Application of satellite images and GIS to assess the site situation and propose solutions for developing pineapple cultivation in U Minh Thuong, Kien Giang, Vietnam

The study was conducted to assess the site situation and propose solutions to develop the cropping area of pineapples in U Minh Thuong (UMT), Kien Giang province. The study used the Object-Based Image Analysis (OBIA) classification method to create a map of pineapple cropping in the U Minh Thuong area in 2016. Data on rainfall, soil type, temperature, and water reserves are inherited from the Department of Agriculture and Rural Development of Kien Giang Province and analyzed by the GIS method. Water irrigation in pineapple was estimated based on the formula of Mladen Todorovic, 2016 comparing freshwater reserves in the research region and irrigation water on pineapple, researched spatial distribution map of water response capacity for pineapple. The research results showed that the agricultural area in U Minh Thuong has natural conditions suitable for pineapple cultivation. The size of pineapple cultivation in this area was 4,387.29 ha, the largest in the Vinh Thuan district. The total freshwater reserve in the region varies, where the rainfall is, with the highest volume being 4,546.2 m3/ha (in September 2015) and the lowest 67.7 m3/ha (in March 2016). However, the U Minh Thuong region lacks water in the dry months from January 2016 to April 2016. The amount of water during this period is starved, with watering demand from 2,385 to 2,969 million m3 on the total cultivated area of pineapple. In contrast, the surplus water was quite high in the rainy season, about 2,252 - 27,257 million m3 in Vinh Thuan district in September 2015. The study proposes a solution to store water during the rainy season and use them effectively during the dry season. This solution is compatible with the main pineapple farming habit and model in U Minh Thuong, Kien Giang Province.


Introduction
Kien Giang is one of the Mekong Delta's major pineapple-growing provinces, with about 7,850 hectares and an annual production of over 115,000 tons.According to the Department of Agriculture and Rural Development (DARD) of Kien Giang province (2021), the area of pineapple cultivation in the province is concentrated in the districts of Hon Dat, Chau Thanh, Go Quao, Vinh Thuan, and U Minh Thuong, with an average yield of 14.6 tons/ha.Water supplies for agricultural production depend a lot on the meteorological and hydrological regimes in the region [1].Rainwater and river water from the Hau river are the major sources of surface water in Kien Giang, which are delivered by Rach Gia canal, Vinh Te canal, Cai San canal, Thot Not canal, Chung Bau, Thac Lac-O Mon, KH3 canal, KH6, KH7, KH8, KH9.Three major rivers run through the province: the Cai Lon, Cai Be, and Giang Thanh rivers.The Hau River gives rise to two rivers, Cai Lon and Cai Be, which run into Rach Gia Bay, and the Giang Thanh River, which begins in Cambodia and flows into the Gulf of Thailand.
Kien Giang province is the place most affected by climate change in Vietnam in general and the Mekong Delta in particular, the problem of serious freshwater shortage is caused by the following reasons: prolonged drought plus the exploitation of a lot of groundwater is the main reason.The cause of saltwater intrusion in the Mekong Delta's coastal areas during the dry season posed a major danger to agriculture (including rice, fruit trees, and fisheries), costing an estimated USD 345 million in 2016 [2].Lack of irrigation water and deep saline intrusion often occur in the dry season months of coastal areas of Kien Giang: An Bien, An Minh, Vinh Thuan, along with Cai Lon and Cai Be rivers.
Recognizing the effects of climate change, Kien Giang has developed a master plan for the province's surface water resources.This plan, however, simply estimates the water requirement for plants in general for the entire season based on climate and meteorological parameters and does not take into account soil characteristics, plant growth stages, and notably, the demands of young plants, and especially, maps and data, in particular, have not been shown in terms of place and time [3].Theoretically, each sub-region will have distinct climatic and soil characteristics, resulting in varying water needs for the same crop at any given period.
Applying science and technology to monitor agriculture's current state will help to facilitate the management and save costs and efforts.GIS is one of the most commonly used applications today.GIS is an effective tool in natural resource management to create resource distribution maps, assess resource reserves, complete construction with storage capacity, manage, access, process, analyze and provide the information needed to make decisions in many areas of public service [4].GIS is applied in almost all fields of science, technology, and the environment, serving the very urgent needs of people.Furthermore, techniques of analyzing and building vegetation cover maps or current status maps using remote sensing images are becoming more developed.GIS is an essential tool for monitoring and assessing the current condition of land use and detecting dispersed items on the land.As a result, establishing the existing state of the crop and resource distribution in space and time, analyzing the issue, and proposing ways to boost agricultural production as a basis for land use planning, crop restructuring, and irrigation system improvements.

Overview of the study area
Kien Giang is a Vietnamese coastal province located in the Mekong Delta.U Minh Thuong (UMT) region includes four districts: Vinh Thuan, U Minh Thuong, An Minh, and An Bien, including the administrative scope of communes.

Data Collection
Images of remote sensing data were collected (free of charge) at the website: https://scihub.copernicus.eu of the European Space Agency (ESA) in the Mekong Delta.Both images were collected with a resolution of 10 -20 -60 by the Sentinel-2A satellite in April 2016.
For the secondary data, this study collected data from Kien Giang provincial departments, comprising data, documents, reports, and explanations.One important report about the impact of climate change used in this study is a General Report entitled "Planning surface water resources for socio-economic development in Kien Giang province from 2010 to 2020 and 2030".A report on the current state of hydrology and flow in flood and dry seasons was also collected and used to estimate the total volume of surface water in flood and dry seasons while considering climate change.The relevant maps from Kien Giang province were also collected, including a map of water distribution by ecological zones (salt, brackish, fresh), a soil map, a rainfall distribution map, a water distribution map, and a land-use status map.All of those maps were then processed using Mapinfo 15.0 software.
This study also conducted a field survey for the primary data to verify the accuracy of secondary data and farmer survey data on agricultural conditions and pineapple irrigation water.

Data preprocessing methods
Sentinel 2A images are preprocessed using the Subset data function in the Raster tool in SNAP software to limit the scope of study on the image while also reducing the size to save image processing time for the following steps in the process.

Image classification methods (Sentinel 2)
In the study, the remote sensing picture data is presented, processed, and categorized using eCognition software using the Rule-based Image Analysis technique, which includes the following steps: Step 1: Create object classes and properties for the object Establish four primary feature classifications are plants, urban, canals, aquaculture water surface, and barren terrain are the four primary feature classifications.There are two sub-classes in the vegetation object class: pineapple and forest.On the Class Hierarchy panel, object classes are established with an inheritance connection (Inheritance Relationship).
The selected Object Features include the vegetation difference index (NDVI), vegetation difference index based on a red spectral band (RVI), and vegetation difference index based on a green spectral band (GI).The pixel value of the infrared multispectral band was defined on the Edit Customized Features window.To make statistics, to establish histograms for each similar feature using the near neighbor classification method, we put them in the Standard Nearest Neighbor Feature Space.We placed them in the Standard Nearest Neighbor Feature Space to create statistics and construct histograms for each comparable feature.

Table 1. Table of object properties definition. Object properties
Formula NDVI NDVI = (NIR-Red)/(NIR+Red) Step 2: Fragment of the image object Multiresolution segmentation is a technique for collecting pixels that are close together and grouping them into tiny objects and considered as a unit in classification using the OBIA method.
Using the image segmentation function to separate different objects on the image, create a new image object layer (Level 1) based on the shape, smoothness, and scale parameters with the following values: Parameter scale is 20 Step 3: Select the object samples We use preliminary survey data from Google Earth, which is shown on eCognition, to pick samples for feature classes.Double-click on the image objects for the 06 feature classes to pick them using the Selecting Sample tool.In the whole UMT area, Kien Giang province, each feature class picks 50 -100 matching feature samples.
Step 4: Classify the Rule-based Image Analysis object class The classification method is based on the threshold value of each feature to identify each feature class.Other OBIA classification methods, such as the near neighbor object classifier (NN-OBIA), only stop at selecting samples and classifications.The Membership function performs this procedure, changing the maximum (max), minimum (min), and histogram shape of each characteristic to match the graph presented on the Sample Editor.The feature classes are then categorized using the Classification command in the Process tree window, using the Plant class as the filter (Class Filter) for the Group and Forest layers.

Reliability calculation method after classification.
The image after decoding is evaluated for accuracy to determine the reliability of the image interpretation process.One of them is to use the Kappa reliability index (K) to calculate, compare, and assess the concordance between several data sources.The data used to calculate the accuracy is a set of field survey points and a map of the current agricultural status in the study area (U Minh Thuong area, Kien Giang province).The Kappa index is calculated through the error matrix table after classification [5].T is the overall accuracy given by the error matrix E is a number that reflects a predictable (expected) accurate classification In other words, E contributes to a probability estimate of an accurate classification in the actual classification process.The value of E is calculated as the product of the boundary rows and columns of the error matrix.Then, this E-value is used to estimate the number of pixels assigned to each position in the error matrix or to represent the chances of pixels being assigned to each category.Overlapping the current map of pineapple cultivation and the geographical distribution map of water reserves and estimating the capacity to meet the water demand of pineapple in the UMT area.Methods of analyzing attribute data: estimating the ability to satisfy irrigation water demands by creating an attribute table containing single-calculated data on rainfall, temperature, and soil type.

Method of estimating water reserves in the region.
Kien Giang province has three main surface water sources that are transported and studied, including river water, rainwater, and storage water: Methods of determining river water reserves: The flood season of 2015 and the dry season of 2016 were calculated using a hydraulic mathematical model to simulate flow and saline intrusion (VRSAP).The model is run for the entire Mekong Delta, and the estimated outcomes, such as water level, discharge, and saline intrusion, are then extracted for the study region in Kien Giang province.Then, assess and evaluate the entering and exiting water sources in the research region.The discharge of unsalted rivers and canals coming into the research area multiplied by time is used to compute the amount of fresh water in the river.
The model has been extensively calibrated, and its parameters have been used to simulate low flows in the Mekong Delta for many years in projects conducted by domestic and international organizations such as Australia (Australia), the Mekong and ESSA River Commissions (Canada), NEDECO (Netherlands), and JICA (Japan).The Mekong Delta mathematical model has 4,151 segments, 2,486 nodes, and 880 field plots.The river and canal system depicted in the model has a total length of over 11,000 km, and the flood-affected region described in the model has a total area of up to 5.2 million hectares.
Method of determining the amount of rainwater: Rainfall is based on real data collected at 31 sites in Vietnam and Cambodia.The rain division is done with Thiesen's polygon technique.
The study uses data from the Institute of Marine Technology's studies on river water reserves and rainfall to create a geographical distribution map of water reserves in the UMT area.In which the UMT area's water reserve has reduced the water demand of other activities that utilize freshwater for daily living, services, livestock, and freshwater fisheries, as synthesized from the Institute of Technology Sea Art's study results, is given priority.As a result, the leftover water (total water in the region minus water demand for other activities= remaining water) is calculated.This study excludes water for salt and brackish fishery production because the minimum water requirement for salt concentration for this activity is greater than or equal to 7‰.

Method of estimating irrigation water demand.
The approach for calculating pineapple water requirement is based on Mladen Todorovic's [6]  Depending on the growth stage of the pineapple, there will be different Kc coefficients shown in the following table 3:

Methods to assess the current situation and propose solutions to develop pineapple in the U Minh Thuong area, Kien Giang province
In the UMT area of Kien Giang province, a distribution map of pineapple cultivation, the results of a field study, and information from district Agricultural Extension Stations were used to analyze the area and models of pineapple production.Spatial distribution maps of temperature, soil type, and rainfall were edited and used the data in the attribute table to compare with the ecological requirements of pineapple according to MARD in 2014 for comment.On suitability for pineapple cultivation in the UMT area, Kien Giang province.
The capacity to satisfy the water demand for pineapple irrigation in the UMT area is assessed in this study based on a comparison of the water reserve and the need for pineapple irrigation water.The study employs the CSWC findings from Mladen Todorovic's formula (2016): CSWC ≥ 0 provides enough or surplus water to pineapples; nevertheless, CSWC < 0 cannot deliver water to pineapple plants.

Proposed method of developing pineapple trees in UMT, Kien Giang province
Based on the findings from the current situation assessment, the study considers and proposes a suitable pineapple farming model, as well as solutions for using and storing water for irrigation.The study suggests developing an effective pineapple farming model that can withstand the effects of drought and salinity in the UMT area of Kien Giang province.

Results of mapping the current status of pineapple cultivation in the UMT area in 2016 4.1.1 Accuracy assessment.
Based on field survey data in Vinh Thuan district, the categorization findings are found to be reliable, with 44 points of pineapple cultivation and 56 points of no pineapple cultivation, including bare land, urban areas, rice land, and land.Rice -shrimp.The error matrix shows the reliability of the classification, the overall accuracy is 92%, and the Kappa index is 0.837.The classification results exhibit a high level of consistency, indicating that this approach may be used to categorize cluster objects.

Results of mapping the current status of pineapple cultivation in the UMT area
According to the results of the classification of pineapple cultivation in 2016 (Figure 3), the pineapple cultivation area is 4,387.29 ha, with the majority of it located around the Cai Lon and Cai Be rivers and irrigation canals.The area of the cluster of each district is presented in Table 4. Accordingly, pineapple is grown the most in Vinh Binh Bac commune, Vinh Thuan district, with 3,300.38 hectares opposite Go Quao district, Kien Giang province.In communes such as Pho Chanh, Minh Thuan, and U Minh Thuong districts, the remaining pineapple production land is dispersed with a lesser density (1,004.86ha).The remaining pineapple farming land is spread over the remaining districts, including 42.45 ha in An Bien and 9.61 ha in An Minh, most located along the Cai Lon River.Based on the data from the meteorological and hydrological stations, this study has developed a map of the average temperature distribution in the UMT region in 2015 and 2016.In general, there is no difference in temperature across districts.The average temperature is 27.7 o C.However, it varies significantly.In which the average yearly temperature in the north of An Bien district is always greater than 27.85 o C, while the average temperature in districts such as UMT and Vinh Thuan district is around 27.2 o C lower.The average temperature does not differ significantly between the two years.At the same time, according to FAO, pineapple is a drought-tolerant plant.Therefore, it can grow and develop well in temperatures ranging from 27 to 28 degrees Celsius.The pineapple's evapotranspiration (ETc) requirement was calculated using the UMT zone temperature.Figure 4 shows the regional distribution map of the mean annual temperature in the U Minh Thuong area.In terms of farming practices.The mulching model for plowing ditches is suitable for pineapple cultivation in the research area; nevertheless, the aforementioned model must be followed in the future.When dredging rivers and building dikes is costly and time-consuming, farmers can improve their pineapple fields by dredging adjacent ditches and filling them with pineapple beds to make the land more flood-resistant and provide crops with fresh nutrients.The pineapple waste must be used as fertilizer for the new pineapple plantation after demolishing the perennial pineapple garden.
In terms of varieties and cultivation techniques: Due to the effect of leaf wilt and rot, pineapple in the UMT area has a comparatively low output of around 16 tons/ha, which is lower than neighboring localities.Farmers in the region should contact the agricultural extension station's technical personnel as well as the Department of Agriculture and Rural Development for technical assistance, necessary medicines, and disease-free seedlings.This is when a plot is being renovated to plant a new one.
In terms of irrigation water: Urge people to modify their agricultural techniques, check pineapple plants regularly to ensure that the crops receive timely water fulfilling their physiological demands, and use more high-tech models to enhance water quality.
In terms of construction measures: The findings of this analysis demonstrate that the entire potential water volume in the province fulfilled all activities with water demand for the year (from July 2015 to June 2016).Storage of water during the rainy season, through canal dredging and construction of dikes, will provide better irrigation water for the entire area, which is another way to bring fresh water from the West of the Hau River.
In terms of forecasting: The Mekong River's water regime and local rainfall impact the amount of fresh water available in the province.As a result, precise and timely meteorological and hydrological forecasting is critical.
In terms of management: It is critical to provide resources for the development, maintenance, and upgrade of rural household water supply systems, with an emphasis on incorporating them into the National Target Program for new rural buildings.Actively allocate local budgets and socialized capital sources to implement urgent solutions to prevent and combat drought, water shortage, and saltwater intrusion, and conduct innovative research about varieties and techniques.In addition, it is also important to pay attention to pineapple quality and output consistency.Develop collaborative work on a pineapple production cooperative or business, so the farmers in the UMT district can get off-take contracts with processing firms to get a better price and steady and consistent output for long-term benefits.A brand identity for Ba Dinh pineapple may also be established.

Conclusion
In sum, the study has built a map of the current status of pineapple cultivation from Sentinel-2A remote sensing images as the basis for extracting pineapple cultivation data for estimating irrigation water demand.The area of pineapple cultivation is classified as 4,387.29 ha, mainly concentrated in Vinh Binh Bac commune, Vinh Thuan district, with the main farming model for plowing ditchesmulching.
The UMT location has ideal rainfall, temperature, and soil type for pineapple tree growth and development.Freshwater reserves in the UMT region are projected to vary with rainfall and are geographically irregularly distributed among districts, with a total reserve of 2,352 million m3 for the whole UMT region.The difference between the dry and wet seasons is fairly considerable.The average water consumption for pineapple is predicted to be 29.2 m3/ha.From July 2015 to June 2016, the study developed a geographical distribution map of the potential to satisfy water demand for pineapples in the UMT area of Kien Giang province.As a result, during the dry season, the region's ability to meet demand is nearly depleted, with a shortfall of 2.71 million m 3 (the worst shortage occurred in March 2016).In contrast, the excess water demand during the rainy season is quite high, reaching 27,257 million m3 on pineapple growing land.The regions surrounding the Cai Lon River in Vinh Thuan district have the most excess water.
The study proposes some pineapple-growing solutions based on the above findings, one of which is to store water in ditches during the rainy season and use this amount of water during the dry season.This suggestion is compatible with the main pineapple farming habits and models in the UMT area, Kien Giang province.

Figure 2 .
Figure 2. Survey point distribution map for dependability.

Figure 4 .
Figure 4. Map of temperature distribution in the U Minh Thuong area.

4. 2 . 2 10 Figure 5 .
Figure 5. Soil map of U Minh Thuong area, Kien Giang province.4.3Some suggestions for pineapple production in the UMT area.The research recommends the following solutions for effective water usage and development of pineapple production in the UMT area, Kien Giang province, based on an assessment of the existing status of pineapple farming in the UMT region:In terms of farming practices.The mulching model for plowing ditches is suitable for pineapple cultivation in the research area; nevertheless, the aforementioned model must be followed in the future.When dredging rivers and building dikes is costly and time-consuming, farmers can improve their pineapple fields by dredging adjacent ditches and filling them with pineapple beds to make the land more flood-resistant and provide crops with fresh nutrients.The pineapple waste must be used as fertilizer for the new pineapple plantation after demolishing the perennial pineapple garden.In terms of varieties and cultivation techniques: Due to the effect of leaf wilt and rot, pineapple in the UMT area has a comparatively low output of around 16 tons/ha, which is lower than neighboring localities.Farmers in the region should contact the agricultural extension station's technical personnel as well as the Department of Agriculture and Rural Development for technical assistance, necessary medicines, and disease-free seedlings.This is when a plot is being renovated to plant a new one.In terms of irrigation water: Urge people to modify their agricultural techniques, check pineapple plants regularly to ensure that the crops receive timely water fulfilling their physiological demands, and use more high-tech models to enhance water quality.In terms of construction measures: The findings of this analysis demonstrate that the entire potential water volume in the province fulfilled all activities with water demand for the year (from July 2015 to June 2016).Storage of water during the rainy season, through canal dredging and construction of dikes, will provide better irrigation water for the entire area, which is another way to bring fresh water from the West of the Hau River.In terms of forecasting: The Mekong River's water regime and local rainfall impact the amount of fresh water available in the province.As a result, precise and timely meteorological and hydrological forecasting is critical.In terms of management: It is critical to provide resources for the development, maintenance, and upgrade of rural household water supply systems, with an emphasis on incorporating them into the National Target Program for new rural buildings.Actively allocate local budgets and socialized capital sources to implement urgent solutions to prevent and combat drought, water shortage, and

Table 2 .
Reliability rating scale of Kappa index.

Table 4 .
Area of pineapple cultivation in districts of UMT region.
4.2 Results of assessing the situation of pineapple cultivation in the UMT area 4.2.1 Average temperature in UMT area, Kien Giang province.