Normalized Difference Soil Index Research Articles

Downscaling SMAP soil moisture product in cold and arid region: Incorporating NDSI and BSI into the random forest algorithm

AbstractSoil moisture (SM) is a critical element of the hydrological cycle, land surface processes, and surface energy balance. However, the low spatial resolution of commonly used SM products limits the application of SM in agriculture and eco‐hydrology in cold and arid regions. In this study, the normalized difference soil index (NDSI) and bare soil index (BSI) were added to traditional downscaling factors including land surface temperature, normalized difference vegetation index, digital elevation mode, apparent thermal inertia, Albedo, and temperature vegetation dryness index, as they are more strongly correlated with surface SM in the bare soil‐vegetation alternation zone of such region. Using the random forest algorithm, a downscaling model of SM was constructed for such region. The accuracy of the downscaled SM estimates was validated by comparing them with the original SM data collected from May to September 2021, which is the non‐freezing period of the soil. The findings indicate that the newly added NDSI and BSI have good correlation with SM. Incorporating NDSI and BSI to construct the downscaled model enhances the accuracy by over 19% compared to excluding them, while also providing a more comprehensive representation of SM information. NDSI and BSI can be well applied to the downscaled research of SM in the bare soil‐vegetation alternation zone, which is of great value for the study of eco‐hydrology and agricultural drought monitoring in cold and arid regions.

A Learning Strategy for Amazon Deforestation Estimations Using Multi-Modal Satellite Imagery

Estimations of deforestation are crucial as increased levels of deforestation induce serious environmental problems. However, it is challenging to perform investigations over extensive areas, such as the Amazon rainforest, due to the vast size of the region and the difficulty of direct human access. Satellite imagery can be used as an effective solution to this problem; combining optical images with synthetic aperture radar (SAR) images enables deforestation monitoring over large areas irrespective of weather conditions. In this study, we propose a learning strategy for multi-modal deforestation estimations on this basis. Images from three different satellites, Sentinel-1, Sentinel-2, and Landsat 8, were utilized to this end. The proposed algorithm overcomes visibility limitations due to a long rainy season of the Amazon by creating a multi-modal dataset using supplementary SAR images, achieving high estimation accuracy. The dataset is composed of satellite data taken on a daily basis with relatively less monthly generated, ground truth masking data, which is called the many-to-one-mask condition. The Normalized Difference Vegetation Index and Normalized Difference Soil Index bands are selected to comprise the datasets. This yields better detection performance and a shorter training time than datasets consisting of RGB or all bands. Multiple deep neural networks are independently trained for each modality and an appropriate fusion method is developed to detect deforestation. The proposed method utilizes the distance similarity of the predicted deforestation rate to filter prediction results. The elements with high degrees of similarity are merged into the final result with average and denoising operations. The performances of five network variants of the U-Net family are compared, with Attention U-Net observed to exhibit the best prediction results. Finally, the proposed method is utilized to estimate the deforestation status of novel queries with high accuracy.

Temporal remote sensing based soil salinity mapping in Indo-Gangetic plain employing machine-learning techniques

Soil salinization is one of the most active land degradation processes, affecting predominantly arid, semi-arid, and dry sub-humid regions and leading to decreased agricultural yields. The Indo-Gangetic plain, which includes the irrigated command areas with arid and semi-arid climatic conditions are severely affected by secondary soil salinization. Assessing the spatial and temporal extent as well as the severity of salinization is an important step for adoption of proper reclamation measures to boost agricultural productivity in the salt affected areas. The study was conducted with this background to evaluate the extent and severity of soil salinization in alluvial plains of Mathura district of Uttar Pradesh, India. In this study, the satellite data of 6 months from January 2019 to June 2019 were pre-processed and various spectral indices were generated in Google Earth Engine. Remote sensing techniques provides an ideal platform for addressing this problem at larger scales and thus we employed Sentinel-2 derived vegetation and salinity spectral indices for distinguishing temporal change in severity of soil salinization and map the salinity as a function of these indices for the entire study area. The time series salinity analysis showed that among the various spectral indices Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Soil Index (NDSI) had a clear differentiation between slight, moderate and severe salinity class in the first three months of the study period and the Salinity Index II (SI_II) could differentiate for the first four months. Further, two machine learning algorithms namely Random Forest (RF) and Support Vector Machine (SVM), were used to create soil salinity prediction models making use of the soil Electrical Conductivity (EC) values of 115 ground-sampling sites as the predictand variable and the optimal spectral indices as the predictor variables. Further, we evaluated the prediction ability of different models using 12 and 24 variables combination using R2 and RMSE values. The prediction accuracy of the RF model was found to be slightly higher than that of the SVM model, and the spatial distribution pattern of soil salinity predicted by the two models were comparable. We concluded that spectral indices combined with machine learning techniques have the potential for low cost reliable spatial and temporal soil salinity distribution mapping for planning and implementation of salinity reclamation measures.

Machine learning based combinatorial analysis for land use and land cover assessment in Kyiv City (Ukraine)

The main goal of this study is to evaluate different models for further improvement of the accuracy of land use and land cover (LULC) classification on Google Earth Engine using random forest (RF) and support vector machine (SVM) learning algorithms. Ten indices, namely normalized difference vegetation index, normalized difference soil index, index-based built-up index, biophysical composition index, built-up area extraction index (BAEI), urban index, new built-up index, band ratio for built-up area, bare soil index, and normalized built up area index, were used as input parameters for the machine learning algorithms to improve classification accuracy. The combinatorial analysis of the Sentinel-2 bands and the aforementioned indices allowed us to create four combinations based on surface reflectance characteristics. The study includes data from April 2020 to September 2021 and April 2022 to June 2022. The multitemporal Sentinel-2 data with spatial resolutions of 10 m were used to determine the LULC classification. The major land use classes such as water, forest, grassland, urban areas, and other lands were obtained. Generally, the RF algorithm showed higher accuracy than the SVM. The overall accuracy for RF and SVM was 86.56% and 84.48%, respectively, and the mean Kappa was 0.82 and 0.79, respectively. Using the combination 2 with the RF algorithm and combination 4 with the SVM algorithm for LULC classification was more accurate. The additional use of vegetation indices allowed to increase in the accuracy of LULC classification and separate classes with similar reflection spectra.

Decision Tree and Random Forest Classification Algorithms for Mangrove Forest Mapping in Sembilang National Park, Indonesia

Sembilang National Park, one of the best and largest mangrove areas in Indonesia, is very vulnerable to disturbance by community activities. Changes in the dynamic condition of mangrove forests in Sembilang National Park must be quickly and easily accompanied by mangrove monitoring efforts. One way to monitor mangrove forests is to use remote sensing technology. Recently, machine-learning classification techniques have been widely used to classify mangrove forests. This study aims to investigate the ability of decision tree (DT) and random forest (RF) machine-learning algorithms to determine the mangrove forest distribution in Sembilang National Park. The satellite data used are Landsat-7 ETM+ acquired on 30 June 2002 and Landsat-8 OLI acquired on 9 September 2019, as well as supporting data such as SPOT 6/7 image acquired in 2020–2021, MERIT DEM and an existing mangrove map. The pre-processing includes radiometric and atmospheric corrections performed using the semi-automatic classification plugin contained in Quantum GIS. We applied decision tree and random forest algorithms to classify the mangrove forest. In the DT algorithm, threshold analysis is carried out to obtain the most optimal threshold value in distinguishing mangrove and non-mangrove objects. Here, the use of DT and RF algorithms involves several important parameters, namely, the normalized difference moisture index (NDMI), normalized difference soil index (NDSI), near-infrared (NIR) band, and digital elevation model (DEM) data. The results of DT and RF classification from Landsat-7 ETM+ and Landsat-8 OLI images show similarities regarding mangrove spatial distribution. The DT classification algorithm with the parameter combination NDMI + NDSI + DEM is very effective in classifying Landsat-7 ETM+ image, while the parameter combination NDMI + NIR is very effective in classifying Landsat-8 OLI image. The RF classification algorithm with the parameter Image (6 bands), the number of trees = 100, the number of variables predictor (mtry) is square root (√k), and the minimum number of node sizes = 6, provides the highest overall accuracy for Landsat-7 ETM+ image, while combining Image (7 bands) + NDMI + NDSI + DEM parameters with the number of trees = 100, mtry = all variables (k), and the minimum node size = 6 provides the highest overall accuracy for Landsat-8 OLI image. The overall classification accuracy is higher when using the RF algorithm (99.12%) instead of DT (92.82%) for the Landsat-7 ETM+ image, but it is slightly higher when using the DT algorithm (98.34%) instead of the RF algorithm (97.79%) for the Landsat-8 OLI image. The overall RF classification algorithm outperforms DT because all RF classification model parameters provide a higher producer accuracy in mapping mangrove forests. This development of the classification method should support the monitoring and rehabilitation programs of mangroves more quickly and easily, particularly in Indonesia.

Evaluation of Temporal and Spatial Changes in Ecological Environmental Quality on Jianghan Plain From 1990 to 2021

Located in the middle reaches of the Yangtze River, Jianghan Plain is an important part of the middle and lower reaches of the Yangtze River Plain, and together with Dongting Lake Plain, it is known as the Two-Lake Plain. It is a well-developed agricultural area and is an important source of grain in China, as well as one of the major cotton-producing areas, and aquaculture is also an important local industry. With the rapid development of urbanization in China, the impact of human activities on the ecological environment of Jianghan Plain has become increasingly obvious in recent years, and how to timely and objectively assess spatial and temporal changes in ecological environmental quality is of great practical significance for the sustainable development of the region and the construction of an ecological civilization. The Google Earth Engine platform was used to optimize the reconstruction of Landsat TM/OLI images of Jianghan Plain from 1990 to 2021, coupled with four indicators of the natural ecological environment such as the Normalized Difference Vegetation Index, WET, Normalized Difference Soil Index, and Land Surface Temperature to construct the remote sensing ecological index (RSEI) and evaluate the spatial and temporal changes in ecological environmental quality on Jianghan Plain. The results showed that the mean RSEI values in 1990, 1998, 2006, 2014, and 2021 were 0.667, 0.636, 0.599, 0.621, and 0.648, respectively, indicating that the overall ecological environmental quality of Jianghan Plain showed a decreasing trend from 1990 to 2006 and an increasing trend from 2006 to 2021. Degradation was most serious from 1990 to 1998, accounting for 44.86% of the total area, and improvement was most obvious from 2006 to 2014, accounting for 26.64% of the total area. Moran’s I values from 1990 to 2021 were 0.531, 0.529, 0.525, 0.540, and 0.545, respectively, indicating that the spatial distribution of ecological environmental quality was positively correlated. The local spatial clustering of the RSEI local indicators of spatial association showed that H-H clustering areas on Jianghan Plain were mainly distributed in the northern and western regions, and L-L clustering areas were mainly distributed in the densely populated eastern regions with frequent human activities. The results can provide a theoretical basis for ecological environmental protection and improvement on Jianghan Plain.

Improving the accuracy of land cover classification using sentinel 2 data and knowledge based classification system in the west of Amara city, Iraq

In this paper, we focus on the use of remote sensing analysis technique with the Sentinel-2 data in order to discriminate land cover in Al-Maimouna District, West of Amara, Iraq. The main objective of the study is to explore whether the expert classification technique can improve the accuracy of land cover classification. In our experiment, an expert classification technique was applied to the Sentinel-2 data set by using, normalized difference vegetation index, soil-adjusted vegetation index, normalized difference water index, modified normalized difference water index, urban index, normalized difference built-up index, normalized difference soil index and bare soil index. Build-up area was found to be the dominant type of land use classified which covers about 41.9% of the total study area, while the least classified was vegetation which accounts for 15.1%. The results of the study demonstrates that the expert classification technique produced an overall accuracy of 88.3%. The results of the study also indicate that by using an expert classification technique, a significantly higher discrimination accuracy can be achieved. Moreover, the expert classification technique reduces problems associated with high-resolution images; while reliabilities are achieved that are better than those achieved with traditional methods.

Mapping the Impact of COVID-19 Lockdown on Urban Surface Ecological Status (USES): A Case Study of Kolkata Metropolitan Area (KMA), India

An urban ecosystem’s ecological structure and functions can be assessed through Urban Surface Ecological Status (USES). USES are affected by human activities and environmental processes. The mapping of USESs are crucial for urban environmental sustainability, particularly in developing countries such as India. The COVID-19 pandemic caused unprecedented negative impacts on socio-economic domains; however, there was a reduction in human pressures on the environment. This study aims to assess the effects of lockdown on the USES in the Kolkata Metropolitan Area (KMA), India, during different lockdown phases (phases I, II and III). The land surface temperature (LST), normalized difference vegetation index (NDVI), and wetness and normalized difference soil index (NDSI) were assessed. The USES was developed by combining all of the biophysical parameters using Principal Component Analysis (PCA). The results showed that there was a substantial USES spatial variability in KMA. During lockdown phase III, the USES in fair and poor sustainability areas decreased from 29% (2019) to 24% (2020), and from 33% (2019) to 25% (2020), respectively. Overall, the areas under poor USES decreased from 30% to 25% during lockdown periods. Our results also showed that the USES mean value was 0.49 in 2019but reached 0.34 during the lockdown period (a decrease of more than 30%). The poor USES area was mainly concentrated in built-up areas (with high LST and NDSI), compared to the rural fringe areas of KMA (high NDVI and wetness). The mapping of USES are crucial in different biophysical environmental conditions, and they can be very helpful for the assessment of urban sustainability.

Rapid detection of land cover change in tropical savanna environment using conditional change vector analysis on remote sensing data in Moyo watershed, Sumbawa Regency, West Nusa Tenggara Province, Indonesia

In environmental management, land cover change is a crucial aspect. The area of tropical savanna environments is vulnerable to land degradation. This study aimed to rapidly detect land cover changes in a tropical savanna environment based on remote sensing data. Conditional change detection was performed using the Change Vector Analysis (CVA) with input parameters such as the Enhanced Vegetation Index (EVI) and Normalized Difference Soil Index (NDSI). The results showed that during the period 2015 to 2019, changes were detected in the Moyo watershed every year. From 2015 to 2016, the Moyo River Basin was dominated by changes with a change magnitude of less than 0.088, which was 63% of the Moyo River Basin area. From 2016 to 2017, the changes were dominated by the change magnitude value of 0.063, which was 58.6% of the Moyo River Basin area. From 2017 to 2018, changes were dominated by the change magnitude value of 0.084 of 55.26% of the Moyo watershed area. From 2018 to 2019, the change was dominated by the change magnitude value of 0.057, which was 47.57% of the Moyo watershed area. The direction of land cover change was dominated by Q2 in 2016, Q4 in 2017 and 2018, and Q2 and Q4 in 2019. These changes generally occurred in the Moyo watershed middle and downstream parts, which are grasslands. The use of the Conditional Change Vector Analysis (CCVA) approach in a tropical savanna environment can detect changes and the direction of change with an accuracy of about 70%.

Application of geospatial indices for mapping land cover/use change detection in a mining area

Mining contributes significantly to the GDP of South Africa but it also comes with some adverse impacts on the environment. In this study, remote sensing was used to quantify land-use/cover changes in the Welkom – Virginia Goldfields. The aim was to analyse Landsat images with a 5-year interval from 1988 to 2018 using geospatial indices: Global Environmental Monitoring Index (GEMI), the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Soil Index (NDSI) and the Normalized Difference Water Index (NDWI) to discriminate different land cover types. Supervised classification with the maximum likelihood method was used to classify the images into appropriate classes. Findings revealed different land-use changes with fluctuations in values for each index with an overall accuracy of the classified images ranging from 88% to 96% respectively. Hence these indices are reliable for mapping and monitoring land use/cover changes in mining areas over a large extent.

Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise

Recent evolutions of the geospatial technologies are more accurate in mapping and monitoring land use land cover, LULC, in different environments and at different spatial scales. However, some urban applications keep facing issues such as misclassification and other noise in unplanned cities with disorganized built-up and mixed housing material, and surrounded by a composed biophysical environment. This paper reports the processing leading to a new spectral index, that balances the land surface brightness temperature and spectral reflectance to accurately extract the built-up. The namely Brightness Adjusted Built-up Index, BABI, is proposed as a weighted ratio of Landsat OLI-TIRS bands. The methodology is based on a multi-perceptron layers, MLP, regression between a classified image and individually classified red, SWIR1, SWIR2 and TIR bands reclassified “1 = built-up; 0 = Non-Built-up”, with an average r2=0.78. The same way, a linear regression of popular built-up spectral indices such as Normalized Difference Built-up Index, NDBI, and Urban Index, UI, or recently proposed Modified New Built-up Index, MNBI, and Normalized Difference Built-up and Surroundings Unmixing Index, NDBSUI, on one hand, by light-dark spectral indices such as, Normalized Difference Soil Index, NDSI, Bare Soil Index, BSI, and Shadow index on the other hand, stands for the natural environment noise assessment in and around the built-up, with an r2=0.75. The MLP r2 standing for the built-up information, is rounded to 0.8 and according to their rank in the process, the weights allotted are 0.2, 0.4 and 0.8 in the numerator, and inversely 0.8, 0.6 and 0.2 in the denominator, to the red, SWIR1 and SWIR2 bands respectively. Whereas, the simple linear regression r2 standing for the noise is used to weigh the brightness temperature, TB in the numerator and subtracted from the previous group. The value 0.001 multiplies the whole ratio to lower the decimals of the outputs for an easy interpretation. As results, on the floating images scaled [0-1], built-up values are ≥0.1 in Yaounde (Cameroon) and ≥0.07 in Bangui (Central African Republic). The overall accuracies are 96% in Yaounde and 98.5% in Bangui, with corresponding kappa coefficients of 0.94 and 0.97. These scores are better than those of the NDBI, UI, MNBI and NDBSUI.

Water Benefit-Based Ecological Index for Urban Ecological Environment Quality Assessments

Urbanization and climate change cause the urban ecological environment to become increasingly dependent on water. However, open water areas and green spaces in cities are constantly decreasing, making water resources increasingly scarce. There is an urgent need for a method that aligns with the current urban status and can quickly assess the urban ecological–environmental quality (UEEQ). Traditional UEEQ methods have abandoned the water factor, neglecting the influence of water on the ecological environment. In modern cities, water, which guarantees the operation and maintenance of the urban ecological environment, must be considered in the UEEQ system. Therefore, we propose a water benefit-based ecological index (WBEI). In the formulation of the WBEI, we integrate a water ecofactor, the thermal environment, and the land cover type to represent the surface ecological environment. We first construct a surface potential water abundance index (SPWI) to describe the spatial distribution of water. The combination of the SPWI and the normalized difference latent heat index allows the WBEI to better evaluate the UEEQ around water areas. Then, we choose the land-surface temperature to represent the thermal environment. To represent the land cover type, the ratio vegetation index and the normalized difference soil index are adopted in the WBEI. Finally, we use an entropy-based fusion method to fuse these indicators and obtain the WBEI values. The performance of the WBEI is tested using eight datasets with a variety of environmental characteristics. The results show that 75% of the WBEI results are consistent with the EI values. The correlation coefficient between the WBEI and EI is 0.8883, which is significantly better than those of the other methods. The research shows that the UEEQ of the Qingdao West-Coast Economic New Zone is declining continuously at a rate of 3.7% per year. From 2013 to 2017, the percentage of areas with good environments decreased by 21.46%, and the percentage of areas with poor environments increased by 12.76%. The UEEQ inside the city deteriorated radially outward along the main traffic route, the UEEQ in the suburbs did not change significantly, and the UEEQ in the water areas deteriorated significantly. These relevant research results can provide quantitative information for the green sustainable development of cities.

Assessing the degree of soil salinity in the Indian Sundarban Biosphere Reserve using measured soil electrical conductivity and remote sensing data–derived salinity indices

The Sundarban Biosphere Reserve (SBR) in India is vulnerable to soil salinity issues arising out of the occurrence of regular floods and storm surges. While much research has investigated various geo-hazards in this Reserve, the soil salinity aspect has remained underemphasized. This study assesses the degree of soil salinity and its spatial distribution in the SBR, using remote sensing and field measured datasets. Eight soil salinity indices extracted from satellite images were statistically correlated with measured electrical conductivity values using the Pearson correlation coefficient. Through this, the Salinity Index (SI–3) and the Normalized Difference Soil Index (NDSI) were determined as the most suitable indices to map the SBR’s soil salinity. The spatial analysis of these indices revealed that the administrative Blocks of Patharpratima, Basanti, Kultali, Sagar, Sandeshkhali-I, Gosaba, and Haroa experienced extremely high level of soil salinity and salt intrusion. The high drainage density and close proximity of these blocks to rivers fosters such high soil salinity. The study calls for efficacious policy measures to lessen the effects of soil salinity on the subsistence prospects of the coastal communities. The methodology adopted in this study can be utilized for similar analysis in other saline-affected areas at various spatial scales.

A novel method to quantify urban surface ecological poorness zone: A case study of several European cities

A set of factors cause the Surface Ecological Status (SES) of urban areas to become largely different from the surrounding rural areas. Hence, the degree of poorness of SES in urban areas versus surrounding rural areas forms a zone, which is named Urban Surface Ecological Poorness Zone (USEPZ). The main objective of this study was to propose a new method to quantify USEPZ Intensity (USEPZI). To this end, Landsat-8 satellite images, water vapor products, and High Resolution Imperviousness Layer (HRIL) datasets of Budapest, Bucharest, Ciechanow, Hamburg, Lyon, Madrid, Porto, and Rome cities were used. Firstly, Single Channel (SC) algorithm, Tasseled cap transformation, and spectral indices were used to model the surface biophysical characteristics including Land Surface Temperature (LST), Wetness, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Soil Index (NDSI). Then, SES was modeled based on the combination of surface biophysical characteristics using Remote Sensing-based Ecological Index (RSEI). Finally, the USEPZI was modeled based on the linear regression function obtained from RSEI-Impervious Surface Percentage (ISP) feature space. The spatial variability of the ISP, LST, NDVI, NDSI and Wetness of the selected cities was found to be heterogeneous. The coefficient of determination (R2) between RSEI and ISP values for Budapest, Bucharest, Ciechanow, Hamburg, Lyon, Madrid, Porto, and Rome cities were obtained to be 0.99, 0.97, 0.98, 0.99, 0.98, 0.99, 0.99, and 0.94, respectively. Also, the USEPZI values of these cities were 0.14, 0.31, 0.41, 0.26, 0.40, 0.81, 0.44 and 0.46, respectively. Our findings show that the significant differences in their SES and USEPZI are due to the surface biophysical characteristics. The USEPZI in the selected cities with humid climate conditions was higher than the selected cities in dry climate conditions. Also, the use of the RSEI-ISP feature space is quite useful in modeling USEPZI of cities in different conditions.

Ecological impact assessment method of highways in Tibetan Plateau: A Case study of Gonghe-Yushu Expressway

In recent years, the ecological environment along highways in Tibetan Plateau has been severely affected due to the rapid construction of highways. In order to solve the problems of multiple indicators and inconsistent criteria in the ecological impact assessment of highways, and to scientifically screen assessment indicators, the paper proposes a multi-round indicator screening method, which combines literature analysis, expert rating, and statistical analysis. Based on this screening method, normalized difference vegetation index, land surface temperature, elevation, and normalized difference soil index are screened out. Combined with multiple linear regression, an ecological impact assessment model is established and applied to ecological impact assessment of Gonghe-Yushu Expressway. The results show that the expressway construction is the first driving force for the deterioration of the ecological environment along the roadside, and its interference range on the desert grassland ecosystem is greater than that on the agroforestry system. The ecological environment within 150 m on both sides of the expressway should be protected.

Remote Sensing Evaluation of Urban Ecological Changes in Arid Region Oasis: A Case Study in Liangzhou District of Wuwei, China

The ecological environment quality of the oasis city in Liangzhou District of Wuwei City in arid region from 2000 to 2016 is evaluated by using RSEI, which integrates four ecological evaluation indicators (greenness, humidity, dryness, and heat). The results show that the ecological index of remote sensing based on principal component analysis can objectively quantify regional ecological changes; the overall ecological environment quality in Liangzhou District of Wuwei is slightly improved, the average RSEI raised from 0.447 to 0.458, and the overall increased 2.46%. Liangzhou District Eco-environmental Quality Grades from Medium to Two Polarized Development. The large-scale cultivation of arable land in the northeastern desert area has improved the ecological environment. The expansion of urban areas and the construction of new areas in the central region have reduced the quality of the ecological environment. The NDSI (Normalized difference soil index) which represents the degree of dryness has the largest contribution to the RSEI, indicating that the decline in the quality of the ecological environment is closely related to the increasing impervious surface.

Remotely Sensed Urban Surface Ecological Index (RSUSEI): An Analytical Framework for Assessing the Surface Ecological Status in Urban Environments

Urban Surface Ecological Status (USES) reflects the structure and function of an urban ecosystem. USES is influenced by the surface biophysical, biochemical, and biological properties. The assessment and modeling of USES is crucial for sustainability assessment in support of achieving sustainable development goals such as sustainable cities and communities. The objective of this study is to present a new analytical framework for assessing the USES. This analytical framework is centered on a new index, Remotely Sensed Urban Surface Ecological index (RSUSEI). In this study, RSUSEI is used to assess the USES of six selected cities in the U.S.A. To this end, Landsat 8 images, water vapor products, and the National Land Cover Database (NLCD) land cover and imperviousness datasets are downloaded for use. Firstly, Land Surface Temperature (LST), Wetness, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Soil Index (NDSI) are derived by remote sensing methods. Then, RSUSEI is developed by the combination of NDVI, NDSI, Wetness, LST, and Impervious Surface Cover (ISC) with Principal Components Analysis (PCA). Next, the spatial variations of USES across the cities are evaluated and compared. Finally, the association degree of each parameter in the USES modeling is investigated. Results show that the spatial variability of LST, ISC, NDVI, NDSI, and Wetness is heterogeneous within and between cities. The mean (standard deviation) value of RSUSEI for Minneapolis, Dallas, Phoenix, Los Angeles, Chicago and Seattle yielded 0.58 (0.16), 0.54 (0.17), 0.47 (0.19), 0.63 (0.21), 0.50 (0.17), and 0.44 (0.19), respectively. For all the cities, PC1 included more than 93% of the surface information, which is contributed by greenness, moisture, dryness, heat, and imperviousness. The highest and lowest mean values of RSUSEI are found in “Developed, High intensity” (0.76) and “Developed, Open Space” (0.35) lands, respectively. The mean correlation coefficient between RSUSEI and LST, ISC, NDVI, NDSI, and Wetness, is 0.47, 0.97, −0.31, 0.17, and −0.27, respectively. The statistical significance of these correlations is confirmed at 95% confidence level. These results suggest that the association degree of ISC in USES modeling is the highest, despite the differences in land cover and biophysical characteristics in the cities. RSUSEI could be very useful in modeling and comparing USES across cities with different geographical, climatic, environmental, and biophysical conditions and can also be used for assessing urban sustainability over space and time.

Integrated ecological index (IEI) for urban ecological status based on remote sensing data: a study at Semarang – Indonesia

The sustainability of a city will be greatly influenced by its urban ecological status. Remote sensing technology is very supportive for understanding the urban ecological status with advantages in terms of timely, precise and objective, so that has become an increasing concern in the world. In this paper, urban ecological status of Semarang city was analysed by an image of Landsat-8 OLI was acquired in a different season. The urban ecological status is integrated by two main components that are assumed that forming urban land, that is the primary land surface component (i.e. built-up area and vegetation area) and the climate (i.e. land surface humidity and dryness degree). These components are divided into four indicators obtained from the transformation of remote sensing data, which is the NDBI (normalized difference build-up index) for built areas representing the resident aggregation degree, SAVI (soil adjusted vegetation index) for greenness degree, NDMI (normalized difference moisture index) for land surface moisture and NDSI (normalized difference soil index) for the dryness degree. Principal component analysis (PCA) was utilized to compress the four indicators into one to construct the index for assessing overall urban ecological status. The results showed that there were differences in the urban ecological status in different seasons. Urban ecological conditions in the dry season are more varied than in the rainy season. Overall urban ecological conditions in the rainy season are better than the dry season.

Effect of Urban Expansion Intensity on Urban Ecological Status Utilizing Remote Sensing and GIS: A Study of Semarang-Indonesia

The rapid urban expansion will drive land-use conversion and cause substantial environmental and ecological impacts. The ability of remote sensing to record spatial and temporal data on the land surface with complete coverage has been proven effective for recognizing spatiotemporal changes in the regional eco-environment quickly. This study utilized Landsat TM / ETM + and Landsat-8 OLI remote sensing data to assess the effect of urban expansion intensity on the urban ecological status with the study area in Semarang, Indonesia. The method to measure urban expansion uses the urban expansion intensity index and to assess the urban ecological status using an integrated ecological index (IEI). The IEI generated from four parameters which include the degree of greenness (i.e. the soil adjusted vegetation index, SAVI), the moisture degree (i.e. the normalized difference moisture index, NDMI), dryness degree (i.e. the normalized difference soil index, NDSI) and built-up aggregation degree (i.e. the normalized difference built-up index, NDBI). Principal component analysis (PCA) is used to compress the four indicators to construct the index of ecological status and Pearson correlation analysis was used to determine the effect of urban expansion intensity on urban ecological status. The result showed that the urban land area increased by 38.98 km2 with an average expansion area of 3.9 km2 y-1 from 2005 to 2015. The index of urban expansion intensity during 2011-2015 increased about 2.1 times from 2005-2011. The pattern of deteriorating urban ecological status of the city of Semarang spreading towards the west, south, southeast, and east. The lowest integrated ecological index is in the central and northern parts of the city of Semarang. The extent of good and excellent urban ecological status continues to decline in a decade, on the contrary, the urban ecological status of poor and extremely poor increased. The negative linear relationship of the index of urban expansion intensity and urban ecological status shows that the intensity of urban expansion influences the urban ecological status in Semarang city.

Multi-temporal remote sensing data to monitor terrestrial ecosystem responses to climate variations in Ghana

Operational monitoring of vegetation and its response to climate change involves the use of vegetation indices (VIs) in relation to relevant climatic data. This study analyses the temporal variations of vegetation indices in response to climatic data (temperature and precipitation) to better understand the phenological changes in the Wa-West and Tolon districts of Ghana during 1999–2011. This study also examines the inter-annual variation of vegetation indices and lag effects of climate variables (temperature and precipitation) using simple regression and correlation approaches. Results indicate that the mean Normalized Difference Vegetation Index (NDVI) and Normalized Difference Soil Index (NDSI) were significantly correlated with the mean temperature, whereby the value of NDVI increases with a decrease in temperature and value of NDSI increases with an increase in temperature. On examining seasonal variations, our findings indicated that the months of August and September have the highest mean NDVI values. This study confirms that consistently rising temperature and altered precipitation patterns have exerted a strong influence on temporal distributions and productivities of the terrestrial ecosystems of the Tolon and Wa-West districts of Ghana. Furthermore, this research demonstrates how vegetation indices can be used as an indicator to monitor phenological changes in the terrestrial ecosystem.