Sensitivity of landscape pattern indices to input data characteristics on real landscapes: implications for their use in natural disturbance emulation

Resource management strategies have begun to adopt natural landscape disturbance emulation as a means of minimizing risk to ecosystem integrity. Detailed understanding of the disturbance regime and the associated spatial landscape patterns are required to provide a natural baseline for comparison with the results of emulation strategies. Landscape pattern indices provide a useful tool to quantify spatial pattern for developing these strategies and evaluating their success. Despite an abundance of indices and tools to calculate these, practical knowledge of interpretation is rare. Quantifying changes in landscape pattern indices and the meaning of these changes is confounded by index sensitivity to input data characteristics such as spatial extent, spatial resolution, and thematic resolution. Sensitivity has been examined for simulated landscapes but rarely using real data for large areas as real landscapes are more difficult to manipulate systematically than simulated data. While simulated data offer a control, they do not provide an accurate portrayal of reality for practical applications. Our goal was to test the sensitivity of a suite of landscape pattern indices useful for disturbance emulation strategy development and evaluation to spatial extent, spatial resolution, and thematic resolution using current land cover data for a case study of the managed forest of Ontario, Canada. We also examined how sensitivity varies spatially across the study area. We used Landsat TM-based land cover data (> 45.5 million ha), controlling spatial extent (2,500 to 2,560,000 ha), spatial resolution (1 to 16 ha), and thematic resolution (2 to 26 classes). For each index we tested a hypothesis of insensitivity to changes in each input data characteristic using a combination of ANOVA and regression and compared our results with previous studies. Of the 18 indices studied, significant (p< 0.01) effects were found for 17 indices with changes in spatial extent, 13 indices with changes in spatial resolution and 18 indices with changes in thematic resolution. A significant (p < 0.01) linear trend accounted for the majority of the variance for all of the significant relationships identified. Most of the mean index responses were consistent with those interpreted from previous studies of simulated and real landscapes; however, sensitivity varied greatly among indices and over space. We suggest that variation in sensitivity to input data characteristics among indices and over space must be explicitly incorporated in the design of future natural disturbance emulation efforts.This revised version was published online in May 2005 with corrections to the Cover Date.     

On-line monitoring of cellulase treatment by differential refractometer

Cellulase treatment of cellulose fibers needs to be monitored to give proper weight loss without significant strength loss. On-line monitoring of cellulase treatment is presented which can monitor the weight loss of cotton fabrics by measuring differential refractive index in real time. On-line monitoring was tried under the condition where the cellulase treatment of scoured 100% cotton knits would give weight loss of about 6.6% during one hour cellulase treatment. For comanalyses processing solutions with treatment time of 5, 10, 20, 30, 45 and 60 minutes were taken out, and subjected to sugar analyses using total organic carbon (TOC), and dinitrosalicylic acid (DNS) method as well as HPLC analyses. HPLC analyses showed that cellobiose and glucose were the major components resulting from cellulase treatment of cotton fabric. TOC and differential refractive index measurement proved to be linear to total sugar concentration while DNS method result was dependent on the type of sugar. Various sugar concentrations of the processing solutions measured by these methods were compared with HPLC measurements and correlated with the actual weight loss of the cotton fabric processed. The on-line monitoring device using differential refractometer gave a real time signal which was independent of the ratio of cellobiose and glucose but dependent on the total sugar concentration. The device was also non-destructive.     

基于集体推进视角下的农地经营权有序流转问题研究

近年来,随着农地经营权的逐渐放活,农地经营权流转规模不断扩大,在农地经营权流转中 存在的问题也逐渐显露。农地经营权流转涉及主体多元,本文选取集体推进视角,采用文献法、专家 咨询法、实地调研法,对农地经营权流转问题进行研究。发现在农地经营权流转中存在农村集体经济 组织与村委会职能模糊,流转效率低、公平性不够等问题。在此基础上,提出规范农村集体经济组织、 加强相关制度建设、建立专业化服务体系、健全交易平台等措施。     

Estimating aboveground carbon stocks of urban trees by synergizing ICESat-2 LiDAR with GF-2 data

Accurately mapping carbon stocks of urban trees is necessary for urban managers to design strategies to mitigate climate change. However, the aboveground carbon stocks of urban trees are usually underestimated by passive remote sensing data because of the signal saturation problem. The research is the first attempt to develop a framework to map aboveground carbon density of trees in urban areas by synergizing Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) LiDAR data with Gaofen-2 (GF-2) imagery. The framework consists of three key steps. First, we used a support vector machine classifier to classify GF-2 images and extracted urban tree regions. Second, we estimated the tree carbon density of ICESat-2 strips by developing a ICESat-2 photon feature-based aboveground carbon density estimation model. Third, we mapped the carbon density of urban trees by developing a synergistic model between ICESat-2 and GF-2 data based on an object-oriented method. We tested the approach for the areas within the fifth ring road of Beijing, China. The results showed that the 50th percentile height (PH50) of nighttime photons was a good predictor for estimating carbon density of urban trees, with a R² of 0.69 and a Root Mean Square Error (RMSE) of 2.81 kg C m⁻². Using the spectral features generated by GF-2 imagery, we could further extrapolate the carbon density estimated by ICESat-2 strip data to a full coverage of accurate mapping carbon density by urban trees, resulting in a R² of 0.64 and a RMSE of 2.32 kg C m⁻². The carbon stocks within the fifth ring road of Beijing were 8.28 × 10⁸ kg in total, with the mean carbon density of 3.52 kg C m⁻². Such estimations were larger than that of previous study using passive remote sensing data only, suggesting the integration of spaceborne LiDAR and spectral data could greatly reduce the underestimation of carbon stocks of urban trees. Our approach can more accurately estimate carbon stocks of urban trees and has the potential to be applicable in other cities.     

Modelling tree canopy cover and evaluating the driving factors based on remotely sensed data and machine learning

Quantifying urban tree cover is important to ensure sustainable urban ecosystem. This study calculates urban percent tree cover (PTC) for Bursa city, Turkey from Sentinel-2 data and evaluates the driving factors of PTC using an Artificial Neural Network-Multi Layer Perception (ANN-MLP) approach. For the PTC calculation, a Regression Tree (RT) analysis was performed using several vegetation indices (NDVI, LAI, fCOVER, MSAVI2, and MCARI) to improve accuracy. Socio-economic, topographic, and biophysical variables were incorporated into the ANN-MLP approach to evaluate the factors that drive urban PTC. A PTC prediction map was generated with an accuracy of 0.95 and a coefficient of determination of 0.87. The ANN-MLP training process yielded a correlation coefficient value of 0.71 and an R-square of 0.82 was achieved between the predicted ANN-MLP and observed tree cover maps. A priority tree cover map was generated considering statistical relationships between the factors and the ANN-MLP prediction map in addition to visual interpretations at the urban scale. Results demonstrate that, unlike other urban forms, PTC has a statistically negative relationship with the gross dwelling density (R2 =0.31). Topographic variables including slope and DEM were positively correlated with PTC with the R2 value of 0.80 and 0.72 respectively. The integration of remote sensing data with vegetation indices and driving factors yielded accurate prediction for identifying and evaluating the variability in the urban PTC.     

Polysaccharide and salt effects on infiltration and soil erosion. A rainfall simulation study

Seals forming at the soil surface during rainstorms reduce water penetration and increase runoff in many arid and semi-arid regions. The effect of surface application of an anionic polysaccharide (designated F-Ac), synthesized by the filamentous cyanobacterium Anabaenopsis circularis PCC 6720, on infiltration rate (IR), runoff and erosion of three soils during simulated rainstorms, was studied. The interaction between F-Ac and electrolyte concentration at the soil surface was studied by using distilled water (DW) or tap water (TW) or by spreading phosphogypsum (PG) on the soil surface. F-Ac added at the rate of 3.4 kg ha⁻¹ together with PG at the rate of 5 t ha⁻¹ was the most efficient treatment in improving infiltration and reducing runoff and erosion. This treatment reduced runoff, from the three soils studied, from 65–80% in the control to 14–24%. Soil loss was reduced from 3.6–4.5 Mg ha⁻¹ in the control to 0.5–1.3 Mg ha⁻¹ in the treated soils. DW treatment, singly and in combination with F-Ac, was quite inefficient in preventing seal formation and in reducing runoff and soil loss. Adding F-Ac with TW maintained final IR and runoff levels intermediate between those of F-Ac with PG and those of F-Ac with DW. Electrolytes in the soil surface which flocculated soil clay, enhanced the beneficial effect of F-Ac on aggregate stability and thus greatly reduced water and soil losses. The efficacy of F-Ac as a stabilizing agent (i.e., soil conditioner) wore out during three consecutive storms of 60 mm each.