It is essential to produce land cover maps and land use maps separately for different purposes. This study was conducted to generate such maps in Binh Duong province, Vietnam, using a novel combination of pixel-based and object-based classification techniques and geographic information system (GIS) analysis on multi-temporal Landsat images. Firstly, the connection between land cover and land use was identified; thereafter, the land cover map and land use function regions were extracted with a random forest classifier. Finally, a land use map was generated by combining the land cover map and the land use function regions in a set of decision rules. The results showed that land cover and land use were linked by spectral, spatial, and temporal characteristics, and this helped effectively convert the land cover map into a land use map. The final land cover map attained an overall accuracy (OA) = 93.86%, with producer’s accuracy (PA) and user’s accuracy (UA) of its classes ranging from 73.91% to 100%. Meanwhile, the final land use map achieved OA = 93.45%, and the UA and PA ranged from 84% to 100%. The study demonstrated that it is possible to create high-accuracy maps based entirely on free multi-temporal satellite imagery that promote the reproducibility and proactivity of the research as well as cost-efficiency and time savings.

Data fusion has shown potential to improve the accuracy of land cover mapping, and selection of the optimal fusion technique remains a challenge. This study investigated the performance of fusing Sentinel-1 (S-1) and Sentinel-2 (S-2) data, using layer-stacking method at the pixel level and Dempster-Shafer (D-S) theory-based approach at the decision level, for mapping six land cover classes in Thu Dau Mot City, Vietnam. At the pixel level, S-1 and S-2 bands and their extracted textures and indices were stacked into the different single-sensor and multi-sensor datasets (i.e. fused datasets). The datasets were categorized into two groups. One group included the datasets containing only spectral and backscattering bands, and the other group included the datasets consisting of these bands and their extracted features. The random forest (RF) classifier was then applied to the datasets within each group. At the decision level, the RF classification outputs of the single-sensor datasets within each group were fused together based on D-S theory. Finally, the accuracy of the mapping results at both levels within each group was compared. The results showed that fusion at the decision level provided the best mapping accuracy compared to the results from other products within each group. The highest overall accuracy (OA) and Kappa coefficient of the map using D-S theory were 92.67% and 0.91, respectively. The decision-level fusion helped increase the OA of the map by 0.75% to 2.07% compared to that of corresponding S-2 products in the groups. Meanwhile, the data fusion at the pixel level delivered the mapping results, which yielded an OA of 4.88% to 6.58% lower than that of corresponding S-2 products in the groups.

This study aims to analyze land-use change and urban expansion in Binh Duong province, Vietnam, from 1995 to 2020. Multitemporal Landsat images were used to develop land-use maps. Area statistics and transition matrices were employed to explore the land-use change; meanwhile, annual expansion rate (AER), expansion contribution rate (ECR), and district-, ring-, and sector-based analyses were employed to analyze the urban expansion. The results showed that there was a large transition from agricultural and unused lands to other uses. This resulted in an expansion of developed areas, recreational regions, mining sites, and water surfaces, a drastic decline of agricultural land for annual crops, and a fluctuation of perennial cropland and unused land. The study also indicated that the urban area has expanded 65 times within 25 years at an increasing rate. The AER and ECR were uneven between subregions, and there was a gradual expansion and shift from south to north of the province. The factors affecting the changes comprise natural conditions, development histories, policies and practices for urbanization, industrialization, and agricultural development, and product price fluctuations in the market. Practical lessons learned from this study could be useful for land planning and policymaking in other localities.

Our study aimed to determine the most suitable indicator area and a remote sensing-based index (RSI) for warning winter–spring rice yield in the Central Highlands, Vietnam. Rice yield and RSIs, including vegetation condition index (VCI), temperature condition index, vegetation health index, and temperature-vegetation dryness index, in the period 2000 to 2020 were analyzed. Mann–Kendall nonparametric test and Sen’s slope were used to determine the trends of rice yield and RSIs. Meanwhile, multiple correlation coefficients were used to determine the most suitable indicator area and RSI for rice yield warning, and stepwise linear regression was used to evaluate the reliability of their warning ability. Research results showed that VCI was the most suitable RSI for early rice yield warning. The indicator area identified based on VCI occupied 5% of the area of the Central Highlands. This area had more difficult farming conditions than the rice-growing area and crops grown in it were also sensitive to water sources. Furthermore, the results also indicated that the VCI of the indicator area could predict rice yield from 1 to 3 months before harvest. The forecast accuracy depended on the sensitivity of rice yield to VCI and the stability of rice yield in each province.

Monitoring and predicting vegetation health are essential for agricultural activities and food security. This study aimed to select a model and evaluate the factors contributing to predicting the vegetation health index (VHI) in the Dak Nong Province, Vietnam. Machine learning algorithms were evaluated, including multiple linear regression, xGBoost, and artificial neural networks (ANN). The input variables of the models included the standardized precipitation evapotranspiration index (SPEI), soil moisture (SM), and VHI in the previous periods. Research results showed that the ANN model gave the best prediction results. The accuracy of prediction results depended on the season of the year, in which the dry season gave a result with high accuracy. The results also indicated that SM from one to two previous months, SPEI1 from one to three previous months, SPEI3 and SPEI5 from three to six previous months, and VHI from one previous month contributed mainly to the prediction model. The relative contribution of SM and SPEI ranged from 42% to 52% in the last 4 months of the dry season. In addition, land use type also affected prediction quality.

The main purpose of this study is to simulate future land use up to 2030 and to evaluate the change in landscape pattern due to land-use change from 1995 to 2030 in Binh Duong province, Vietnam. Land-use maps generated from multi-temporal Landsat images from 1995 to 2020 and various physical and social driving variables were used as inputs. Markov chain and Decision Forest algorithm integrated in Land Change Modeler application of IDRISI software were used to predict quantity and location of future land-use allocation. Meanwhile, FRAGSTATS software was used to calculate landscape metrics at class and landscape levels. The simulation results showed that there will be 253.8 km2 of agricultural land urbanized in the period from 2020 to 2030. The urban areas will gradually expand from the edge of the existing zones and fill the newly planned areas from South to North and Northeast of the province. The results also revealed that the studied landscape was decreasing in dominance and increasing diversity and heterogeneity at landscape level. The processes of dispersion and aggregation were taking place at the same time in the entire landscape and in the urban class. Meanwhile, the classes of agriculture, mining, and greenspace were increasingly dispersed, but the shape of patches was becoming more regular. The water class increased the dispersion and the irregularity of the patch shape. Finally, the landscape metrics of the unused land fluctuated over time.