Time Series Of Remote Sensing Data Research Articles

Estimating the Maximal Light Use Efficiency for Different Vegetation through the CASA Model Combined with Time-Series Remote Sensing Data and Ground Measurements

Maximal light use efficiency (LUE) is an important ecological index of a vegetation essential attribute, and a key parameter of the LUE-based model for estimating large-scale vegetation productivity by remote sensing technology. However, although currently used in different models there still exists extensive controversy. This paper takes the Zoige Plateau in China as a case area to develop a new approach for estimating the maximal LUEs for different vegetation. Based on an existing land cover map and MODIS NDVI product, the linear unmixing method with a moving window was adopted to estimate the time-series NDVI for different end members in a MODIS NDVI pixel; then Particle Swarm Optimizer (PSO) was applied to search for the optimization of LUE retrievals through the CASA (Carnegie-Ames-Stanford Approach) model combined with time-series NDVI and ground measurements. The derived maximal LUEs present significant differences among various vegetation types. These are 0.669 gC·MJ−1, 0.450 gC·MJ−1 and 0.126 gC·MJ−1 for the xerophilous grasslands with high, moderate and low vegetation fraction respectively, 0.192 gC·MJ−1 for the hygrophilous grasslands, and 0.125 gC·MJ−1 for the helobious grasslands. The field validation shows that the estimated net primary productivity (NPP) by the derived maximal LUE is closely related to the ground references, with R2 of 0.8698 and root-mean-square error (RMSE) of 59.37 gC·m−2·a−1. This indicates that the default set in the CASA model is not suitable for NPP estimation for the regional mountain area. The derived maximal LUEs can significantly improve the capability of NPP mapping, and open up the perspective for long-term monitoring of vegetation ecological health and ecosystem productivity by combining the LUE-based model with remote sensing observations.

Development Status and Application Research of the Time Series Remote Sensing Data Products based on AVHRR,VEGETATION and MODIS

Development Status and Application Research of the Time Series Remote Sensing Data Products based on AVHRR,VEGETATION and MODIS

Who is responsible for deforestation in the Amazon? A spatially explicit analysis along the Transamazon Highway in Brazil

Understanding actor-specific responsibility for deforestation in the Brazilian Amazon is key in adjusting policy and resource allocation in the face of current forest destruction. However, previous research shows that there is great variability in such assessments. To contribute to the ongoing discussions on forest conservation and rural development policies in the Amazon, this paper studies actor-specific deforestation and its environmental effects in four municipalities situated along the Transamazon Highway. We used spatially explicit methods that integrate a database of 8281 georeferenced properties with a time series of remote-sensing data covering four periods between 1986 and 2007. We also included landscape ecology metrics as improved indicators of the complex environmental effects of forest fragmentation. The analysis demonstrates that smallholders (defined as colonists who own less than 100ha of land) were responsible for 23% of total deforestation in the study region while accounting for 55% of the total properties. We also explored the relationship between property size and deforestation at the property level, finding that it closely follows a power distribution. Property deforestation increased with property size, while the percentage of property deforestation decreased. In spite of this, compliance with current legal requirements to maintain 50% of property forest cover was not statistically different between smallholders and largeholders. In comparison to municipalities dominated by medium- and large-scale ranchers, the smallholder-dominated municipality of Medicilândia showed better performance in all applied landscape metrics with well-established relationships with the provision of important environmental goods and services. Although all studied municipalities showed severe accumulated deforestation, Medicilândia experienced an abrupt decrease in municipal deforestation after 1999 to just 0.03%year−1, while municipalities dominated by larger holders maintained or increased their previous deforestation rates to between 0.90% and 1.34%year−1 in the same period. This indicates that the smallholders’ productions schemes in our study area might present potential for agricultural frontier stabilization based on improved land-use efficiency. The policy implications of our findings are discussed, especially with regard to the role of smallholders in productive forest conservation.

Variational retrieval of leaf area index from MODIS time series data: examples from the Heihe river basin, north-west China

Leaf area index (LAI) products retrieved from observations acquired on one occasion have obvious discontinuity in the time series owing to cloud coverage and other factors, and the accuracy may not meet the needs of many applications. Effectively utilizing data assimilation techniques to retrieve biophysical parameters from the time series of remote-sensing data has attracted special interest. The data assimilation technique is based on a reasonable consideration of dynamic change rules of biophysical parameters and time series observational quantities, thereby improving the quality of retrieved profiles. In this article, a variational assimilation procedure for retrieving LAI from the time series of remote-sensing data is developed. The procedure is based on the formulation of an objective function. A dynamic model is constructed based on the climatology from multi-year Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data to evolve LAI in time, and a radiative transfer model is coupled with the dynamic model to simulate a time series of surface reflectances. A shuffled complex evolution method (developed at the University of Arizona; SCE-UA) optimization algorithm is then used to minimize the objective function and estimate the dynamic model states and the parameters of the coupled model from the MODIS reflectance data with a higher quality in a given time window. The variational assimilation method is applied to the MODIS surface reflectance data for the whole of 2008 at the Heihe river basin to produce regional LAI mapping results. The ground LAI data measured in situ are used to develop algorithms to estimate LAI from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) surface reflectance, and ASTER LAI maps are produced for each ASTER scene using the algorithms developed. Then the ASTER LAI maps are aggregated to compare with the new LAI products. It is found that the variational assimilation method is able to produce temporal continuous LAI products and that accuracy has been improved over the MODIS LAI standard product.