The Robinia pseudoacacia forest in the Yellow River delta of China has been planted since the 1970s, and a large area of dieback of the forest has occurred since the 1990s. To assess the condition of the R. pseudoacacia forest in three forest areas (i.e., Gudao, Machang, and Abandoned Yellow River) in the delta, we combined an estimation of scale parameters tool and geometry/topology assessment criteria to determine the optimal scale parameters, selected optimal predictive variables determined by stepwise discriminant analysis, and compared object-based image analysis (OBIA) and pixel-based approaches using IKONOS data. The experimental results showed that the optimal segmentation scale is 5 for both the Gudao and Machang forest areas, and 12 for the Abandoned Yellow River forest area. The results produced by the OBIA method were much better than those created by the pixel-based method. The overall accuracy of the OBIA method was 93.7% (versus 85.4% by the pixel-based) for Gudao, 89.0% (versus 72.7%) for Abandoned Yellow River, and 91.7% (versus 84.4%) for Machang. Our analysis results demonstrated that the OBIA method was an effective tool for rapidly mapping and assessing the health levels of forest.
In this paper, spatial autocorrelation analysis, ordinary least square (OLS) and spatial regression models were applied to
explore spatial variation of soil salinity based on samples collected from the Yellow River Delta. Generally, spatial data,
like soil salinity, elevation height etc., are characterized by spatial effects such as spatial dependence and spatial
structure. Inasmuch as these effects exist, the utilization of OLS model may lead to inaccurate inference about predictor
variable. Moreover, the traditional regression models used to analyze spatial data often have autocorrelated residuals
which violate the assumption of Guess-Markov Theorem. This indicates that conventional regression models cannot be
used in analyzing variability of soil salinity directly. To overcome this limitation, spatial regression model was
introduced to explore the relationship between soil salinity and environmental factors (including elevation height, pH
value and organic matter concentration). By verifying Moran's I scatterplot of residuals, we found no autocorrelation in
spatial regression model compared with high significant (p < 0.001) positive autocorrelation in the OLS model; besides,
the spatial regression model had a significant (p < 0.01) estimations and good-fit-it in our study. Finally, an approach of
specifying optimal spatial weight matrix was also put forward.
Analysis and interpretation of spatial variability of soil salinity is a keystone in site-specific farming. To better
understand patters of multi-scale spatial variability in soil salinity, soil samples (30 to 40 cm depth) were collected with
separation distances of 0.04, 0.2, 1 and 6 km in the Yellow River Delta of China. Laboratory measurements of soil salt
content were also made from these samples (n = 239). Moran's I autocorrelation coefficient was computed at preselected
lag distances and correlograms were plotted to examine trends in autocorrelation. Spatial autocorrelation was found at
scales ranging from 0.7 km to more than 75 km, depending on the sampling scale considered. A correlation range in
regional scale appeared to be associated with elevation height, while a shorter range in field scale was likely influenced
by alternating land use/land cover or microtopography types. Moran's I correlogram calculated with salinity data from
all of the sampling locations suggested spatial pattern detection for soil salinity can be achieved with a sampling interval
of approximately 2 km or less. The magnitude and spatial patterns of soil salinity have implications for devising
appropriate schemes to improve land productivity and design of soil sampling strategies in the Yellow River Delta.