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Accurate and high-resolution crop yield and crop water productivity (CWP) datasets are required to understand and predict spatiotemporal variation in agricultural production capacity; however, datasets for maize and wheat, two key staple dryland crops in China, are currently lacking. In this study, we generated and evaluated a long-term data series, at 1-km resolution of crop yield and CWP for maize and wheat across China, based on the multiple remotely sensed indicators and random forest algorithm. Results showed that MOD16 products are an accurate alternative to eddy covariance flux tower data to describe crop evapotranspiration (maize and wheat RMSE: 4.42 and 3.81 mm/8d, respectively) and the proposed yield estimation model showed accuracy at local (maize and wheat rRMSE: 26.81 and 21.80%, respectively) and regional (maize and wheat rRMSE: 15.36 and 17.17%, respectively) scales. Our analyses, which showed spatiotemporal patterns of maize and wheat yields and CWP across China, can be used to optimize agricultural production strategies in the context of maintaining food security. Measurement(s) crop yield and crop water productivity Technology Type(s) remote sensing and machine learning [ABSTRACT FROM AUTHOR]

The present study assesses the spatio-temporal dynamics of land use/cover over a 28-year period in the upper Tana River Basin (TRB), Kenya using digital change detection techniques. The results indicate that during the last three decades, agricultural land and built-up area have increased by 32.57% (184,796 ha) and 26.35% (1460 ha) respectively, while open land, waterbodies and vegetation have decreased by 35.9%, 3.13% and 8.29% respectively. There was a huge expansion of agricultural land to marginal semi-arid and arid areas (lower part of the basin) over the period. The results of this study provide a better understanding of the spatial and temporal dynamics of the natural resources and form a basis for better planning and effective spatial organization. Such information can help various stakeholders including policy decision-makers in balancing development needs and river basin vital environmental systems protection and sustainability, especially in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

The western region of Kenya is experiencing remarkable land changes resulting from population growth and related impacts. The study used remote sensing and GIS techniques to analyze the land use/cover changes in the years 1995, 2001, 2010 and 2017. Multi-spectral Landsat (TM, ETM + and OLI) images were pre-processed and classified using maximum likelihood algorithm in ENVI version 5.4. The overall classification accuracies in all the images were more than 80%. The results revealed major conversions of each land use/land cover type in varying trends and magnitudes. Between 1995 and 2001, there was an increase in built-up areas by 71%, forest cover by 43%, farms by 5%; and decrease in grassland by 47%. By 2017, the built-up areas had increased by 225% and farms by 17%; the forestland, grassland and water reduced by 38, 10 and 11%, respectively. The observed changes are characterized by increased settlements and encroachment of sensitive ecosystems. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A high-resolution multispectral image was used to study the reflectance of Dubas bug infestation. • The reflectance decreased in the red edge and near-infrared bands as the infestation increased. • Nineteen out of 32 vegetation indices showed a significant correlation with infestation. • The locations have an effect on the relation between the infestation and the vegetation indices. • Maximum likelihood classification approach results in a fairly good detection level for the infestation. Abstract The Dubas bug, Ommatissus lybicus de Bergevin, is one of the major pests of the date palm, Phoenix dactylifera , in Oman, reducing its production by 28%. In addition to the important annual costs to control this pest nationwide, effort, cost and time are spent surveying and spotting O. lybicus infestations. Several studies have indicated the possibility of using remote sensing technology to identify plants stressed by pest infestation. The aim of the present study is to detect O. lybicus infestations by quantifying reflectance changes of different infestation levels and calculating different vegetation indices (VIs) using high-resolution multispectral (MS) images. An image of an area with different sub-locations that had varying levels of infestation was acquired in March 2017 using the WorldView-3 satellite. The reflectance of 8 bands, 32 spectral VIs and maximum likelihood classification (MLC) were derived from the image, and then the correlation was tested using ground-infestation data. The results revealed that the reflectance decreased in the red edge and near-infrared (NIR) bands as the infestation level increased. High levels of infestation showed a significant difference in three bands, red edge, NIR1 and NIR2, compared to no, low and medium levels of infestation. Nineteen out of 32 VIs showed a significant relation with the infestation levels. The relation ranged between r = −0.12, p < 0.05 using the Normalized Difference Mud Index (NDMI) and r = −0.39, p < 0.000 using the Transformed Difference Vegetation Index (TDVI)) and Tasselled Cap – Non - Such Index (TC-NSI). The location affected the relation between the infestation and VIs, where the correlation coefficients increased. The maximum correlation found was r = 0.64 using the Visible Atmospherically Resistant Index (VARI) in Al'Ayn Village. The overall accuracy of the supervised classification for detecting the infestation level was 68.3%, and the Kappa coefficient was 0.50. [ABSTRACT FROM AUTHOR]

Abstract River channel dynamics are natural autogenic occurrences for fluvial rivers with influences from human modifications and climatic factors. Remote sensing and geographic information system tools and techniques, aerial photographs, and satellite imagery have been used to determine epochal channel erosion, accretion, and unchanged locations along Tana River, Kenya's longest river. Six reaches within a 142-km Saka-Mnazini stretch were studied by comparing sequential changes in the position of the channel in 1975–1986, 1986–2000, 2000–2017, and 1975–2017 epochs. Manual and automatic digital processing procedures and GIS tools were applied to visualize and quantify the reach-wise spatial and temporal morphological changes. The erosion and accretion channel changes over the study period were observed and quantified at all reaches. Meandering and switching off or abandoning the main active channel was also illustrated. The potential driving forces of morphological changes included varying hydrological regime, upstream land use practices, nature of channel gradient, and riparian vegetation occurrence changes. We found no clear evidence to link river regulation with the river channel dynamics. Results deliver the latest evidence on the dynamics of Tana River. This information is crucial for understanding river evolution characteristics and aid in planning and management at the lower reaches which has remained poorly understood. Use of remote sensing data in concert with GIS provides efficient and economical quantitative spatial and temporal analysis of river channel changes. Highlights • River channel dynamics at a floodplain reach using remote sensing and GIS tools • Erosion/accretion vulnerability of Tana River is higher at lower reaches. • The Tana River exhibits dynamic channel behaviour, both, before and after regulation. • Reach-wise historical morphological dynamics guide river engineering and planning. [ABSTRACT FROM AUTHOR]

Date palm trees, Phoenix dactylifera, are the primary crop in Oman. Most date palm cultivation is under the traditional agricultural system. The plants are usually under dense planting, which makes them prone to pest infestation. The main pest attacking date palm crops in Oman is the Dubas bug Ommatissus lybicus. This study integrated modern technology, remote sensing and geographic information systems to determine the number of date palm trees in traditional agriculture locations to find the relationship between date palm tree density and O. lybicus infestation. A local maxima method for tree identification was used to determine the number of date palm trees from high spatial resolution satellite imagery captured by WorldView-3 satellite. Window scale sizes of 3, 5 and 7 m were tested and the results showed that the best window size for date palm trees number detection was 7 m, with an overall estimation accuracy 88.2%. Global regression ordinary least square (OLS) and local geographic weighted regression (GWR) were used to test the relationship between infestation intensity and tree density. The GWR model showed a good positive significant relationship between infestation and tree density in the spring season with R2 = 0.59 and medium positive significant relationship in the autumn season with R2 = 0.30. In contrast, the OLS model results showed a weak positive significant relationship in the spring season with R2 = 0.02, p < 0.05 and insignificant relationship in the autumn season with R2 = 0.01, p > 0.05. The results indicated that there was a geographic effect on the infestation of O. lybicus, which had a greater impact on infestation severity, and that the impact of tree density was higher in the spring season than in autumn season. [ABSTRACT FROM AUTHOR]

Timely and accurate estimation of crop yield before harvest to allow crop yields management decision-making at a regional scale is crucial for national food policy and security assessments. Modeling dynamic change of crop growth is of great help because it allows researchers to determine crop management strategies for maximizing crop yield. Remote sensing is often used to provide information about important canopy state variables for crop models of large regions. Crop models and remote sensing techniques have been combined and applied in crop yield estimation on a regional scale or worldwide based on the simultaneous development of crop models and remote sensing. Many studies have proposed models for estimating canopy state variables and soil properties based on remote sensing data and assimilating these estimated canopy state variables into crop models. This paper, firstly, summarizes recent developments of crop models, remote sensing technology, and data assimilation methods. Secondly, it compares the advantages and disadvantages of different data assimilation methods (calibration method, forcing method, and updating method) for assimilating remote sensing data into crop models and analyzes the impacts of different error sources on the different parts of the data assimilation chain in detail. Finally, it provides some methods that can be used to reduce the different errors of data assimilation and presents further opportunities and development direction of data assimilation for future studies. This paper presents a detailed overview of the comparative introduction, latest developments and applications of crop models, remote sensing techniques, and data assimilation methods in the growth status monitoring and yield estimation of crops. In particular, it discusses the impacts of different error sources on the different portions of the data assimilation chain in detail and analyzes how to reduce the different errors of data assimilation chain. The literature shows that many new satellite sensors and valuable methods have been developed for the retrieval of canopy state variables and soil properties from remote sensing data for assimilating the retrieved variables into crop models. Additionally, new proposed or modified crop models have been reported for improving the simulated canopy state variables and soil properties of crop models. In short, the data assimilation of remote sensing and crop models have the potential to improve the estimation accuracy of canopy state variables, soil properties and yield based on these new technologies and methods in the future. [ABSTRACT FROM AUTHOR]

Rapid and unplanned urbanization, together with climate change, have exacerbated flood risk which has caused devastating loss of human life and property in Ho Chi Minh City, Vietnam. Our study utilized remote sensing techniques combined with Geographic Information Systems-based hydrological modelling to identify flood risk in this urban area. QuickBird imagery was used to create land-use/land-cover information, an important input into the U.S. Soil Conservation Service Technique Release 55 (SCS TR-55) model which is used for predicting rainfall-induced flood. Tidal floods were examined using a Digital Elevation Model in a GIS framework with water level in rivers as an input. The findings indicated that rainfall-induced flood is not a serious problem with the flood depth of 2–10 cm while tidal flood is a substantial issue with 10–100 cm flood depths. Increasing impervious surfaces and decreasing flow length areas resulting from the growth of urbanization in combination with tidal effects contributed significantly to increased flood risk. These findings have implications on solutions for flood risk control in the district, including managing urbanization processes with appropriate infrastructure and improving the infiltration capacity of the runoff with optimized drainage systems. [ABSTRACT FROM AUTHOR]

The main aim of this review paper is to evaluate and make recommendations on how current and emerging remote sensing (RS) technology might be best used to improve vegetation condition assessment and monitoring. This research reviews the vegetation attributes used in various approaches to vegetation condition assessment, the most efficient and rapid techniques to assess those attributes, and proposes applicable suggestions for future vegetation condition assessment using fusion and ensemble techniques. The attributes are those that have strong correlations with components of vegetation condition and are expected to produce trustable indications of change. Further to this, it aims to identify those vegetation attributes that can be best assessed using field survey and those that can be remotely measured world-wide. Vegetation has various structural, functional and compositional characteristics. To measure specific vegetation characteristics, the suitable type of RS sensor is required. Multi-spectral, hyperspectral, Radio Detection And Ranging (RADAR) and Light Detection And Ranging (LiDAR) are the main types of RS sensors, and each type has a range of applications. A variety of automated and repeatable methods are provided by RS technology to monitor the indicators of vegetation condition. However, dependency on site-based data remains. Further work is essential to find a rapid, cost effective and transferable RS method to map and monitor vegetation condition. Moreover, near future improvements in RS, such as Sentinel products, are expected to ease the process of vegetation condition assessment and enhance the outcomes. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which the editor discusses articles in the issue on topics including importance of remote sensing in biomass estimation and mapping across different spatial scales, use of in-situ spatiotemporal biomass production and hyperspectral data for estimation.

Mangrove ecosystems are complex in nature. For monitoring the impact of climate variability in this ecosystem, a multidisciplinary approach is a prerequisite. Changes in temperature and rainfall pattern have been suggested as an influential factor responsible for the change in mangrove species composition and spatial distribution. The main aim of this study was to assess the relationship between temperature, rainfall pattern and dynamics of mangrove species in the Sundarbans, Bangladesh, over a 38 year time period from 1977 to 2015. To assess the relationship, a three stage analytical process was employed. Primarily, the trend of temperature and rainfall over the study period were identified using a linear trend model; then, the supervised maximum likelihood classifier technique was employed to classify images recorded by Landsat series and post-classification comparison techniques were used to detect changes at species level. The rate of change of different mangrove species was also estimated in the second stage. Finally, the relationship between temperature, rainfall and the dynamics of mangroves at species level was determined using a simple linear regression model. The results show a significant statistical relationship between temperature, rainfall and the dynamics of mangrove species. The trends of change for Heritiera fomes and Sonneratia apelatala show a strong relationship with temperature and rainfall, while Ceriops decandra shows a weak relationship. In contrast, Excoecaria agallocha and Xylocarpus mekongensis do not show any significant relationship with temperature and rainfall. On the basis of our results, it can be concluded that temperature and rainfall are important climatic factors influencing the dynamics of three major mangrove species viz. H. fomes, S. apelatala and C. decandra in the Sundarbans. [ABSTRACT FROM AUTHOR]

The Sundarbans mangrove forest is an important resource for the people of the Ganges Delta. It plays an important role in the local as well as global ecosystem by absorbing carbon dioxide and other pollutants from air and water, offering protection to millions of people in the Ganges Delta against cyclone and water surges, stabilizing the shore line, trapping sediment and nutrients, purifying water, and providing services for human beings, such as fuel wood, medicine, food, and construction materials. However, this mangrove ecosystem is under threat, mainly due to climate change and anthropogenic factors. Anthropogenic and climate change-induced degradation, such as over-exploitation of timber and pollution, sea level rise, coastal erosion, increasing salinity, effects of increasing number of cyclones and higher levels of storm surges function as recurrent threats to mangroves in the Sundarbans. In this situation, regular and detailed information on mangrove species composition, their spatial distribution and the changes taking place over time is very important for a thorough understanding of mangrove biodiversity, and this information can also lead to the adoption of management practices designed for the maximum sustainable yield of the Sundarbans forest resources. We employed a maximum likelihood classifier technique to classify images recorded by the Landsat satellite series and used post classification comparison techniques to detect changes at the species level. The image classification resulted in overall accuracies of 72%, 83%, 79% and 89% for the images of 1977, 1989, 2000 and 2015, respectively. We identified five major mangrove species and detected changes over the 38-year (1977-2015) study period. During this period, both Heritiera fomes and Excoecaria agallocha decreased by 9.9%, while Ceriops decandra, Sonneratia apelatala, and Xylocarpus mekongensis increased by 12.9%, 380.4% and 57.3%, respectively. [ABSTRACT FROM AUTHOR]

Urban growth is a serious threat and challenge worldwide due to its role in altering ecosystem processes and contributing to negative environmental impacts. The natural environment of Saudi Arabia has been affected by the increased pace of urban and economic development, which has been supported by large oil revenues in recent years. Measuring the spatiotemporal patterns of urban growth is important to better understand the qualitative and quantitative impacts of urban spatial distribution over periods of time. Optical remote sensing can be a reliable data source that provides valuable information regarding the spatial and temporal distributions of urban growth. This research used two sets of Landsat images from 1985 and 2014 to map and monitor the spatial distribution of the urban extent among five Saudi Arabian cities: Riyadh, Jeddah, Makkah, Al-Taif and Eastern Area. A decision tree classifier was applied using object-based image analysis (OBIA) to analyze urban land cover in the five cities. The accuracy assessment of the urban change detection maps indicated a high overall accuracy and Kappa coefficient. The results of this research show a high rate of urbanization and complex dynamics across the five cites. The significant changes were the result of a rapid increase in land development, exhibiting complex patterns in the urbanization process across the five cities. The government's policy and increased oil revenues significantly contributed to increasing the urban cover in the five selected cities. [ABSTRACT FROM AUTHOR]

Urban area expansion is one of the most critical types of worldwide change, and most urban areas are experiencing increased growth in population and infrastructure development. Urban change leads to many changes in the daily activities of people living within an affected area. Many studies have suggested that urbanization and crime are related. However, they focused particularly on land uses, types of land use, and urban forms, such as the physical features of neighbourhoods, roads, shopping centres, and bus stations. Understanding the correlation between urban area expansion and crime is very important for criminologists and urban planning decision-makers. In this study, we have used satellite images to measure urban expansion over a 10-year period and tested the correlations between these expansions and the number of criminal activities within these specific areas. The results show that there is a measurable relationship between urban expansion and criminal activities. Our findings support the crime opportunity theory as one possibility, which suggests that population density and crime are conceptually related. We found the correlations are stronger where there has been greater urban growth. Many other factors that may affect crime rate are not included in this paper, such as information on the spatial details of the population, city planning, economic considerations, the distance from the city centre, neighbourhood quality, and police numbers. However, this study will be of particular interest to those who aim to use remote sensing to study patterns of crime. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of four driving forces, including elevation, slope, distance to drainage and distance to major roads, on urban expansion in five Saudi Arabian cities: Riyadh, Jeddah, Makkah, Al-Taif and Eastern Area. The prediction of urban probabilities in the selected cities based on the four driving forces is generated using a logistic regression model for two time periods of urban change in 1985 and 2014. The validation of the model was tested using two approaches. The first approach was a quantitative analysis by using the Relative Operating Characteristic (ROC) method. The second approach was a qualitative analysis in which the probable urban growth maps based on urban changes in 1985 is used to test the performance of the model to predict the probable urban growth after 2014 by comparing the probable maps of 1985 and the actual urban growth of 2014. The results indicate that the prediction model of 2014 provides a reliable and consistent prediction based on the performance of 1985. The analysis of driving forces shows variable effects over time. Variables such as elevation, slope and road distance had significant effects on the selected cities. However, distance to major roads was the factor with the most impact to determine the urban form in all five cites in both 1985 and 2014. [ABSTRACT FROM AUTHOR]

Often landscape metrics are not thoroughly evaluated with respect to remote sensing data characteristics, such as their behavior in relation to variation in spatial and temporal resolution, number of land cover classes or dominant land cover categories. In such circumstances, it may be difficult to ascertain whether a change in a metric is due to landscape pattern change or due to the inherent variability in multi-temporal data. This study builds on this important consideration and proposes a rank-based metric selection process through computation of four difference-based indices (β,γ, ξ and θ) using a Max-Min/Max normalization approach. Land cover classification was carried out for two contrasting provinces, the Liverpool Range (LR) and Liverpool Plains (LP), of the Brigalow Belt South Bioregion (BBSB) of NSW, Australia. Landsat images, Multi Spectral Scanner (MSS) of 1972-1973 and TM of 1987-1988, 1993-1994, 1999-2000 and 2009-2010 were classified using object-based image analysis methods. A total of 30 landscape metrics were computed and their sensitivities towards variation in spatial and temporal resolutions, number of land cover classes and dominant land cover categories were evaluated by computing a score based on Max-Min/Max normalization. The landscape metrics selected on the basis of the proposed methods (Diversity index (MSIDI), Area weighted mean patch fractal dimension (SHAPE_AM), Mean core area (CORE_MN), Total edge (TE), No. of patches (NP), Contagion index (CONTAG), Mean nearest neighbor index (ENN_MN) and Mean patch fractal dimension (FRAC_MN)) were successful and effective in identifying changes over five different change periods. Major changes in land cover pattern after 1993 were observed, and though the trends were similar in both cases, the LP region became more fragmented than the LR. The proposed method was straightforward to apply, and can deal with multiple metrics when selection of an appropriate set can become difficult. [ABSTRACT FROM AUTHOR]

Species distribution modeling has been widely used in studying habitat relationships and for conservation purposes. However, neglecting ecological knowledge about species, e.g. their seasonal movements, and ignoring the proper environmental factors that can explain key elements for species survival (shelter, food and water) increase model uncertainty. This study exemplifies how these ecological gaps in species distribution modeling can be addressed by modeling the distribution of the emu (Dromaius novaehollandiae) in Australia. Emus cover a large area during the austral winter. However, their habitat shrinks during the summer months. We show evidence of emu summer habitat shrinkage due to higher fire frequency, and low water and food availability in northern regions. Our findings indicate that emus prefer areas with higher vegetation productivity and low fire recurrence, while their distribution is linked to an optimal intermediate (~0.12 m3 m-3) soil moisture range. We propose that the application of three geospatial data products derived from satellite remote sensing, namely fire frequency, ecosystem productivity, and soil water content, provides an effective representation of emu general habitat requirements, and substantially improves species distribution modeling and representation of the species’ ecological habitat niche across Australia. [ABSTRACT FROM AUTHOR]

Presence data for four mammals in the Turan Biosphere Reserve in Iran including the Asiatic cheetah ( Acinonyx jubatus venaticus ), the Persian onager ( Equus hemionus onager ), the wild sheep ( Ovis vignei ), and the gazelle ( Gazelle Bennettii ) were used to analyze and model their potential interaction, facilitation, habitat coverage and niche dimensions. A geostatistical approach using the spatial autocorrelation between the locality points, and their relationship with habitat resources and characteristics with application of remotely sensed maximum enhanced vegetation index (EVI) and surface temperature, elevation, aspect, vegetation cover and soil moisture was used to predict herbivores species niche. The potential suitable habitat of herbivores along with environmental variables was used to model the predator species (cheetah) niche. The model results were tested using fivefold cross validation by area under the curve (AUC) values on set of independent testing data and were compared to more commonly used models of generalized linear model (GLM) and MaxEnt. The results show that cheetah's potential suitable habitat has 61% overlap with wild sheep, 36% with onager, and 30% with gazelle. Onager habitat has 64% overlap with gazelle and 60% the wild sheep. Wild sheep on the hand, shares only 37% of its habitat with gazelle. The most prey and predator interaction exists between cheetahs and wild sheep, while onagers provides facilitation for gazelles and wild sheep by potentially providing extra water sources. Among the implemented modeling techniques, spatial GLM showed better performance over GLM and MaxEnt. We suggest that conservation effort should focus more on maintaining the population of wild sheep and onagers to support other species in the habitat. [ABSTRACT FROM AUTHOR]

A large percentage of the world’s population is concentrated along the coastal zones. These environmentally sensitive areas are under intense pressure from natural processes such as erosion, accretion and natural disasters as well as anthropogenic processes such as urban growth, resource development and pollution. These threats have made the coastal zone a priority for coastline monitoring programs and sustainable coastal management. This research utilizes integrated techniques of remote sensing and geographic information system (GIS) to monitor coastline changes from 1989 to 2010 at Hatiya Island, Bangladesh. In this study, satellite images from Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) were used to quantify the spatio-temporal changes that took place in the coastal zone of Hatiya Island during the specified period. The modified normalized difference water index (MNDWI) algorithm was applied to TM (1989 and 2010) and ETM (2000) images to discriminate the land–water interface and the on-screen digitizing approach was used over the MNDWI images of 1989, 2000 and 2010 for coastline extraction. Afterwards, the extent of changes in the coastline was estimated through overlaying the digitized maps of Hatiya Island of all three years. Coastline positions were highlighted to infer the erosion/accretion sectors along the coast, and the coastline changes were calculated. The results showed that erosion was severe in the northern and western parts of the island, whereas the southern and eastern parts of the island gained land through sedimentation. Over the study period (1989–2010), this offshore island witnessed the erosion of 6476 hectares. In contrast it experienced an accretion of 9916 hectares. These erosion and accretion processes played an active role in the changes of coastline during the study period. [ABSTRACT FROM AUTHOR]

Context: Reliable information on tree stem diameter variation at local spatial scales and on the factors controlling it could potentially lead to improved biomass estimation over pine plantations. Aims: This study addressed the relationship between local topography and tree diameter at breast height (DBH) within two even-aged radiata pine plantation sites in New South Wales, Australia. Methods: A total of 85 plots were established, and 1,302 trees were sampled from the two sites. Airborne light detection and ranging (LiDAR) was used to derive slope and aspect and to link them to each individual tree. Results: The results showed a significant relationship between DBH and local topography factors. At both sites, trees on slopes below 20° and on southerly aspects displayed significantly larger DBHs than trees on steeper slopes and northerly aspects. Older trees with similar heights also exhibited a significant relationship between DBH and aspect factor, where greater DBHs were found on southerly aspects. Conclusions: The observed correlation between tree DBH and LiDAR-derived slope and aspect could contribute to the development of improved biomass estimation approaches in pine plantations. These topographical variables are easily attained with airborne LiDAR, and they could potentially improve DBH predictions in resource inventories (e.g. stand volume or biomass) and support field sampling design. [ABSTRACT FROM AUTHOR]

Better information regarding the spatial variability of height, Diameter at Breast Height (DBH) and stocking could improve inventory estimates at the operational Planning Unit since these parameters are used extensively in allometric equations, including stem volume, biomass and carbon calculations. In this study, the influence of stand stocking on height and DBH of two even aged radiata pine (Pinus radiata D. Don) stands were investigated using airborne Light Detection and Ranging (LiDAR) data at a study site in New South Wales, Australia. Both stands were characterized by irregular stocking due to patchy establishment and self-thinning in the absence of any silvicultural thinning events. For the purpose of this study, a total of 34 plots from a 34 year old site and 43 plots from a nine year old site were established, from which a total of 447 trees were sampled. Within these plots, DBH and height measurements were measured and their relationships with stocking were evaluated. LiDAR was used for height estimation as well as stem counts in fixed plots (stocking). The results showed a significant relationship between stem DBH and stocking. At both locations, trees with larger diameters were found on lower stocking sites. Height values were also significantly correlated with stocking, with taller trees associated with high stocking. These results were further verified of additional tree samples, with independent field surveys for DBH and LiDAR-derived metrics for height analysis. This study confirmed the relationship between P. radiata tree heights and stem diameter with stocking and demonstrated the capacity of LiDAR to capture sub-compartment variation in these tree-level attributes. [ABSTRACT FROM AUTHOR]

Soil salinity is one of the most damaging environmental problems worldwide, especially in arid and semi-arid regions. An integrated approach using remote sensing in addition to various statistical methods has shown success for developing soil salinity prediction models. The aim of this study was to develop statistical regression models based on remotely sensed indicators to predict and map spatial variation in soil salinity in the Al Hassa oasis. Different spectral indices were calculated from original bands of IKONOS images. Statistical correlation between field measurements of Electrical Conductivity (EC), spectral indices and IKONOS original bands showed that the Salinity Index (SI) and red band (band 3) had the highest correlation with EC. Combining these two remotely sensed variables into one model yielded the best fit with R² = 0.65. The results revealed that the high performance of this combined model is attributed to: (i) the spatial resolution of the images; (ii) the great potential of the enhanced images, derived from SI, by enhancing and delineating the spatial variation of soil salinity; and (iii) the superiority of band 3 in retrieving soil salinity features and patterns, which was explained by the high reflectance of the smooth and bright surface crust and the low reflectance of the coarse dark puffy crust. Soil salinity maps generated using the selected model showed that strongly saline soils (>16 dS/m) with variable spatial distribution were the dominant class over the study area. The spatial variability of this class over the investigated areas was attributed to a variety factors, including soil factors, management related factors and climate factors. The results demonstrate that modelling and mapping spatial variation in soil salinity based on regression analysis and remote sensing data is a promising approach, as it facilitates timely detection with a low-cost procedure and allows decision makers to decide what necessary action should be taken in the early stages to prevent soil salinity from becoming prevalent, sustaining agricultural lands and natural ecosystems. [ABSTRACT FROM AUTHOR]

Digital curve identification from remote sensing data is difficult in continuous objects such as roads. Use of high spatial resolution images can increase geometrical details and accuracy of estimation and detect curvy segments from the road boundary. We detect a road as a curve in 2D raster grid and analyze its shape using fuzzy c-means and alpha shapes. Two approaches identify curvature from the polylines on two sides of the road. Image resolution, radius of alpha circles and size of moving window are the three main parameters for detection of curvy segments. Lower resolution, larger alpha circles and larger moving windows decrease the chance of detecting sharp and narrow curve segments. [ABSTRACT FROM AUTHOR]

Abstract: For successful characterization of ecological processes and prioritization of habitat networks it is necessary to describe and quantify landscape structure and connectivity. However, at landscape scale, it is highly impractical to measure and map all elements of biodiversity, and therefore, biodiversity surrogates are commonly used to represent biodiversity values. Land cover and vegetation are most often used as a biodiversity surrogate. The study investigated how land use change affects the status of the biodiversity surrogates in terms of the loss or gain of habitat (areal extent), loss of habitat condition (degradation) and habitat fragmentation. Effective habitat area (EHA) and raster based cost–benefit analysis (CBA) modeling techniques were used for the assessment of the impact of land use change scenarios on wildlife habitat as biodiversity surrogates. The modeling was carried out on time-series land cover data from 1972 to 2009 for the Liverpool Range of New South Wales (NSW). The model estimated the future condition of vegetation in each and every grid-cell in the region as a function of current condition, existing land cover, and the threatening processes. The results indicated a continuous pattern of clearing in the region, while the habitat conditions were mostly static throughout the study period. There was a decline in EHA after 1993, by 3%. Clearing was identified as the main cause of such decline during the change period. [Copyright &y& Elsevier]

Abstract: Binary images from one or more spectral bands have been used in many studies for land-cover change/no-change identification in diverse climatic conditions. Determination of appropriate threshold levels for change/no-change identification is a critical factor that influences change detection result accuracy. The most used method to determine the threshold values is based on the standard deviation (SD) from the mean, assuming the amount of change (due to increase or decrease in brightness values) to be symmetrically distributed on a standard normal curve, which is not always true. Considering the asymmetrical nature of distribution histogram for the two sides, this study proposes a relatively simple and easy ‘Independent Two-Step’ thresholding approach for optimal threshold value determination for spectrally increased and decreased part using Normalized Difference Vegetation Index (NDVI) difference image. Six NDVI differencing images from 2007 to 2009 of different seasons were tested for inter-annual or seasonal land cover change/no-change identification. The relative performances of the proposed and two other methods towards the sensitivity of distributions were tested and an improvement of ∼3% in overall accuracy and of ∼0.04 in Kappa was attained with the Proposed Method. This study demonstrated the importance of consideration of normality of data distributions in land-cover change/no-change analysis. [Copyright &y& Elsevier]

Knowledge of the spatial distribution and clustering of productive areas of research can be used by prospective postgraduate students and early career researchers to target locations with high potential for professional development, as well as encourage strategic networking and sharing of knowledge. In the present study, the research output of the Remote Sensing and Geographic Information Science (RS/GIS) discipline in Australia was examined and hotspots of research activity were identified. Publication data from 10 peer-reviewed journals for the 1991 to 2010 period were used to look at research productivity and the levels of research collaboration between research institutions, based on authorship data. Results identified a number of hotspot areas, signifying a clustering of RS/GIS research. The productivity of these hotspot clusters changed over time, with some clusters that were very productive in the early 1990s giving way to other clusters that had developed significantly over time. Some clusters declined in productivity over time, while others were superseded by the development of more productive clusters. The study also found a substantial increase in cross-institutional collaboration in RS/GIS research in Australia over the last 20 years. [ABSTRACT FROM PUBLISHER]

The purpose of this study was to investigate the potential of imaging spectroscopy for the discrimination between eucalyptus species. High spectral reflectance signatures of 11 eucalyptus species were measured in the laboratory and significant differences at a number of wavelength positions were detected. There were differences in terms of absolute reflectance, depths of absorption features and the relative position of change in terms of the wavelength. The differences between species were more noticeable in the first derivative spectra when compared with the raw spectra. This was attributed to the ability of derivatives to remove the noise from raw reflectance spectra. The results also indicate the possibility of utilizing the common vegetation indices and ratios used in remote sensing for discriminating species and highlight the need to select spectral channels at pertinent positions where the differences are the greatest. This study has identified many of these positions in relation to some eucalyptus species. However, the study also shows that there is no single wavelength which will discriminate between all species and it also shows that even with hyperspectral data, issues with detailed level of mapping will still exist. [ABSTRACT FROM AUTHOR]

In the Sahel, poor soil quality and rainfall levels have a great influence on pasture production and hence on secondary output. In areas where rainfall is the limiting factor for primary production, recovery of primary and secondary production after the dry season depends on soil type. On sandy soils a large fraction of rainfall infiltrates and becomes available for plant growth, stimulating fast herbage growth, while on clayey and loamy soils low infiltration rates generate runoff, leading to slower herbage growth rates. The very different moisture retention characteristic of sands and clays is another possible cause for the observed differences in growth rates. In this paper we investigate the herbage growth rate from the onset of the rainy season. We hypothesise that, in areas where rainfall is the limiting factor for primary production, the vegetation growth rate on clayey soils is lower than that on sandy soils. We will test this hypothesis using long-term rainfall, soil types and satellite derived normalised difference vegetation index data. This research shows that the growth rates on sandy soil are significantly greater than that on clayey soils during the early part of the rainy season. We also show that these differences can be detected at large scales using satellite imagery. We also conclude that, at this scale, movement strategies of pastoralists would be intrinsically linked to not only rainfall patterns and distribution, but also to the underlying soil types in the region as this affects the quality and quantity of fodder available. [Copyright &y& Elsevier]

Although algorithms are well developed to predict soil organic carbon (SOC), selecting appropriate covariates to improve prediction accuracy is an ongoing challenge. Terrain attributes and remote sensing data are the most common covariates for SOC prediction. This study tested the predictive performance of nine different combinations of topographic variables and multi-season remotely sensed data to improve the prediction of SOC in the cultivated lands of a middle mountain catchment of Nepal. The random forest method was used to predict SOC contents, and the quantile regression forest for quantifying the prediction uncertainty. Prediction of SOC contents was improved when remote sensing data of multiple seasons were used together with the terrain variables. Remote sensing data of multiple seasons capture the dynamic conditions of surface soils more effectively than using an image of a single season. It is concluded that the use of remote sensing images of multiple seasons instead of a snapshot of a single period may be more effective for improving the prediction of SOC in a digital soil mapping framework. However, an image with the right timing of cropping season can provide comparable results if a parsimonious model is preferred. [ABSTRACT FROM AUTHOR]

Coastal wetlands in the Mekong Delta (MD), Vietnam, provide various vital ecosystem services for the region. These wetlands have experienced critical changes due to the increase in regional anthropogenic activities, global climate change, and the associated sea level rise (SLR). However, documented information and research on the dynamics and drivers of these important wetland areas remain limited for the region. The present study aims to determine the long-term dynamics of wetlands in the south-west coast of the MD using remote sensing approaches, and analyse the potential factors driving these dynamics. Wetland maps from the years 1995, 2002, 2013, and 2020 at a 15 m spatial resolution were derived from Landsat images with the aid of a hybrid classification approach. The accuracy of the wetland maps was relatively high, with overall accuracies ranging from 86–93%. The findings showed that the critical changes over the period 1995/2020 included the expansion of marine water into coastal lands, showing 129% shoreline erosion; a remarkable increase of 345% in aquaculture ponds; and a reduction of forested wetlands and rice fields/other crops by 32% and 73%, respectively. Although mangrove forests slightly increased for the period 2013/2020, the overall trend was also a reduction of 5%. Our findings show that the substantial increase in aquaculture ponds is at the expense of mangroves, forested wetlands, and rice fields/other crops, while shoreline erosion significantly affected coastal lands, especially mangrove forests. The interaction of a set of environmental and socioeconomic factors were responsible for the dynamics. In particular, SLR was identified as one of the main underlying drivers; however, the rapid changes were directly driven by policies on land-use for economic development in the region. The trends of wetland changes and SLR implicate their significant effects on environment, natural resources, food security, and likelihood of communities in the region sustaining for the long-term. These findings can assist in developing and planning appropriate management strategies and policies for wetland protection and conservation, and for sustainable development in the region. [ABSTRACT FROM AUTHOR]

With continuous improvement of earth observation technology, source, and volume of remote sensing data are gradually enriched. It is critical to realize unified organization and to form data sharing service capabilities for massive remote sensing data effectively. We design a hierarchical multi-dimensional hybrid indexing model (HMDH), to address the problems in underlying organization and management, and improve query efficiency. Firstly, we establish remote sensing data grid as the smallest unit carrying and processing spatio-temporal information. We implement the construction of the HMDH in two steps, data classification based on fuzzy clustering algorithm, and classification optimization based on recursive neighborhood search algorithm. Then, we construct a hierarchical "cube" structure, filled with continuous space filling curves, to complete the coding of the HMDH. The HMDH reduces the amount of data to 6–17% and improves the accuracy to more than eight times than traditional grid model. Moreover, it can reduce the query time to 25% in some query scenarios than algorithms selected as the baseline in this paper. The HMDH model proposed can be used to solve the efficiency problems of fast and joint retrieval of remote sensing data. It extends the pattens of data sharing service and has a high application value. [ABSTRACT FROM AUTHOR]

Soil salinity is a serious threat to coastal agriculture and has resulted in a significant reduction in agricultural output in many regions. Jaffna Peninsula, a semi-arid region located in the northern-most part of Sri Lanka, is also a victim of the adverse effects of coastal salinity. This study investigated long-term soil salinity changes and their link with agricultural land use changes, especially paddy land. Two Landsat images from 1988 and 2019 were used to map soil salinity distribution and changes. Another set of images was analyzed at four temporal periods to map abandoned paddy lands. A comparison of changes in soil salinity with abandoned paddy lands showed that abandoned paddy lands had significantly higher salinity than active paddy lands, confirming that increasing salts owing to the high levels of sea water intrusion in the soils, as well as higher water salinity in wells used for irrigation, could be the major drivers of degradation of paddy lands. The results also showed that there was a dramatic increase in soil salinity (1.4-fold) in the coastal lowlands of Jaffna Peninsula. 64.6% of the salinity-affected land was identified as being in the extreme saline category. In addition to reducing net arable lands, soil salinization has serious implications for food security and the livelihoods of farmers, potentially impacting the regional and national economy. [ABSTRACT FROM AUTHOR]

Climate variability can influence the dynamics of aquatic invasive alien plants (AIAPs) that exert tremendous pressure on aquatic systems, leading to loss of biodiversity, agricultural wealth, and ecosystem services. However, the magnitude of these impacts remains poorly known. The current study aims to analyse the long-term changes in the spatio-temporal distribution of AIAPs under the influence of climate variability in a heavily infested tank cascade system (TCS) in Sri Lanka. The changes in coverage of various features in the TCS were analysed using the supervised maximum likelihood classification of ten Landsat images over a 27-year period, from 1992 to 2019 using ENVI remote sensing software. The non-parametric Mann–Kendall trend test and Sen's slope estimate were used to analyse the trend of annual rainfall and temperature. We observed a positive trend of temperature that was statistically significant (p value < 0.05) and a positive trend of rainfall that was not statistically significant (p values > 0.05) over the time period. Our results showed fluctuations in the distribution of AIAPs in the short term; however, the coverage of AIAPs showed an increasing trend in the study area over the longer term. Thus, this study suggests that the AIAPs are likely to increase under climate variability in the study area. [ABSTRACT FROM AUTHOR]

The monitoring and assessment of land use/land cover (LULC) change over large areas are significantly important in numerous research areas, such as natural resource protection, sustainable development, and climate change. However, accurately extracting LULC only using the spectral features of satellite images is difficult owing to landscape heterogeneities over large areas. To improve the accuracy of LULC classification, numerous studies have introduced other auxiliary features to the classification model. The Google Earth Engine (GEE) not only provides powerful computing capabilities, but also provides a large amount of remote sensing data and various auxiliary datasets. However, the different effects of various auxiliary datasets in the GEE on the improvement of the LULC classification accuracy need to be elucidated along with methods that can optimize combinations of auxiliary datasets for pixel- and object-based classification. Herein, we comprehensively analyze the performance of different auxiliary features in improving the accuracy of pixel- and object-based LULC classification models with medium resolution. We select the Yangtze River Delta in China as the study area and Landsat-8 OLI data as the main dataset. Six types of features, including spectral features, remote sensing multi-indices, topographic features, soil features, distance to the water source, and phenological features, are derived from auxiliary open-source datasets in GEE. We then examine the effect of auxiliary datasets on the improvement of the accuracy of seven pixels-based and seven object-based random forest classification models. The results show that regardless of the types of auxiliary features, the overall accuracy of the classification can be improved. The results further show that the object-based classification achieves higher overall accuracy compared to that obtained by the pixel-based classification. The best overall accuracy from the pixel-based (object-based) classification model is 94.20% (96.01%). The topographic features play the most important role in improving the overall accuracy of classification in the pixel- and object-based models comprising all features. Although a higher accuracy is achieved when the object-based method is used with only spectral data, small objects on the ground cannot be monitored. However, combined with many types of auxiliary features, the object-based method can identify small objects while also achieving greater accuracy. Thus, when applying object-based classification models to mid-resolution remote sensing images, different types of auxiliary features are required. Our research results improve the accuracy of LULC classification in the Yangtze River Delta and further provide a benchmark for other regions with large landscape heterogeneity. [ABSTRACT FROM AUTHOR]

Soil salinity is a major threat to land productivity, water resources and agriculture in coastal areas and arid and semi-arid regions of the world. This has a significantly negative effect on the land and causes desertification. Monitoring salt accumulation in the soil is crucial for the prevention of land degradation in such environments. This study attempted to estimate and map soil salinity in Jaffna Peninsula, a semi-arid region of Sri Lanka. A Partial Least Squares Regression (PLSR) model was constructed using Sentinel 2A satellite imagery and field-measured soil electrical conductivity (EC) values. The results showed that satisfactory prediction of the soil salinity could be made based on the PLSR model coupled with Sentinel 2A satellite imagery (R2 = 0.69, RMSE = 0.4830). Overall, 32.8% of the land and 45% of paddy lands in Jaffna Peninsula are affected by salt. The findings of this study indicate that PLSR is suitable for the soil salinity mapping, especially in semi-arid regions like Jaffna Peninsula. The results underpin the importance of building adaptive capacity and implementing suitable preventive strategies for sustainable land and agricultural management. [ABSTRACT FROM AUTHOR]

Ecohydrological changes in large rivers of the world result from a long history of human dimensions and climate. The increasing human population, intensified land use, and climate change have led to a decline in the most critical aspect of achieving sustainable development, namely, that of water resources. This study assessed recent hydromorphological characteristics of the tropical Tana River in Kenya using flow duration curve, and geospatial techniques to gain a better understanding of human impacts over the last two decades and their consequences for new development projects. The results show that all extremal peak, low, and mean discharges exhibited significant increasing trends over a period of 17 years. Dam construction represents a 13% reduction of the maximum discharge and a 30% decrease in low flows, while post-regulation hydrological changes indicated an increase of 56 and 40% of high flows and low flows respectively. Dominant flow was observed to be higher for the current decade than the previous decade, representing a rise of the dominant streamflow by 33%. The assessment of four morphologically active sites at the downstream reach showed channel adjustments which support the changes in the flow regimes observed. The channel width increased by 8.7 and 1.9% at two sites but decreased by 31.5 and 16.2% for the other two sites under study during the time period. The results underscore the contribution of other main human modifications, apart from regulation, such as increased water abstraction and inter basin transfer, up-stream land use and anthropogenic climate change to assess the ecohydrological status in this river basin. Such streamflow regime dynamics may have implications on water resource management, riverine environments, and development of new water projects. [ABSTRACT FROM AUTHOR]

The Google Earth Engine (GEE) portal provides enhanced opportunities for undertaking earth observation studies. Established towards the end of 2010, it provides access to satellite and other ancillary data, cloud computing, and algorithms for processing large amounts of data with relative ease. However, the uptake and usage of the opportunity remains varied and unclear. This study was undertaken to investigate the usage patterns of the Google Earth Engine platform and whether researchers in developing countries were making use of the opportunity. Analysis of published literature showed that a total of 300 journal papers were published between 2011 and June 2017 that used GEE in their research, spread across 158 journals. The highest number of papers were in the journal Remote Sensing, followed by Remote Sensing of Environment. There were also a number of papers in premium journals such as Nature and Science. The application areas were quite varied, ranging from forest and vegetation studies to medical fields such as malaria. Landsat was the most widely used dataset; it is the biggest component of the GEE data portal, with data from the first to the current Landsat series available for use and download. Examination of data also showed that the usage was dominated by institutions based in developed nations, with study sites mainly in developed nations. There were very few studies originating from institutions based in less developed nations and those that targeted less developed nations, particularly in the African continent. [ABSTRACT FROM AUTHOR]

Accuracy assessment of classified imagery is an important task in remote sensing. Various measures have been developed to describe and compare the accuracy of maps and the performance of different classifiers, but the extent to which these measures are consistent with each other is largely unknown. In this paper the consistency of fourteen category-level and twenty map-level accuracy measures was tested on 595 published error matrices using nonparametric correlation coefficients (Spearman''s rho and Kendall''s tau-b) as well as the probability of concordance. The results show that four groups can be identified for the category-level measures and three groups for map-level measures. The consistency among the measures within a group is generally higher than that among the measures from different groups though all the measures at the same level are highly consistent with each other. We recommend that user''s accuracy and producer''s accuracy and the overall accuracy should be provided as primary accuracy measures and the two relative entropy change measures and the mutual information normalized by the arithmetic mean of the entropies on map and ground truthing be provided as supplementary measures. The chance-corrected, error matrix-normalized and user''s and producer''s accuracy-combined measures were found to contain estimation and interpretation problems at both category-and map-levels and are therefore not recommended for general use. [Copyright &y& Elsevier]