Poplar looper (Apocheima cinerarius Erschoff) is a destructive insect infesting Euphrates or desert poplars (Populus euphratica) in Xinjiang, China. Since the late 1950s, it has been plaguing desert poplars in the Tarim Basin in Xinjiang and caused widespread damages. This paper presents an approach to the detection of poplar looper infestations on desert poplars and the assessment of the severity of the infestations using time-series MODIS NDVI data via the wavelet transform and discriminant analysis, using the middle and lower reaches of the Yerqiang River as a case study. We first applied the wavelet transform to the NDVI time series data in the period of 2009–2014 for the study area, which decomposed the data into a representation that shows detailed NDVI changes and trends as a function of time. This representation captures both intra- and inter-annual changes in the data, some of which characterise transient events. The decomposed components were then used to filter out details of the changes to create a smoothed NDVI time series that represent the phenology of healthy desert poplars. Next the subset of the original NDVI time series spanning the time period when the pest was active was extracted and added to the smoothed time series to generate a blended time series. The wavelet transform was applied again to decompose the blended time series to enhance and identify the changes in the data that may represent the signals of the pest infestations. Based on the amplitude of the enhanced pest infestation signals, a predictive model was developed via discriminant analysis to detect the pest infestation and assess its severity. The predictive model achieved a severity classification accuracy of 91.7% and 94.37% accuracy in detecting the time of the outbreak. The methodology presented in this paper provides a fast, precise, and practical method for monitoring pest outbreak in dense desert poplar forests, which can be used to support the surveillance and control of poplar looper infestations on desert poplars. It is of great significance to the conservation of the desert ecological environment.
A paddy field ecosystem (PFE) is an important component of an agricultural land ecosystem and is also a special artificial wetland with extremely high value. Taking Tianjin (a municipality city in China) as the research area and using multi-source remote sensing data, we improved the accounting framework of the ecosystem service value (ESV) of PFEs and the calibration of model parameters. The ESV of PFEs was mapped at medium-high resolution and fine-grain at the provincial scale. The results showed that: (1) the net ESV of PFEs in Tianjin in 2019 was RMB 29.68 × 108, accounting for 0.21% of GDP. The positive ESV was RMB 35.53 × 108, the negative ESV was RMB 5.84 × 108, and the average ESV per unit area was RMB 5.47 × 104/ha; (2) as a proportion of the ESV of PFE, the value of climate regulation (61.27%) was greater than the value of carbon fixation and oxygen release (15.29%), which was greater than the value of primary products supply (8.08%). The production value of PFEs is far lower than their ESV; (3) the total net ESV in Baodi District was RMB 16.85 × 108, accounting for 56.77% of Tianjin’s ESV, and the net ESV per unit area was RMB 5.72 × 104/ha, both of which were higher than in other districts; (4) the pixel-based hot spots analysis showed that the number of hot spots (high-value ESV) and cold spots (low-value ESV) reached 98.00% (hot spots 56.9%, cold spots 41.1%) with a significant cluster distribution. The hot spots were mostly distributed in Baodi District (37.8%) and the cold spots were mostly distributed in Ninghe District (17.2%). The research results can support agricultural development, improve countermeasures according to local conditions, and provide theoretical support for regional land use planning, ecological compensation policy formulation and ecological sustainable development. Our methodology can be used to assess the impact of land use change on ESV.
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