Study on Ecological Change Remote Sensing Monitoring Method Based on Elman Dynamic Recurrent Neural Network

In this paper, Hailin City of Heilongjiang Province, China is taken as the research area. As an important city in Heilongjiang Province, China, the sustainable development of its ecological environment is related to the opening up, economic prosperity and social stability of Northeast China. In this paper, the remote sensing ecological index (RSEI) of Hailin City in recent 20 years was calculated by using Landsat 5/8/9 series satellite images, and the temporal and spatial changes of the ecological environment in Hailin City were further analyzed and the influencing factors were discussed. From 2003 to 2023, the mean value of RSEI in Hailin City decreased and increased, and the ecological environment decreased slightly as a whole. RSEI declined most significantly from 2003 to 2008, and it increased from 2008 to 2013, decreased from 2013 to 2018, and increased from 2018 to 2023 again, with higher RSEI value in the south and lower RSEI value in the northwest. It is suggested to appropriately increase vegetation coverage in the northwest to improve ecological quality. As a result, the predicted value of Elman dynamic recurrent neural network model is consistent with the change trend of the mean value, and the prediction error converges quickly, which can accurately predict the ecological environment quality in the future study area.

Spatiotemporal variations in ecological quality of Otindag Sandy Land based on a new modified remote sensing ecological index

Otindag Sandy Land in China is an important ecological barrier to Beijing;the changes in its ecological quality are major concerns for sustainable development and planning of this area.Based on principal component analysis and path analysis,we first generated a modified remote sensing ecological index(MRSEI)coupled with satellite and ground observational data during 2001–2020 that integrated four local indicators(greenness,wetness,and heatness that reflect vegetation status,water,and heat conditions,respectively,as well as soil erosion).Then,we assessed the ecological quality in Otindag Sandy Land during 2001–2020 based on the MRSEI at different time scales(i.e.,the whole year,growing season,and non-growing season).MRSEI generally increased with an upward rate of 0.006/a during 2001–2020,with clear seasonal and spatial variations.Ecological quality was significantly improved in most regions of Otindag Sandy Land but degraded in the southern part.Regions with ecological degradation expanded to 18.64%of the total area in the non-growing season.The area with the worst grade of MRSEI shrunk by 15.83%of the total area from 2001 to 2020,while the area with the best grade of MRSEI increased by 9.77%of the total area.The temporal heterogeneity of ecological conditions indicated that the improvement process of ecological quality in the growing season may be interrupted or deteriorated in the following non-growing season.The implementation of ecological restoration measures in Otindag Sandy Land should not ignore the seasonal characteristics and spatial heterogeneity of local ecological quality.The results can explore the effectiveness of ecological restoration and provide scientific guides on sustainable development measures for drylands.

Environmental Monitoring in Northern Aksu, China Based on Remote Sensing Ecological Index Model

In order to understand the development status of ecological environment quality in the Aksu region of China, to effectively adjust the ecological environment quality, so as to promote the sustainable development of its social economy and ecological environment protection. This paper selects the Landsat series remote sensing images of the northern Aksu region in 2013, 2016, and 2019, and uses the tools such as ENVI5.3 and ArcGIS 10.8.1 to process the image data accordingly. The principal component analysis method is used to calculate the Remote Sensing Ecological Index (RSEI) of the northern Aksu region. The data show that: 1) The ecological environment quality index in the northern Aksu region in 2013, 2016, and 2019 was 0.706087, 0.25243 and 0.362991 respectively;2) The areas where the ecological environment quality declined significantly in the northern Aksu region were the human settlements and the Gobi, fan-shaped land and other special terrain areas;3) The humidity index and the heat index are the two factors that have the greatest impact on the ecological environment quality in the northern Aksu area. The data as a whole show that the ecological environment in the northern part of the Aksu region has deteriorated seriously, and the severely deteriorated area is close to the human living area.

Ecological environment quality evaluation of the Sahel region in Africa based on remote sensing ecological index

Long-term monitoring of the ecological environment changes is helpful for the protection of the ecological environment.Based on the ecological environment of the Sahel region in Africa,we established a remote sensing ecological index(RSEI)model for this region by combining dryness,moisture,greenness,and desertification indicators.Using the Moderate-resolution Imaging Spectroradiometer(MODIS)data in Google Earth Engine(GEE)platform,this study analyzed the ecological environment quality of the Sahel region during the period of 2001-2020.We used liner regression and fluctuation analysis methods to study the trend and fluctuation of RSEI,and utilized the stepwise regression approach to analyze the contribution of each indicator to the RSEI.Further,the correlation analysis was used to analyze the correlation between RSEI and precipitation,and Hurst index was applied to evaluate the change trend of RSEI in the future.The results show that RSEI of the Sahel region exhibited spatial heterogeneity.Specifically,it exhibited a decrease in gradient from south to north of the Sahel region.Moreover,RSEI in parts of the Sahel region presented non-zonal features.Different land-cover types demonstrated different RSEI values and changing trends.We found that RSEI and precipitation were positively correlated,suggesting that precipitation is the controlling factor of RSEI.The areas where RSEI values presented an increasing trend were slightly less than the areas where RSEI values presented a decreasing trend.In the Sahel region,the areas with the ecological environment characterized by continuous deterioration and continuous improvement accounted for 44.02%and 28.29%of the total study area,respectively,and the areas in which the ecological environment was changing from improvement to deterioration and from deterioration to improvement accounted for 12.42%and 15.26%of the whole area,respectively.In the face of the current ecological environment and future change trends of RSEI in the Sahel region,the research results provide a reference for the construction of the"Green Great Wall"(GGW)ecological environment project in Africa.

Development of a large-scale remote sensing ecological index in arid areas and its application in the Aral Sea Basin

The Aral Sea Basin in Central Asia is an important geographical environment unit in the center of Eurasia.It is of great significance to the ecological protection and sustainable development of Central Asia to carry out dynamic monitoring and effective evaluation of the eco-environmental quality of the Aral Sea Basin.In this study,the arid remote sensing ecological index(ARSEI)for large-scale arid areas was developed,which coupled the information of the greenness index,the salinity index,the humidity index,the heat index,and the land degradation index of arid areas.The ARSEI was used to monitor and evaluate the eco-environmental quality of the Aral Sea Basin from 2000 to 2019.The results show that the greenness index,the humidity index and the land degradation index had a positive impact on the quality of the ecological environment in the Aral Sea Basin,while the salinity index and the heat index exerted a negative impact on the quality of the ecological environment.The eco-environmental quality of the Aral Sea Basin demonstrated a trend of initial improvement,followed by deterioration,and finally further improvement.The spatial variation of these changes was significant.From 2000 to 2019,grassland and wasteland(saline alkali land and sandy land)in the central and western parts of the basin had the worst ecological environment quality.The areas with poor ecological environment quality are mainly distributed in rivers,wetlands,and cultivated land around lakes.During the period from 2000 to 2019,except for the surrounding areas of the Aral Sea,the ecological environment quality in other areas of the Aral Sea Basin has been improved in general.The correlation coefficients between the change in the eco-environmental quality and the heat index and between the change in the eco-environmental quality and the humidity index were–0.593 and 0.524,respectively.Climate conditions and human activities have led to different combinations of heat and humidity changes in the eco-environmental quality of the Aral Sea Basin.However,human activities had a greater impact.The ARSEI can quantitatively and intuitively reflect the scale and causes of large-scale and long-time period changes of the eco-environmental quality in arid areas;it is very suitable for the study of the eco-environmental quality in arid areas.

Evaluation of Natural Ecological Environment in Guangzhou City Based on Remote Sensing Technology and Comprehensive Index Method

Based on the ETM remote sensing images of Guangzhou City in 2014, the spatial distribution results o f three environmental factors including vegetation coverage(NDVI), soil index(vegetation index of bare soil) and sl ope were obtained. By using comprehensive index method, the normalized environmental factors were weighted and superimposed, and the fi nal evaluation results of ecological environment in Guangzhou City were obtained. The results showed that overall situation of natural ecological environment in Guangzhou was not optimistic, that is, the area of land with bad, moderate, good and superior environment accounted for 59.70%, 35.79%, 4.50% and around 0.01% of total area of land in Guangzhou City respectively. Ecological environment was generally poor in the central urban districts in the south of Guangzhou City, while it was relatively better in the north and northeast. Attaching importance to the constr uction of greenbelts and greenways is an effective way to improve regional environmental quality and natural ecological e nvironment level.

Human Settlement Evaluation in Mountain Areas Based on Remote Sensing,GIS and Ecological Niche Modeling

The Qinghai-Tibet Plateau is the world's highest and largest plateau.Due to increasing demands for environment exploration and tourism,a large transitional area is required for altitude adaptation.Hehuang valley,which locates in the transition zone between the Loess Plateau and the Qinghai-Tibet Plateau,has convenient transportation and relatively low elevation.Our question is whether the geographic conditions here are appropriate for adapted stay before going into the Qinghai-Tibet Plateau.Therefore,in this study,we examined the potential use of ecological niche modeling(ENM) for mapping current and potential distribution patterns of human settlements.We chose the Maximum Entropy Method(Maxent),an ENM which integrates climate,remote sensing and geographical data,to model distributions and assess land suitability for transition areas.After preprocessing and selection,the correlation between variables and spatial autocorrelation input data were removed and 106 occurrence points and 9 environmental layers were determined as the model inputs.The thresholdindependent model performance was reasonable according to 10 times model running,with the area under the curve(AUC) values being 0.917 ± 0.01,and 0.923 ± 0.002 for test data.Cohen's kappa coefficient of model performance was 0.848.Results showed that 82.22% of the study extent was not suitable for human settlement.Of the remaining areas,highly suitable areas accounted for 1.19%,moderately for 5.3% and marginally for 11.28%.These suitable areas totaled 418.79 km 2,and 86.25% of the sample data was identified in the different gradient of suitable area.The decisive environmental factors were slope and two climate variables:mean diurnal temperature range and temperature seasonality.Our model showed a good performance in mapping and assessing human settlements.This study provides the first predicted potential habitat distribution map for human settlement in Ledu County,which could also help in land use management.

Eco-Environment Evaluation of Grassland Based on Remote Sensing Ecological Index: A Case in Hulunbuir Area, China

This research is based on Landsat5 TM, Landsat8 OLI/TIRS remote sensing data using RSEI model to analyze and monitor the ecological environment and its temporal and spatial changes in the forest-grass transition zone in Northeast China from 2004 to 2019. The change characteristics of the ecological environment of different types of land cover types are monitored by RSEI method, and the response of different land cover types to natural factors such as precipitation and temperature is analyzed at the same time. The distribution of RSEI in the study area presents the characteristics of high in the east and low in the west. The eastern mountainous area is densely covered with woodland, which is the area with the best ecological environment quality in the study area. The grassland in the western plain and the saline-alkali land around the river are the areas with poor ecological environment in the study area. Climate, precipitation, topography and other natural elements work together to form the quality of the ecological environment in the study area roughly bounded by 120?E. In years with poor natural conditions, this dividing line will have a clear eastward shifting trend, especially in the northern part of the study area. The spatial distribution of RSEI in the study area has a high degree of spatial autocorrelation, and Global Moran’s I has been above 0.8 over the years. In terms of temporal changes in ecological conditions, the ecological environment in the study area was basically stable from 2004 to 2008, with a slight deterioration;it improved significantly from 2008 to 2011;however, it deteriorated significantly from 2011 to 2019. According to the results of partial correlation analysis, the ecological environment of the former is highly correlated with natural elements such as climate and precipitation, while the latter is mainly affected by human factors.

基于多时相RSEI的生态环境质量评价——以新民市为例

以辽宁省新民市作为研究对象,基于2014年、2017年、2020年的相近月份(5—6月)Landsat 8 OLI_TRIS数据,提取4个生态因子[绿度(NDVI)、湿度(WET)、干度(NDBSI)、热度(LST)],采用主成分分析法构建遥感生态指数(RSEI),对研究区域生态环境质量时空演变特征进行评价。结果表明,2014年、2017年、2020年新民市RSEI的均值分别为0.397、0.348、0.506,呈先降后升的趋势。2014—2020年,生态环境质量等级为差和较差的区域主要分布在西北区域,面积占比由62.5%降至33.2%;生态环境质量等级为较好和好的区域主要分布在东南区域,面积占比呈明显的先降低后升高趋势,由21.3%先下降到18.4%后上升到37.0%。4个因子中绿度和湿度对生态环境质量起到正面作用,其中湿度的正面影响较为显著;干度和热度起到负面作用,其中干度的负影响较为显著。

Integrated Evaluation of Ecological Security at Different Scales Using Remote Sensing: A Case Study of Zhongxian County, the Three Gorges Area, China

Based on related literature and this research, an ecological security evaluation from the pixel scale to the small watershed or county scale was presented using remote sensing data and related models. With the driver-pressure, state and exposure to pollution-response (DPSER) model as a basis, a conceptual framework of regional ecological evaluation and an index system were established. The extraction and standardization of evaluation indices were carried out with GIS techniques, an information extraction model and a data standardization model. The conversion of regional ecological security results from the pixel scale to a small watershed or county scale was obtained with an evaluation model and a scaling model. Two conceptual scale conversion models of regional ecological security from the pixel scale to the county scale were proposed: 1) scale conversion of ecological security regime results from pixel to small watershed; and 2) scale conversion from pixel to county. These research results could provide useful ideas for regional ecological security evaluation as well as ecological and environmental management.

STUDY ON FOREST FIRE DANGER MODEL WITH REMOTE SENSING BASED ON GIS

Forest fire is one of the main natural hazards because of its fierce destructiveness. Various researches on fire real time monitoring, behavior simulation and loss assessment have been carried out in many countries. As fire prevention is probably the most efficient means for protecting forests, suitable methods should be developed for estimating the fire danger. Fire danger is composed of ecological, human and climatic factors. Therefore, the systematic analysis of the factors including forest characteristics, meteorological status, topographic condition causing forest fire is made in this paper at first. The relationships between biophysical factors and fire danger are paid more attention to. Then the parameters derived from remote sensing data are used to estimate the fire danger variables, According to the analysis, not only PVI (Perpendicular Vegetation Index) can classify different vegetation but also crown density is captured with PVI. Vegetation moisture content has high correlation with the ratio of actual evapotranspiration (LE) to potential ecapotranspiration (LEp). SI (Structural Index), which is the combination of TM band 4 and 5 data, is a good indicator of forest age. Finally, a fire danger prediction model, in which relative importance of each fire factor is taken into account, is built based on GIS.

Developing a process-based and remote sensing driven crop yield model for maize(PRYM–Maize) and its validation over the Northeast China Plain

Spatial dynamics of crop yield provide useful information for improving the production. High sensitivity of crop growth models to uncertainties in input factors and parameters and relatively coarse parameterizations in conventional remote sensing(RS) approaches limited their applications over broad regions. In this study, a process-based and remote sensing driven crop yield model for maize(PRYM–Maize) was developed to estimate regional maize yield, and it was implemented using eight data-model coupling strategies(DMCSs) over the Northeast China Plain(NECP). Simulations under eight DMCSs were validated against the prefecture-level statistics(2010–2012) reported by National Bureau of Statistics of China, and inter-compared. The 3-year averaged result could give more robust estimate than the yearly simulation for maize yield over space. A 3-year averaged validation showed that prefecture-level estimates by PRYM–Maize under DMCS8, which coupled with the development stage(DVS)-based grain-filling algorithm and RS phenology information and leaf area index(LAI), had higher correlation(R, 0.61) and smaller root mean standard error(RMSE, 1.33 t ha^(–1)) with the statistics than did PRYM–Maize under other DMCSs. The result also demonstrated that DVS-based grain-filling algorithm worked better for maize yield than did the harvest index(HI)-based method, and both RS phenology information and LAI worked for improving regional maize yield estimate. These results demonstrate that the developed PRYM–Maize under DMCS8 gives reasonable estimates for maize yield and provides scientific basis facilitating the understanding the spatial variations of maize yield over the NECP.

UAV and Satellite-Based Sensing to Map Ecological States at the Landscape Scale

Mapping ecological states in semi-arid rangelands is crucial for effective land management and conservation efforts because it identifies difference in the ecological conditions across a landscape. This study presents an innovative approach for mapping two ecological states, Large Shrub Grass (LSG) and Large Shrub Eroded (LSE), within the Sandy Loam Upland and Deep (SLUD) ecological sites using a combination of drone and satellite data. The methodology leverages the Largest Patch Index (LPI) as a proxy metric to estimate eroded areas and classify ecological states. The integration of unmanned aerial vehicle (UAV) data with satellite-based remote sensing provides a scalable approach that can benefit various stakeholders involved in rangeland management. The study demonstrates the potential of this methodology by generating spatial layers at the landscape scale to inform on the state of rangeland ecosystems. The workflow showcases the power of remote sensing technology to map ecological states and addresses limitations in spatial coverage by integrating UAV and satellite data. By utilizing the bare ground LPI metric, which indicates the connectedness of bare ground, the methodology enables the classification of ecological states at a regional scale. This cost-effective approach potentially offers a standardized and reproducible method applicable across different sites and regions. The accuracy of the classification process is evaluated by comparing the results to ground-based polygons, dirt roads, and water locations. While the model performs well in identifying eroded areas, misclassifications occur in regions with mixed vegetation cover or low biomass. Future research should focus on incorporating temporal information from historical remote sensing archives to improve understanding of ecological state dynamics. Additionally, validation efforts can be enhanced by incorporating more ground-truth data and testing the methodology in diverse rangeland areas. The workflow serves as a blueprint for scaling up ecological states mapping in similar semi-arid rangelands. Further work should involve refining the approach through additional validation and exploring new remote sensing datasets. The methodology can be replicated in other regions to inform land management decisions, promote sustainable resource use, and advance the field of ecological states mapping.

基于RSEI的山东省不同土地覆盖区生态环境质量变化特征及驱动因素

[目的]厘清城市化建设的持续推进下山东省目前的生态环境质量时空状况及其演化的主要驱动因子,对认清山东省生态环境现状及后期制定切实可靠的保护战略和制度至关重要。[方法]基于1991—2021年的综合指标——遥感生态指数(remote sensing ecological index,RSEI)并辅以Sen趋势法、变异系数法、Hurst指数法和地理探测器对山东省生态环境质量变化特征及其变化的驱动因子进行了探究。[结果](1)1991—2000年山东省生态环境质量总体为下降趋势,变化较为稳定;2001—2010年RSEI总体呈增加趋势,以稳定变化趋势为主;2011—2021年RSEI变化大部分区域呈稳定状态,林地和耕地区域RSEI为增加趋势,其他土地利用类型均为减小趋势。(2)RSEI未来一段时期内以增加趋势为主,且不同土地利用类型区域未来一段时间内变化趋势与过去31 a相反。(3)1990—2000年、2001—2010年土地利用变化对山东省RSEI空间分布变化的影响最大,2011—2021年RSEI空间分布变化受到湿度变化和土地利用变化的影响较大,且土地利用与其他因子间的交互作用对RSEI的影响均较大。[结论]山东省不同时段、不同生态环境质量对环境因子的敏感性存在显著差异,这是因为不同区域人类活动强度、方式和不同区域生态环境脆弱性存在差异。生态环境质量的可视化表达可以较好地反映生态环境质量时空演化特征,为维护人-生态环境平衡的决策制定提供重要的参考信息。

STUDY ON MODEL FOR REMOTE SENSING ESTIMATION OF MAIZE YIELD

ＳＴＵＤＹＯＮＭＯＤＥＬＦＯＲＲＥＭＯＴＥＳＥＮＳＩＮＧＥＳＴＩＭＡＴＩＯＮＯＦＭＡＩＺＥＹＩＥＬＤＬｉｕＺｈａｏｌｉ（刘兆礼）ＨｕａｎｇＴｉｅｑｉｎｇ（黄铁青）ＷａｎＥｎｐｕ（万恩璞）ＺｈａｎｇＹａｎｇｚｈｅｎ（张养贞）ＣｈａｎｇｃｈｕｎＩｎｓｔｉ...

基于RSEI的长时序区域生态安全格局构建——以太行山区河北段为例

以太行山区河北段为研究对象,计算遥感生态指数(Remote Sensing Ecological Index,RSEI),进而识别生态源地,选取土地利用类型、归一化植被指数、高程、坡度等多源空间数据进行综合阻力评价,采用电路理论模型提取生态廊道和生态夹点,构建“源地-廊道-节点”的长时序区域生态安全格局。结果表明:(1)研究区2002年、2007年、2012年、2017年、2021年的RSEI均值分别为0.507、0.538、0.554、0.493、0.541,20年间RSEI呈现“上升-下降-上升”的变化趋势;(2)研究区源地总面积逐期增加,初期至末期面积增加5705 km^(2),面积占比由2.66%增加至18.56%,源地斑块RSEI均值由0.774上升为0.834;(3)研究区主要生态廊道呈现南北纵向分布,研究末期识别生态源地16个,生态廊道21条,生态夹点5个。

Spatiotemporal Evolution of Ecological Quality in Alpine Freshwater Lake Nature Reserves in the Past 20 Years

The Caohai Nature Reserve is one of the three major plateau freshwater lakes in China.Since the 1950s,human activities such as land reclamation and population relocation have greatly damaged Caohai.A rapid evaluation of the spatiotemporal evolution trend of the ecological quality of the Caohai Nature Reserve is significant for the maintenance and construction of the ecosystem in this area.The research is based on the Google Earth Engine(GEE)remote sensing cloud computing platform.Landsat TM/OLI images from May to October in five time periods:2000-2002,2004-2006,2009-2011,2014-2016,and 2019-2021 were obtained to reconstruct the optimal cloud image set by averaging the images in each time period.By constructing four ecological indicators:Greenness(NDVI),Wetness(Wet),Hotness(LST),and Dryness(NDBSI),and using Principal Component Analysis(PCA)method to obtain the Remote Sensing Ecological Index(RSEI)for the corresponding years,the spatiotemporal variation of ecological quality in the Caohai Nature Reserve over 20 years was analyzed.The results indicate:①the mean value of RSEI increased from 0.460 in 2000-2002 to 0.772 in 2019-2021,a 67.83%increase,indicating a significant improvement in the ecological quality of the reserve over the 20 years;②from the perspective of functional zoning of the Caohai Nature Reserve,the ecological quality of the core area showed a degrading trend,while the ecological quality of the buffer zone and experimental zone significantly improved;③with the implementation of ecological restoration projects,the ecological quality of the reserve gradually recovered and improved from 2014 to 2021.The trend of RSEI value changes is well correlated with human interventions,indicating that the PCA-based RSEI model can be effectively used for ecological quality assessment in lake areas.

基于GEE和RSEI的京津冀地区生态环境质量时序动态评估

探究京津冀地区生态环境质量时空变化特征及其驱动因素,为该地区可持续协调、高质量一体化发展提供依据。基于MODIS数据,借助Google Earth Engine(GEE)平台构建遥感生态指数(RSEI)模型,辅以多种空间分析和统计方法对京津冀地区及其4大功能区生态环境质量进行评价和变化监测。结果表明,1)京津冀地区RSEI均值由2001年的0.512增至2020年的0.575,增幅达12.30%,多年均值为0.499,总体处于中等水平。2)生态环境质量整体呈现“西北优、东南差”的空间格局,35.87%的区域变好,以变好1个等级为主。3)各功能区生态环境质量均得到了改善,其中南部功能拓展区、中部核心功能区的改善最为显著,改善区域占比分别为49.66%、45.79%。4)各因子对生态环境质量的影响不同,自然因素中的降水以及人为因素中的人口和土地利用为主导因素。总的来说,京津冀地区2001-2020年生态环境质量向好,但未来仍需加强人工干预,持续扩大生态优良区。

基于RSEI模型的成都市生态环境遥感评价

城市生态环境是评价区域发展的重要指标,而遥感技术可以提供一种高效的监测手段。本文以成都市为例,基于遥感生态指数(RSEI)模型,通过Google Earth Engine(GEE)云计算平台对城市生态环境质量进行监测和评估。文章采取了一种适用于盆地平原地区的遥感生态指数,结合绿度指数(NDVI)、热度指数(LST)、湿度指数(WET)和干度指数(NDBSI)来评估区域的生态环境质量水平。结果表明,该方法可以快速、高效地进行城市生态环境监测,填补了仅基于有限地面环境站点监测的不足,有助于城市生态环境的高质量建设和区域可持续发展。