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Chlorophyll–a (Chl–a) concentration is an indicator of phytoplankton pigment, which is associated with the health of marine ecosystems. A commonly used method for the determination of Chl–a is satellite remote sensing. However, due to cloud cover, sun glint and other issues, remote sensing data for Chl–a are always missing in large areas. We reconstructed the Chl–a data from MODIS and VIIRS in the Arabian Sea within the geographical range of 12–28° N and 56–76° E from 2020 to 2021 by combining the Data Interpolating Convolutional Auto–Encoder (DINCAE) and the Bayesian Maximum Entropy (BME) methods, which we named the DINCAE–BME framework. The hold–out validation method was used to assess the DINCAE–BME method's performance. The root–mean–square–error (RMSE) and the mean–absolute–error (MAE) values for the hold–out cross–validation result obtained by the DINCAE–BME were 1.8824 mg m−3 and 0.4682 mg m−3, respectively; compared with in situ Chl–a data, the RMSE and MAE values for the DINCAE–BME–generated Chl–a product were 0.6196 mg m−3 and 0.3461 mg m−3, respectively. Moreover, DINCAE–BME exhibited better performance than the DINEOF and DINCAE methods. The spatial distribution of the Chl–a product showed that Chl–a values in the coastal region were the highest and the Chl–a values in the deep–sea regions were stable, while the Chl–a values in February and March were higher than in other months. Lastly, this study demonstrated the feasibility of combining the BME method and DINCAE. [ABSTRACT FROM AUTHOR]

Pressure on the Wiang Nong Lom and Nong Luang wetland resources in the Chiang Saen Valley of Chiang Rai Province (Thailand) has increased in recent years with the expansion of farmlands and other major sources of wetland conflict related to public land encroachment. Both of these wetlands have been designated as strategic ecosystems. Yet, there is a limited understanding of the way different wetlands respond to change drivers (agriculture, climate, population, etc.), and currently no scientifically valid protocols exist for local wetland mapping and monitoring. Distinguishing between small wetlands and land use and land cover (LULC) components is a challenging affair due to the highly heterogeneous landscape and spectral similarity of compositionally different types of tropical regions. The goals of this article are both technological and substantive, i.e., it aims to (A) propose a synthesis of quantitative techniques that can improve LULC mapping using remotely sensed data (Landsat TM, ETM+, OLI), and (B) assess the wetland changes during the last three decades and better understand the interaction between wetland changes, human population, and the environment. In regards to goal (A), the proposed classification approach employed a synthesis of techniques of decision tree classification (DTC), maximum likelihood classification (MLC), and Mahalanobis distance classification (MDC), with different bands and ancillary data inputs. The results demonstrated that the implementation of the DTC algorithms to address LULC mapping problems exhibited an overall mapping accuracy of 83.9%, which is significantly higher than that of MLC and MDC. It was found that the DTC technique performs well when combined with visible, near-infrared (NIR), and shortwave-infrared bands, a digital elevation model and normalized difference vegetation index layers. Subsequently, the postclassification analysis using DTC showed a notable improvement of approximately 88.0% classification accuracy. Regarding goal (B), our results showed that during the last 30 years, wetland areas in the Chiang Saen Valley have experienced a dramatic decrease of 30.5%, whereas forest landscape surrounding the wetlands has decreased by an astonishing 50.9%. Contrarily, we found that agricultural land size has increased by 24.3%. We suggest that ground data can be linked to the etiology of these results, including the gradual conversion of wetlands to rice cultivation fields as a result of the government rice pledging scheme. Large areas in the study region have been cultivated by para-rubber, palm oil, and pineapple agribusiness production since 2003. In addition, short-term subsidizing government policies promote intensive production for commercial agriculture prompting farmers to transition from subsistence to commercial farming, further impacting wetland conversion. As a result, and in further view of the fact that rapidly expanding agricultural areas have contributed significantly to the decrease of wetland areas during the last three decades, the Chiang Saen Valley wetlands have been designated as wetlands of international importance. The overall recommendation of the present work is that special land-use policy and relevant regulation and/or legislation are critical components of any effort to achieve wetland sustainability. [ABSTRACT FROM AUTHOR]

Remote sensing is the main approach to map aquatic vegetation, and classification tree (CT) is superior to various classification methods. Based on previous studies, modified CT can be developed from traditional CT by adjusting the thresholds based on the statistical relationship between spectral features to classify different images without ground-truth data. However, no studies have yet employed this method to resolve marine vegetation. In this study, three Gao-Fen 1 satellite images obtained on 30 January 2014, 5 November 2014 and 21 January 2015 were selected, and two features were then employed to extract macroalgae farms. Results show that the overall accuracies of traditional CTs for three images are 92.0, 94.2 and 93.9%, respectively, whereas those of the two corresponding modified CTs for images obtained on 21 January 2015 and 5 November 2014 are 93.1 and 89.5%, respectively. This indicates modified CTs can map macroalgae with multi-date imagery and monitor their spatiotemporal distribution in coastal environments. [ABSTRACT FROM AUTHOR]

Abstract: Real time and spatially distributed T a (air temperature) data are desired for many applications. T s (land surface temperature) derived from remote sensors has been used to estimate T a in previous studies. Exploring MODIS Aqua T s and station measured daily maximum and minimum T a over east Africa, we found that T s did not agree well with T a during the day (MAE (Mean Absolute Error)=6.9±5.0°C) but had better agreement during the night (MAE=1.9±1.7°C). A stepwise linear regression method was applied to construct possible models to predict T a based on MODIS data. Our results showed that, only considering elevation, high spatial resolution T a could be obtained by simple linear models, with MAE=1.9°C, agreement index=0.79 for daily maximum T a, and MAE=1.9°C, agreement index=0.92 for daily minimum T a. MODIS T s data could provide temporal variation information and slightly improve the accuracy of model predictions (by 0.2°C of MAE). However, considering (i) major absences (about 2/3 of days) of T s data due to cloud cover and (ii) small T a variations in time (σ =2.1°C) over east Africa, models without T s might be more practical in particular applications such as tsetse fly distribution models. Other variables including solar zenith angle, low level precipitable water content, and vegetation index (NDVI and EVI) were insignificant in the daily maximum and minimum T a estimation models after elevation and T s had already been considered as predictors. [Copyright &y& Elsevier]

Assessing agroecosystem health at landscape scale has been rarely reported. A geospatial assessment framework, by integrating remote sensing, geographical information systems, landscape metrics, geostatistics and catastrophe theory, was proposed and applied to characterize the spatial variations of agroecosystem health for a typical region in the eastern coastal agricultural plain, China. After appropriate pretreatments, eleven hierarchically structured indicators, subject to catastrophe models, were aggregated into an integrated index for each 30 m grid cell across the study area. Great spatial variations in agroecosystem health were identified. Areas covered by water bodies and impervious surfaces, under which soils can not function, generally presented low values. Larger slope or higher heavy metal polluted areas and those near roads showed low or moderate values of agroecosystem health index. Concerning the spatial variations of agroecosystems health, patches of approximately 4 km in diameter were identified, within which more homogenous patterns would be expected. Specifically, the spatial variations of agroecosystems health should be resulted from the location-specific coupled influences of the underlying indicators. The integrated agroecosystem health index is believed to be helpful for managers to promote environmental management. The geospatial assessment framework reported in this paper was simple and operational, and has potential to be applicable to other areas with similar conditions at landscape scale. [ABSTRACT FROM AUTHOR]

Mapping rice cropping areas with optical remote sensing is often a challenge in tropical and subtropical regions because of frequent cloud cover and rainfall during the rice growing season. Synthetic aperture radar (SAR) is a potential alternative for rice mapping because of its all-weather imaging capabilities. The recent Phased Array-type L-band SAR (PALSAR) sensor onboard the Advanced Land Observing Satellite (ALOS) acquires multipolarization and multitemporal images that are highly suitable for rice mapping. In this pilot study, we demonstrate the feasibility of this sensor in mapping the rice planting area in Zhejiang Province, southeast China. High-resolution ALOS/PALSAR images were acquired at three rice growing stages (transplanting, tillering and heading) and were applied in a support vector machine (SVM) classifier to map rice and other land use surfaces. The results show that, based on the 1:10 000 land use/land cover (LULC) survey map, the rice fields can be mapped with a conditional Kappa value of 0.87 and at user's and producer's accuracies of 90% and 76%, respectively. The large commission error primarily came from confusion between rice and dryland crops or orchards because of their similar backscatter amplitudes in the rice growing season. The relatively high rice mapping accuracy in this study indicates that the new ALOS/PALSAR data could provide useful information in rice cropping management in subtropical regions such as southeast China. [ABSTRACT FROM AUTHOR]

Remote sensing reflectance (Rrs) values measured by satellite sensors involve large amounts of uncertainty leading to non-negligible noise in remote Chlorophyll-a (Chl-a) concentration estimation. This work distinguished between two main stages in the case of estimating distributions of Chl-a within the Gulf of St. Lawrence (Canada). At the model building stage, the retrieval algorithm used both in-situ Chl-a measurements and the corresponding Moderate Resolution Imaging Spectroradiometer (MODIS) L2-level data estimated Rrs at 412, 443, 469, 488, 531, 547, 555, 645, 667, 678 nm at a 1 km spatial resolution during 2004–2013. Through the training and validation of various models and Rrs combinations of the considered eight techniques (including support vector regression, artificial neural networks, gradient boosting machine, random forests, standard CI-OC3M, multiple linear regression, generalized addictive regression, principal component regression), the support vector regression (SVR) technique was shown to have the best performance in Chl-a concentration estimation using Rrs at 412, 443, 488, 531 and 678 nm. The accuracy indicators for both the training (850) and the validation (213) datasets were found to be very good to excellent (e.g., the R 2 value varied between 0.7058 and 0.9068). At the space-time estimation stage, this work took a step forward by using the Bayesian maximum entropy (BME) theory to further process the SVR estimated Chl-a concentrations by incorporating the inherent spatiotemporal dependency of physical Chl-a distribution. A 56% improvement was achieved in the reduction of the mean uncertainty of the validation data decreased considerably (from 1.2222 to 0.5322 mg/m3). Then, this novel BME/SVR framework was employed to estimate the daily Chl-a concentrations in the Gulf of St. Lawrence during Jan 1-Dec 31 of 2017 (1 km spatial resolution). The results showed that the daily mean Chl-a concentration varied from 1.6630 to 3.3431 mg/m3, and that the daily mean Chl-a uncertainty reduction of the composite BME/SVR vs. the SVR estimation had a maximum reduction value of 1.0082 and an average reduction value of 0.6173 mg/m3. The monthly spatial Chl-a distribution covariances showed that the highest Chl-a concentration variability occurred during November and that the spatiotemporal Chl-a concentration pattern changed a lot during the period August to November. In conclusion, the proposed BME/SVR was shown to be a promising remote Chl-a retrieval approach that exhibited a significant ability in reducing the non-negligible uncertainty and improving the accuracy of remote sensing Chl-a concentration estimates. • BME and SVR (ML) were combined to retrieve Chl-a in optically complex waters. • SVR shows great ability to retrieve Chl-a using satellite remote sensing data. • Introducing BME can significantly reduce the uncertainty of Chl-a estimation. • The distribution pattern of Chl-a at GSL varies a lot during the summer season. [ABSTRACT FROM AUTHOR]

Abstract: Dynamics of soil sealing and soil landscape patterns under rapid urbanization were qualified in the Urban Agglomeration around Hangzhou Bay (UAHB), China. Rapid population growth, economic development, and settlement sprawl were identified from 1994 to 2009. Such accelerating urbanization resulted in considerable soil sealing. For the entire region, percentage of sealed soils increased from 2.7% in 1994 to 4.7% in 2003 and to 8.7% in 2009. Paddy soils, especially those of the highest quality, were the first victims. Soil landscapes, at the regional scale, became more fragmented and irregular, but less dominated, diverse and connected as urbanization intensified. Spatial regression uncovered the diverging relationships between landscape metrics with urbanization indicators and measurement period. The dynamics of soil sealing and soil landscape patterns not only reflected common tendencies in China, but represented a global process. Our study provided a practical methodological framework, by integrating remote sensing, geographical information systems, landscape metrics and spatial regression, for monitoring the impacts of urbanization on soil resources. [Copyright &y& Elsevier]

Abstract: This study employed geographically weighted regression (GWR), a local spatial modeling approach, to characterize the local varying response of paddy soils to urbanization at the landscape scale in the urban agglomeration around Hangzhou Bay, China. Spatial non-stationary and scale-dependent relationships between three geographical urbanization indices (the common urbanization intensity index, distance to road corridors and distance to urban centers) and four spatial metrics depicting paddy soil landscape patterns (total area, edge density, aggregation index, and perimeter area ratio distribution) were captured at two block scales (4 km × 4 km and 7 km × 7 km) because the predictive ability of GWR and the nature and strength of the identified relationships all varied significantly in space and scale. Moreover, GWR models have achieved better performance than their corresponding global ordinary least squares (OLS) models given their greater ability to (1) describe data (higher adjusted R 2 and lower Akaike Information Criterion values) and (2) address spatial autocorrelation in model residuals. This study highlighted scale effects when applying GWR. Both bandwidth and block size can impact the predictive ability of GWR as well as estimated parameters for GWR analysis. This study demonstrates a novel joint application of GWR and associated metrics in applied geography. [Copyright &y& Elsevier]

The Conservation Reserve Program (CRP) instituted one of the largest and most rapid land use/land cover conversions in US history. Approximately 14.8 million ha (36.5 million acres) of cropland were converted to grassland, woodland, and other conservation uses between 1986 and 1995. As policy makers continue to evaluate the future of the program and as scientists examine its effects, it is critical that the impact of CRP on landscape structure be considered because of its potential influence on wildlife populations. Utilizing multi-seasonal Landsat thematic mapper imagery in an unsupervised classification technique, we produced highly accurate maps of cropland and grassland for 1987 and 1992 for Finney County, Kansas. Post-classification differencing identified regions of cropland that had been converted to CRP. We then used the Finney County CRP map to examine changes in landscape structure caused by the introduction of CRP. Using the fragstats spatial pattern analysis program, we calculated the number of patches, mean patch size, patch density, edge density, mean shape index, nearest neighbor distance, and an interspersion/juxtaposition index. In addition, we calculated total grassland area and percent of area in grassland for the pre- and post-CRP enrollment years. We found that the total grassland area and the percent area in grassland in Finney County increased due to CRP and that mean grassland patch size also increased. The total number of grassland patches decreased, however, due to coalescence of smaller grassland patches. Patch density, edge density, mean shape index, nearest neighbor distance, and the interspersion/juxtaposition index all showed relatively small changes. These small changes appear to reflect geographic differences in CRP effects within the county—large aggregating patches in the northeast were offset by a number of isolated patches of CRP in other areas. The implication of these findings for wildlife managers is that, for species that require large areas of grassland habitat, especially habitat that is contiguous, CRP in Finney County represents a substantial increase in potential habitat. This holds for species at all levels of management interest, ranging from economically valuable species to species that are rare, threatened, and endangered. These findings emphasize the importance of CRP for wildlife conservation and should further inform ongoing debate concerning the importance of the CRP. [Copyright &y& Elsevier]