مقایسه رگرسیون وزن دار جغرافیایی و رگرسیون کریگینگ برای برآورد مکانی زی توده روی زمین جنگل های بلوط زاگرس

نقشه زی توده روی زمین جنگل برای مدیریت پایدار بوم سازگان های جنگلی و گزارش انتشار کربن، امری ضروری و اجتناب ناپذیر است. هدف از این مطالعه مقایسه رگرسیون وزن دار جغرافیایی با رگرسیون کریگینگ با مبنای رگرسیون خطی چندگانه برای برآورد زی توده روی زمین جنگل های بلوط زاگرس با استفاده از نمونه های میدانی، اطلاعات طیفی و شاخص های پوشش گیاهی استخراج شده از تصاویر لندست 8 است. به منظور جمع آوری اطلاعات، 32 بلوک با ابعاد 1000 متر در 1000 متر به طور سیستماتیک با فاصله 2 کیلومتر پیاده شد. سپس در هر بلوک تعداد 7 قطعه نمونه با ابعاد 30 متر در 30 متر روی یکی از قطرهای اصلی شبکه پیاده شد. در مجموع از 224 قطعه نمونه 30 متر در 30 متر، 184 قطعه نمونه در بخش های جنگلی قرار گرفته بود و تحلیل ها با استفاده از 184 نمونه اجرا شد. پس از محاسبه کربن اندوخته شده در هر قطعه نمونه، کارایی دو روش رگرسیون وزن دار جغرافیایی و رگرسیون کریگینگ برای برآورد و تهیه نقشه زی توده ارزیابی شد. نتایج نشان داد که رگرسیون وزن دار جغرافیایی (ضریب تبیین 0.61 و جذر میانگین مربعات خطای نسبیی نسبی 22) کارایی بهتری برای برآورد و تهیه نقشه زی توده روی زمین جنگل های بلوط نسبت به رگرسیون کریگینگ (ضریب تبیین 0.47 و جذر میانگین مربعات خطای نسبیی نسبی 28) دارد.

Comparison of Geographically Weighted Regression and Regression Kriging to Estimate the Spatial Distribution of Aboveground Biomass of Zagros Forests

Aboveground biomass (AGB) of forests is an essential component of the global carbon cycle. Mapping aboveground biomass is important for estimating CO2 emissions, and planning and monitoring of forests and ecosystem productivity. Remote sensing provides wide observations to monitor forest coverage, the Landsat 8 mission provides valuable opportunities for quantifying the distribution of aboveground biomass at moderate spatial resolution across the globe. The combination of the sample plots and image data has been widely used to map forest aboveground biomass at local, regional, national, and global scales. Many predictive methods have been suggested to estimate forest aboveground biomass from sparse sampling points into continuous surfaces, ranging from regression methods such as Geographically Weighted Regression (GWR) and geostatistical methods such as Regression Kriging (RK). Researchers have been particularly interested in understanding the causes and effects in ecosystem functions of spatial autocorrelation and heterogeneity, over the past decade. Where in forestry data include spatial autocorrelation and heterogeneity, the independence and homogeneity assumptions of standard statistical approaches, such as ordinary least squares (OLS), may be violated. Many spatial models (such as Geographically Weighted Regression and Regression Kriging) have been developed in recent years to discuss spatial effects in the relationships between variables. Spatial models can be divided into global and local models depending on the spatial scales used in the modeling process. A global model usually involves, a tool to model spatial autocorrelation between observations in neighboring locations, through either a covariance matrix that can be calculated using a variogram or spatial weight matrix based on neighborhood proximity. Global models, of course, do not well represent spatial differences at any given location and may not be successful in dealing with spatial heterogeneity. By comparison, local models, such as geographically weighted regression, adequate a regression relationship within a given bandwidth for each spatial location using the neighbors. From the relationships between variables, the local models are more useful in exploring locational spatial variation (heterogeneity). In the present study, using a Landsat 8OLI image, and Geographically Weighted Regression and Regression Kriging modeling were compared for the estimation of aboveground forest biomass. In this study, we gathered aboveground biomass data from a total of 184 (30 × 30 m) sample plots in Zagros forests in the Kohgiluyeh and BoyerAhmad Province. The datasets corresponded to the Landsat 8 image pixel values. We applied the speciesspecific allometric equations for individual trees to estimate forest aboveground biomass. The aboveground biomass at plotlevel is simply the summation for all trees within the same plot. The estimates were evaluated by tenfold crossvalidation and performances of the model was evaluated using the coefficient of determination (R2) and relative root mean squared error (RMSE%). The efficiency of the predictions can be described with the scatterplots showing the relationships between the forest aboveground biomass estimates and reference data. Results showed 1) that Geographically Weighted Regression (R2 = 0.61, RMSE%= 22) was a fairly better approach and could provide promising results for the prediction of forest aboveground biomass compared to Regression Kriging (R2 = 0.47, RMSE%= 28) and 2) scatterplots depicted that the problems of overestimation and underestimation for all the prediction were apparent.