircraft, etc.) without direct contact with the object being measured. This technology is mainly used to observe and analyze various natural phenomena on the earth's surface and in its atmosphere. The remote sensing technology is widely used, including environmental monitoring, resource investigation, disaster assessment, climate research and other fields. The Gross Primary Productivity (GPP) of the forest ecosystem refers to the total amount of carbon fixed by the forest ecosystem through photosynthesis in a certain period of time. It represents the process by which plants absorb carbon dioxide (CO2) from the atmosphere and convert it into organic matter, and is a key link in the carbon cycle of the forest ecosystem. The prior art often rely on ground surveys and traditional monitoring methods, which are affected by human interference, limited sample size and uneven geographical coverage, resulting in greater uncertainty in the estimation results. Traditional methods need to invest a lot of manpower, material resources and time to collect data and estimate data, which make the estimation process cumbersome and time-consuming, and it is difficult to meet the estimation requirements of fast, efficient and accurate. Therefore, it is necessary to provide an automatic and accurate estimation method for GPP of a forest ecosystem based on a remote sensing technology, so as to solve the problem that the estimation for the GPP of the forest ecosystem is not efficient and accurate in the prior art. SUMMARY In view of this, the present disclosure provides an automatic and accurate estimation method for GPP of a forest ecosystem based on a remote sensing technology, aiming to solve the problems of inefficient, inaccurate, and costly estimation for the GPP of the forest ecosystem. The present disclosure provides an automatic and accurate estimation method for GPP of a forest ecosystem based on a remote sensing technology, including: acquiring the real-time position signals of a remote sensing collection device, acquiring the forest map to be estimated from a geographic information database, and displaying the real-time position signals on the map;collecting the meteorological parameters and the forest characteristic parameters of the forest to be estimated by a remote sensing collection device, and performing a preliminary estimation on the GPP of the forest ecosystem by a ground estimation model according to the meteorological parameters and the forest characteristic parameters, wherein the meteorological parameters include the daily temperature, the daily precipitation and the daily sunshine duration; and the forest characteristic parameters include the normalized difference vegetation index, the leaf area index and the plant coverage density;collecting the geomorphological features of the map, judging whether to correct the preliminary estimation result according to the geomorphological features, and if the correction is needed, adjusting the preliminary estimation result according to the geomorphologic features, wherein the geomorphologic features include the topographic slope, the topographic aspect, the altitude, the concavity-convexity and the vegetation height; andstoring the adjusted estimation result, and displaying a dynamic fluctuation chart of the adjusted estimation result on a visual device. Furthermore, the step of acquiring the real-time position signals of a remote sensing collection device, acquiring a forest map to be estimated from a geographic information database, and displaying the real-time position signals on the map includes: configuring a position module on the remote sensing collection device, communicating the position module with the ground estimation model, and acquiring the position signals of the position module;dividing the forest map to be estimated evenly into a plurality of unit grids with equal area based on coordinates, matching the position signals with the divided forest map, and assigning the unique number or identification to each unit grid;receiving and analyzing