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BackgroundThis study analysed the multi-temporal trend in land cover, and modelled a future scenario of land cover for the year 2030 in the highly urbanized state of Selangor, Malaysia. The study used a Decision Forest-Markov chain model in the land change modeller (LCM) tool of TerrSet software. Land cover maps of 1999, 2006 and 2017 were classified into 5 classes, namely water, natural vegetation, agriculture, built-up land and cleared land. A simulated land cover map of 2017 was validated against the actual land cover map 2017. The Area Under the Curve (AUC) value of 0.84 of Total Operating Characteristics (TOC) and higher percentage of components of agreement (Hits + Correct rejection) compared to components of disagreement (Misses + False alarm + Wrong hits) indicated successful validation of the model.ResultsThe results showed between the years 1999 to 2017 there was an increase in built-up land cover of 608.8 km2 (7.5%), and agricultural land 285.5 km2 (3.5%), whereas natural vegetation decreased by 831.8 km2 (10.2%). The simulated land cover map of 2030 showed a continuation of this trend, where built-up area is estimated to increase by 723 km2 (8.9%), and agricultural land is estimated to increase by 57.2 km2 (0.7%), leading to a decrease of natural vegetation by 663.9 km2 (8.1%) for the period 2017 to 2030. The spatial trend of land cover change shows built-up areas mostly located in central Selangor where the highly urbanized and populated cities of Kuala Lumpur and Putrajaya and the Klang valley are located.ConclusionThe future land cover modelling indicates that built-up expansion mostly takes place at edges of existing urban boundaries. The results of this study can be used by policy makers, urban planners and other stakeholders for future decision making and city planning.

Accurately predicting grapevine yield and quality is critical for optimising vineyard management and ensuring economic viability. Numerous studies have reported the complexity in modelling grapevine yield and quality due to variability in the canopy structure, challenges in incorporating soil and microclimatic factors, and management practices throughout the growing season. The use of multimodal data and machine learning (ML) algorithms could overcome these challenges. Our study aimed to assess the potential of multimodal data (hyperspectral vegetation indices (VIs), thermal indices, and canopy state variables) and ML algorithms to predict grapevine yield components and berry composition parameters. The study was conducted during the 2019/20 and 2020/21 grapevine growing seasons in two South Australian vineyards. Hyperspectral and thermal data of the canopy were collected at several growth stages. Simultaneously, grapevine canopy state variables, including the fractional intercepted photosynthetically active radiation (fiPAR), stem water potential (Ψstem), leaf chlorophyll content (LCC), and leaf gas exchange, were collected. Yield components were recorded at harvest. Berry composition parameters, such as total soluble solids (TSSs), titratable acidity (TA), pH, and the maturation index (IMAD), were measured at harvest. A total of 24 hyperspectral VIs and 3 thermal indices were derived from the proximal hyperspectral and thermal data. These data, together with the canopy state variable data, were then used as inputs for the modelling. Both linear and non-linear regression models, such as ridge (RR), Bayesian ridge (BRR), random forest (RF), gradient boosting (GB), K-Nearest Neighbour (KNN), and decision trees (DTs), were employed to model grape yield components and berry composition parameters. The results indicated that the GB model consistently outperformed the other models. The GB model had the best performance for the total number of clusters per vine (R2 = 0.77; RMSE = 0.56), average cluster weight (R2 = 0.93; RMSE = 0.00), average berry weight (R2 = 0.95; RMSE = 0.00), cluster weight (R2 = 0.95; RMSE = 0.13), and average berries per bunch (R2 = 0.93; RMSE = 0.83). For the yield, the RF model performed the best (R2 = 0.97; RMSE = 0.55). The GB model performed the best for the TSSs (R2 = 0.83; RMSE = 0.34), pH (R2 = 0.93; RMSE = 0.02), and IMAD (R2 = 0.88; RMSE = 0.19). However, the RF model performed best for the TA (R2 = 0.83; RMSE = 0.33). Our results also revealed the top 10 predictor variables for grapevine yield components and quality parameters, namely, the canopy temperature depression, LCC, fiPAR, normalised difference infrared index, Ψstem, stomatal conductance (gs), net photosynthesis (Pn), modified triangular vegetation index, modified red-edge simple ratio, and ANTgitelson index. These predictors significantly influence the grapevine growth, berry quality, and yield. The identification of these predictors of the grapevine yield and fruit composition can assist growers in improving vineyard management decisions and ultimately increase profitability.

Low- and middle-income countries in Southeast and East Asia face a range of challenges related to the rapid pace of urbanisation in the region, the scale of pollution, climate change, loss of ecosystem services and associated difficulties for ecological restoration. Possible pathways towards a more sustainable future lie in the applications of nature-based solutions (NBS). However, there is relatively little literature on the application of NBS in the region, particularly Southeast Asia. In this paper we address this gap by assessing the socio-ecological challenges to the application of NBS in the region – one of the most globally biodiverse. We first provide an overview and background on NBS and its underpinnings in biodiversity and ecosystem services. We then present a typology describing five unique challenges for the application of NBS in the region: (1) Characteristics of urbanisation; (2) Biophysical environmental and climatic context; (3) Environmental risks and challenges for restoration; (4) Human nature relationships and conflicts; and (5) Policy and governance context. Exploiting the opportunities through South-South and North-South collaboration to address the challenges of NBS in Southeast and East Asia needs to be a priority for government, planners and academics.

The objective of this research was to assess the feasibility of remote sensing (RS) technology, specifically an unmanned aerial system (UAS), to estimate Bambara groundnut canopy state variables including leaf area index (LAI), canopy chlorophyll content (CCC), aboveground biomass (AGB), and fractional vegetation cover (FVC). RS and ground data were acquired during Malaysia’s 2018/2019 Bambara groundnut growing season at six phenological stages; vegetative, flowering, podding, podfilling, maturity, and senescence. Five vegetation indices (VIs) were determined from the RS data, resulting in single-stage VIs and cumulative VIs (∑VIs). Pearson’s correlation was used to investigate the relationship between canopy state variables and single stage VIs and ∑VIs over several stages. Linear parametric and non-linear non-parametric machine learning (ML) regressions including CatBoost Regressor (CBR), Random Forest Regressor (RFR), AdaBoost Regressor (ABR), Huber Regressor (HR), Multiple Linear Regressor (MLR), Theil-Sen Regressor (TSR), Partial Least Squares Regressor (PLSR), and Ridge Regressor (RR) were used to estimate canopy state variables using VIs/∑VIs as input. The best single-stage correlations between canopy state variables and VIs were observed at flowering (r > 0.50 in most cases). Moreover, ∑VIs acquired from vegetative to senescence stage had the strongest correlation with all measured canopy state variables (r > 0.70 in most cases). In estimating AGB, MLR achieved the best testing performance (R2 = 0.77, RMSE = 0.30). For CCC, RFR excelled with R2 of 0.85 and RMSE of 2.88. Most models performed well in FVC estimation with testing R2 of 0.98–0.99 and low RMSE. For LAI, MLR stood out in testing with R2 of 0.74, and RMSE of 0.63. Results demonstrate the UAS-based RS technology potential for estimating Bambara groundnut canopy variables.

Sana’a the metropolitan capital of Yemen, has experienced rapid spatial growth and uncontrolled development for decades. In the absence of a means to forecast and predict urban growth trends, planning and urban policy decisions have been found wanting. In this study the SLEUTH (Slope, landuse, exclusion, urban extent, transportation and hillshade) model which has been widely and successfully applied in developed countries, has been applied to predict the spatial urban sprawl pattern from 2004–2020 in Sana’a. This was to provide the necessary forecast for better planning and decision making. The model performed well as per the calibration coefficient values. The results showed that there will a 29 % increase in spatial urban sprawl growth during the modeling period. Growth of the sprawl will be mainly at the edges of the urban boundary, there will also be a wide area of scattered urban clusters. Factors that will have major influence on spatial expansion of the city will be diffusion, natural and internal growth, slope (that will hinder spread) and transportation (along which most of the urban sprawl will occur). The study also provides an insight into how the SLEUTH model performs in a poorly planned urban environment as compared to the planned and controlled environment where it has been applied.

Floods in Malaysia have been increasing in frequency and magnitude as reflected in the Kelantan Flood event in 2014 that resulted in a huge loss of lives and properties. Whereas remote sensing (RS) and geographical information system (GIS) tools have been extensively applied in flood disaster management, there are few reports and studies on the impact of floods on the land use/land cover environment in a post-disaster assessment. In this study, an integrated modelling approach was developed that used Landsat 8 OLI TIRS (Operational Land Imager (OLI) and Thermal Infrared Sensor) data, flood indexing and classification processes to estimate the impact of flood on the environment. The Normalized Difference Flood Index-3 (NDFI3) is an improvement on NDFI2 that takes into account the effects of cloud shadow in the images when extracting flood index areas. The flood model developed showed good agreement when compared with flooded areas shown in SAR (synthetic-aperture radar) image. The results of the flood extent as a proxy for damage estimation showed that the total flooded area was 502.34 km2 for the Kelantan Flood event in 2014, with plantation and built-up area accounting for 43 and 34.6% respectively. The least affected land uses/land covers were deforested area and forest, which accounted for 12.2 and 10.2% respectively. The RS and GIS technique developed in this post-disaster damage assessment is effective, relatively inexpensive and simple to implement by local authorities in support of post-flood disaster planning and decision-making.

An effective and efficient planning of an urban growth and land use changes and its impact on the environment requires information about growth trends and patterns amongst other important information. Over the years, many urban growth models have been developed and used in the developed countries for forecasting growth patterns. In the developing countries however, there exist a very few studies showing the application of these models and their performances. In this study two models such as cellular automata (CA) and the SLEUTH models are applied in a geographical information system (GIS) to simulate and predict the urban growth and land use change for the City of Sana’a (Yemen) for the period 2004–2020. GIS based maps were generated for the urban growth pattern of the city which was further analyzed using geo-statistical techniques. During the models calibration process, a total of 35 years of time series dataset such as historical topographical maps, aerial photographs and satellite imageries was used to identify the parameters that influenced the urban growth. The validation result showed an overall accuracy of 99.6 %; with the producer’s accuracy of 83.3 % and the user’s accuracy 83.6 %. The SLEUTH model used the best fit growth rule parameters during the calibration to forecasting future urban growth pattern and generated various probability maps in which the individual grid cells are urbanized assuming unique “urban growth signatures”. The models generated future urban growth pattern and land use changes from the period 2004–2020. Both models proved effective in forecasting growth pattern that will be useful in planning and decision making. In comparison, the CA model growth pattern showed high density development, in which growth edges were filled and clusters were merged together to form a compact built-up area wherein less agricultural lands were included. On the contrary, the SLEUTH model growth pattern showed more urban sprawl and low-density development that included substantial areas of agricultural lands.

Observed rainfall is used for runoff modeling in flood forecasting where possible, however in cases where the response time of the watershed is too short for flood warning activities, a deterministic quantitative precipitation forecast (QPF) can be used. This is based on a limited-area meteorological model and can provide a forecasting horizon in the order of six hours or less. This study applies the results of a previously developed QPF based on a 1D cloud model using hourly NOAA-AVHRR (Advanced Very High Resolution Radiometer) and GMS (Geostationary Meteorological Satellite) datasets. Rainfall intensity values in the range of 3–12 mm/hr were extracted from these datasets based on the relation between cloud top temperature (CTT), cloud reflectance (CTR) and cloud height (CTH) using defined thresholds. The QPF, prepared for the rainstorm event of 27 September to 8 October 2000 was tested for rainfall runoff on the Langat River Basin, Malaysia, using a suitable NAM rainfall-runoff model. The response of the basin both to the rainfall-runoff simulation using the QPF estimate and the recorded observed rainfall is compared here, based on their corresponding discharge hydrographs. The comparison of the QPF and recorded rainfall showed R2 = 0.9028 for the entire basin. The runoff hydrograph for the recorded rainfall in the Kajang sub-catchment showed R2 = 0.9263 between the observed and the simulated, while that of the QPF rainfall was R2 = 0.819. This similarity in runoff suggests there is a high level of accuracy shown in the improved QPF, and that significant improvement of flood forecasting can be achieved through ‘Nowcasting’, thus increasing the response time for flood early warnings.

Conservative peak flood discharge estimation methods such as the rational method do not take into account the soil infiltration of the precipitation, thus leading to inaccurate estimations of peak discharges during storm events. The accuracy of estimated peak flood discharge is crucial in designing a drainage system that has the capacity to channel runoffs during a storm event, especially cloudbursts and in the analysis of flood prevention and mitigation. The aim of this study was to model the peak flood discharges of each sub-watershed in Selangor using a geographic information system (GIS). The geospatial modelling integrated the watershed terrain model, the developed Soil Conservation Service Curve Cumber (SCS-CN) and precipitation to develop an equation for estimation of peak flood discharge. Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) was used again to simulate the rainfall-runoff based on the Clark-unit hydrograph to validate the modelled estimation of peak flood discharge. The estimated peak flood discharge showed a coefficient of determination, r2 of 0.9445, when compared with the runoff simulation of the Clark-unit hydrograph. Both the results of the geospatial modelling and the developed equation suggest that the peak flood discharge of a sub-watershed during a storm event has a positive relationship with the watershed area, precipitation and Curve Number (CN), which takes into account the soil bulk density and land-use of the studied area, Selangor in Malaysia. The findings of the study present a comparable and holistic approach to the estimation of peak flood discharge in a watershed which can be in the absence of a hydrodynamic simulation model.

Infiltration is the process by which water enters the soil, and it plays a significant role in the hydrologic cycle. Direct measurement of infiltration is time consuming; however, empirical and physical models are inaccurate. In this study, we compared the results of a deep learning-based convolutional neural network (CNN) algorithm with those of models based on standalone support vector regression (SVR) and group method of data handling (GMDH) algorithms. We also tested a hybridized SVR and GMDH-based model enhanced by three metaheuristic algorithms: gray wolf optimization (GWO), bat algorithm (BA), and particle swarm optimization (PSO). Measured variables including measurement time; sand, clay and silt contents; bulk density; and soil moisture content were used as model inputs to predict cumulative infiltration as an output. Finally, models were evaluated using the Pearson correlation coefficient, root mean square error, mean absolute error, Nash–Sutcliffe efficiency, percentage of bias, and relative error. Weighting of the model input parameters/variables demonstrated that time was the most effective variable for predicting cumulative infiltration, whereas the most powerful parameter input combination required all seven variables. The prediction model evaluation results showed that the hybridized models improved the predictive capability of the standalone models.