Based on 116-phase Landsat satellite remote sensing data between 1984 and 2021, this paper inverted the long-term distribution of water quality parameters such as dissolved oxygen (DO), oxidation-reduction potential (ORP), and chlorophyll-a (Chl-a) of the water in Baiyangdian Lake, and analyzed the spatiotemporal distribution characteristics and variations of water quality in Baiyangdian Lake over 37 years. In terms of temporal scale, the inter-annual variation of DO shows certain stability, and the images with the proportion of pollution-free area reaching 90% or more account for 88.7% of the total, showing no pollution in terms of DO; In terms of ORP, the images with the proportion of pollution-free area reaching 90% or more account for 81.2% of the total, showing no pollution to light pollution; the inter-annual variation of Chl-a concentration shows certain volatility, and the overall performance is light-moderate pollution, but the pollution level has been alleviated in recent years; the pollution status of water quality in Baiyangdian Lake in terms of Chl-a and ORP has a certain correlation. In terms of spatial scale, the spatial distribution pattern of DO and ORP is stable, presenting the characteristic that most areas are pollution-free, and a few areas with more frequent human activities show light and moderate pollution.
Light Detection And Ranging (LiDAR) is an important branch of remote sensing (RS) technology, and its hardware and software in practical applications are getting more and more mature. Now, it is time for the community to think about its future, and a potential way of further pushing forward LiDAR RS technical progress, no doubt, is to develop its nextgeneration systems and approaches. Hyperspectral LiDAR is such a representative case, which, theoretically, is designed to synchronously collect the spectral and range information of objects. This advantage can inherently handle the errors caused when fusing those corresponding hypespectral images and point clouds in the traditional routines of 4D mapping, and hence, has attracted numerous attention on developing its prototype systems. With the performance enhancements of such prototype systems, more efforts need to be deployed onto pushing these prototypes to practical applications. In the case of the hyperspectral LiDAR prototype system developed by the Finnish Geospatial Research Institute, this study examined its applicability for investigating the intraday 3D variations of tree biophysics and biochemistry. The collected point clouds proved to be able to characterize the biophysical variation of trees in terms of laser point-represented tree geometrical centre. For the aspect of biochemical characterization, the hyperspectral LiDAR was validated through the retrievals of the 3D distributions of the fractions of photosynthetically active radiation (FAPARs), crown chlorophyll concentrations, and crown nitrogen concentrations, and the intraday biochemical variations were characterized by their day-and-night differences. The tests showed that hyperspectral LiDAR will be a kind of technology of high potentials for mapping biophysics and biochemistry and their dynamics.
Rocky desertification is one of the most serious problems in environmental deterioration, and its accurate mapping is of many implications for maintaining Earth’s sustainability. Compared to traditional field surveying approaches, remote sensing (RS), particularly hyperspectral RS, has proved to be a more efficient solution plan. Yet, hyperspectral RS also suffers from the problem of spectral mixing, since rocky desertification may correspond to various fractions of vegetation, bare soil, and exposed rock. Although linear spectral unmixing (LSU) is an effective method for resolving their ratios, different constraint conditions involving the selections of pure objects and end-member spectra may influence the result. To better overcome this basic problem, the key point of parameter comparison for LSU in field hyperspectral sampling of rocky desertification was investigated. In the typical rocky desertification areas in southwest China, an experiment of field hyperspectral RS sampling various scenarios of ground object mixing was conducted. Then, LSU was operated, by firstly classifying the digital photos of the sample plots to derive the proportion of each object. The specific LSU operations were classified into four types. The selection of end-member spectra were classified into three kinds of cases. With the 12 combination cases of the above-listed scenarios compared, we found that the results under the conditions of ANC and full constraint were better than the ASC and unconstrained conditions, and the performance for the end-member selection scenarios from case A to case C was dropping but could handle more complex situations. These inferences can supply a more solid theoretical basis for better implementing spectral unmixing in hyperspectral RS of rocky desertification.
Tree species information is essential for forest research and management purposes, which in turn require approaches for accurate and precise classification of tree species. One such remote sensing technology, terrestrial laser scanning (TLS), has proved to be capable of characterizing detailed tree structures, such as tree stem geometry. Can TLS further differentiate between broad- and needle-leaves? If the answer is positive, TLS data can be used for classification of taxonomic tree groups by directly examining their differences in leaf morphology. An analysis was proposed to assess TLS-represented broad- and needle-leaf structures, followed by a Bayes classifier to perform the classification. Tests indicated that the proposed method can basically implement the task, with an overall accuracy of 77.78%. This study indicates a way of implementing the classification of the two major broad- and needle-leaf taxonomies measured by TLS in accordance to their literal definitions, and manifests the potential of extending TLS applications in forestry.
Desertification is one of the most serious ecological and environmental problems in the arid and semi-arid areas of
western China. This study demonstrated a cell-based modeling approach to monitor and evaluate the land degradation
using remotely sensed data in Shihezi area, Xinjiang, China. Two-date Landsat TM imagery of 2000 and 2008 was used
to derive factors such as land surface temperature, NDVI and soil moisture etc. The preprocessing of the images was
conducted and the DN values were converted into albedo. The mono-window algorithm was applied to the TM band 6 to
compute land surface temperature, and the regressive relationship between Temperature Vegetation Dryness Index
(TVDI) and field-surveyed soil moisture was modeled and then used to derive soil moisture factor. After that a weighted
linear combination of those factors was applied to create an integrated index to characterize the desertification and
degradation of land in the study area. The resultant index was then categorized into four classes: non-desertificated,
slightly-desertificated, moderate-desertificated, and heavily-desertificated, and finally a map of desertification was
created and used to analyze the land degradation in the Shihezi area. The map shows that the degree of desertification
diminished gradually from north to south. Due to the graze control policy and land rehabilitation, the threatening of
desertification in 2008 is smaller than that in 2000, and especially in the northern and middle areas. Field verification
also supports the results positively and thus the map of desertification can be used as a reference for land management
and regional environmental protection.
Ecological vulnerability evaluation has important real significance and scientific value. In this study, under the support of
Remote Sensing and Geographic Information System, we use TM images, distribution map of sand desertification and
soil salinization, and related geographic information, and adopt a combined landscape pattern and ecosystem sensitivity
approach to access the ecological vulnerability of Duerbete County. We consider the following five factors to develop the
model: (1) reciprocal of fractal dimension (FD'), (2) isolation (FI), (3) fragmentation (FN), (4) sensitivity of sand
desertification (SD), and (5) sensitivity of soil salinization (SA). Then we build the evaluation model and calculate the
vulnerability of landscape type of Duerbete. Through Kriging interpolation, we get the regional eco-environment
vulnerability of whole county. Then we evaluate this cropping-pastoral interlacing region-Duerbete County. The
conclusions are: (1) The vulnerability of all landscape types is in the following decreasing order: grassland > cropland >
unused area > water area > construction area > wattenmeer > reed bed > woodland > paddy field; (2) There are
significant positive relationships between VI and
FN, VI and SD, SD and FN, SA and FN. This suggests that FN and SD have considerable impact on the eco-environmental vulnerability; (3) With the combination of FN, SD and SA, the regional eco-environment vulnerability can be evaluated well. The result is reasonable and can
support ecological construction.
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