Drought potential index using Normalized Difference Drought Index (NDDI) method based on Geographical Information System (GIS) in Slogohimo, Wonogiri Indonesia

Climate change is an impact caused by global warming. The phenomenon due to climate change is El Nino, which affects a long dry season. Central Java is an area heavily affected by drought caused by El Nino, one of which is Wonogiri Regency, which has the potential to cause crop failures, forest fires, and decreased water availability, which can be detrimental. Drought potential analysis was carried out to estimate the distribution of the drought index in Slogohimo District, Wonogiri Regency. The method used is the Normalized Difference Drought Index (NDDI), which combines vegetation density and wetting level using Landsat imagery—the NDDI index transformation method on a scale ranging from very low to very high (severe). The results show that the distribution of drought potential in Slogohimo District, Wonogiri Regency, is classified as very low to very high class. Still, the broadest area is classified as low. The tendency for drought to occur in the Slogohimo area is spread from the east to the south. This research suggests the application of organic mulch for improvement to reduce the potential for drought. Apart from being an effort to modify soil temperature, organic mulch also functions as a soil conditioner, which impacts adding soil pores to increase water retention.


Introduction
Climate change is an impact caused by global warming.Increasing global temperatures are changing how rainfall occurs and making water evaporate faster, resulting in extended periods of water shortage.Drought is typically characterized as a natural occurrence during which the amount of accessible water remains considerably below the usual levels for an extended duration, leading to an insufficient water supply to meet present demands [1].Droughts have transformed from infrequent occurrences into ongoing problems that endanger food security, water supplies, and the well-being of communities in numerous areas.Today, the importance of optimizing water utilization in agriculture has significantly increased due to the global challenge of climate change.Climate change encompasses irregular atmospheric conditions and sudden, unforeseen weather occurrences like hurricanes, floods, prolonged droughts, and extreme temperature fluctuations [2].Indonesia is located in an area with a tropical monsoon climate, which is very sensitive to the El Nino climate anomaly [3].El Nino has the 1314 (2024) 012040 IOP Publishing doi:10.1088/1755-1315/1314/1/012040 2 impact of extending the dry season in Indonesia, which causes changes in agricultural patterns.Central Java is an area significantly affected by the drought caused by El Nino, one of which is Wonogiri Regency.According to Indonesian Disaster Information Data (IDID), in 2019, the Wonogiri Regency experienced a drought.Drought disasters can potentially cause crop failure, forest fires, and detrimental reductions in water availability [4].Alterations in temperature and precipitation patterns significantly threaten agriculture, subjecting plants to diverse environmental pressures and diminishing their economic yield [5].An attempt that can be made to minimize the impact of drought is to estimate the amount of water by analyzing the drought index using a geographic information system (GIS).Land conditions such as land suitability, flood vulnerability, and drought potential can be analyzed with information and communication technology development using GIS and remote sensing [6].
Drought index analysis can be done using the Normalized Difference Drought Index (NDDI) method.NDDI assumes a crucial role in monitoring and evaluating drought intensity, facilitating wellinformed choices in managing water resources and agricultural strategies.NDDI operates by analyzing disparities in reflectance between visible and near-infrared light, allowing for the identification of shifts in vegetation well-being that could signal drought-related strain.This data is significant in areas where water scarcity seriously jeopardizes crop yields and ecosystems.Through quantifying drought scope and severity, NDDI contributes to early alert systems, supporting farmers and decision-makers in readiness and mitigation efforts for drought occurrences.Landsat imagery can be used to map the drought vulnerability of an area by calculating using the NDDI method obtained from the Near Infrared band, Red band, and Short Wave Infra-red band [7].Advances in technology can help facilitate efforts to mitigate natural disasters, such as drought.This research suggests an attempt to mitigate the drought disaster in Slogohimo District, Wonogiri Regency, based on calculating drought severity level using the NDDI method.

Research time and place
The research was carried out in July 2023, and the research location was located in Slogohimo District, Wonogiri Regency, Central Java.This location is at coordinates 7°49'03" S and 111°11'13" E. The territorial boundaries of Slogohimo District include, to the north, the border with Selogiri District and Wuryantoro District.To the east, the edge with Purwantoro District, to the south with the District Baturetno and Girimarto Districts, to the west is bordered by Jatipurno District and Jatiroto District.Slogohimo District is divided into 15 villages and 2 sub-districts consisting of Padarangin Village, Watusomo Village, Sambirejo Village, Pandan Village, Made Village, Tunggur Village, Waru Village, Slogohimo Village, Gunan Village, Sedayu Village, Soco Village, Klunggen Village, Randusari Village, Sokoboyo Village, Setren Village, Bulusari Sub-district, and Karang Sub-district.

Research materials and tools
The tools used in this research were ArcGIS and Quantum GIS.The material used was Landsat 8 OLI/TIRS C2 L1 imagery, captured on May 2018-image obtained by downloading data from https://glovis.usgs.gov/app on on June 2023.

Pre-Processing Data
Landsat 8 OLI/TIRS C2 L1 imagery requires an adjustment in shape to the land area to be studied so that cropping is carried out according to the area of Slogohimo District, Wonogiri Regency.The cropped image is then corrected by doing radiometric correction to ensure the quality and consistency of the radiometric data obtained from the satellite.Radiometric correction was carried out to equalize atmospheric conditions [8].Radiometric correction was performed using the Quantum GIS application with the Semi-Automatic Classification Plugin.
Where, ρ : near-infrared reflected band (band 5) ρR : red reflected band (band 4) The NDVI scale is in the range of -1 to 1, whereas the scale gets closer to 1, indicating that the area's plant density is getting denser.A scale close to -1 indicates that the plant density is lower, which can be assumed to be open land and water areas.NDVI information has been applied to analyze the spatiotemporal variations in vegetation and identify the factors influencing these changes.In recent times, researchers have examined the impacts of vegetation growth, phenology, and the length of the growing season on temporal, spatial, and elevational alterations in vegetation, including the influences of climate change, land use, and land cover, at various geographical scales such as basins, plateaus, mountains, and entire countries [9].

Normalized Difference Water Index (NDWI)
Wetness level calculations are carried out using the following formula.
Where, ρ : near-infrared reflect band (band 5) ρ : red reflected band (band 6) The NDWI scale ranges from -1 to 1; if the scale is above 0, it indicates that the area is water surface.A scale below 0 indicates that the area is not a water surface.NDWI data obtained through remote sensing can capture changes in hydrology over time and across different locations, expanding the spatial coverage beyond what can be achieved through on-site hydrological measurements.A distinctive feature of floodplains is the ability to isolate individual components, such as sub-lakes, from the main floodplain during dry periods [10].

Normalized Difference Drought Index (NDDI)
The drought index calculation uses plant density and wetness level indicators using the following formula.

𝑁𝐷𝐷𝐼 =
− + (3) Where, NDVI : Normalized Difference Vegetation Index NDWI : Normalized Difference Water Index In the conversions of NDVI and NDWI, a greater value signifies greater vegetation density and moisture content in the region.Conversely, in the case of the NDDI transformation, a higher value corresponds to a drier area [11].The drought index based on NDDI is classified into five classes as follows.Very high Source : [12] IOP Publishing doi:10.1088/1755-1315/1314/1/0120405

Landsat Imagery Data Processing
The Landsat 8 image has bands 4, 5, and 6.The image goes through a cropping process to suit the Slogohimo District, Wonogiri Regency's landform, shown in Figure 1.The photos following the shape of the research location are then continued for further processing, which is the radiometric correction.Radiometric correction is needed to eliminate radiometric disturbances such as the effects of solar radiation, object geometry, and atmospheric disturbances [13].Radiometric correction was performed using the Quantum GIS software to obtain TOA Reflectance and appropriate band values.Surface reflectance is crucial in accurately characterizing primary land cover types [14].The value in the Landsat 8 image that has not been subjected to radiometric correction is in the range of thousands, as shown in Figure 2.
In contrast, the value is in units shown in Figure 3 after correction.The range of values before correction in Band 4 is 5803 to 41583, in Band 5 is 5747 to 47183, and in Band 6 is 5222 to 38471.Images that have gone through the digital value correction process decrease in Band 4 to 0.00492217 to 1, in Band 5 becomes 0.00778788 to 1, and in Band 6, it becomes 0.00813983 to 1.

Normalized Difference Vegetation Index Calculation
Using corrected images, the normalized Difference Drought Index was calculated from vegetation density data (Normalized Difference Vegetation Index) and humidity data (Normalized Difference Water Index).In contrast to conventional ground-based observation techniques, remote sensing using satellite technology has emerged as a highly effective method for tracking vegetation activity.This coincidence is due to its capability to offer extensive multi-temporal NDVI imagery on a large scale [15].The corrected image is then processed to calculate the vegetation density index and Normalized Difference Vegetation Index using equation (1).This index helps quantify the amount of chlorophyll and overall photosynthetic activity in vegetation.Healthy leaves absorb the most visible radiation and reflect and transmit most Near-Infrared light, so NDVI can be used to estimate leaf chlorophyll and photosynthetically active vegetation cover [16].High NDVI values indicate dense and healthy vegetation, while low values can suggest sparse or stressed vegetation and non-vegetated surfaces.NDVI is widely used in applications such as assessing land cover changes, monitoring deforestation, evaluating agricultural productivity, and understanding the impacts of climate change on ecosystems.The processing results shown in Figure 4 produce data ranging from -0.865309 to 0.92811.A larger value (in blue) indicates the vegetation density is greater, while a smaller value (in red) indicates the vegetation density is lower.

Normalized Difference Water Index Calculation
Landsat imagery is also used to obtain wetness index or Normalized Difference Water Index (NDWI) data using equation (2).NDWI methods typically involve the calculation of an index that leverages the contrast between water and non-water features in multispectral satellite or aerial imagery.However, water bodies can also have NDWI values <0 due to bare sediment in some rivers, lakes, and seas [17].The data processing results shown in Figure 5 produce the lowest value range of -0.703288 and the highest value of 1. Higher values (in blue) indicate that the area has an area with water, whereas the value below 0 (in red) indicates that the area is not a water area.NDWI methods can be applied to monitor droughts, floods, wetland changes, and urban development impacts on water resources.Areas with positive value ranges indicate that the region is not drought-resistant because it contains water.

Normalized Difference Drought Index Calculation
Normalized Difference Drought Index provides information about the relationship between vegetation water content and greenness using equation (3).Positive NDDI values indicate more intense drought (drier conditions) and decreased vegetation health, often associated with drought stress.In contrast, IOP Publishing doi:10.1088/1755-1315/1314/1/0120406 negative values suggest healthier vegetation with sufficient moisture, clouds, or the presence of water [18].This index aids in early drought detection, guiding resource allocation and management decisions to mitigate the impacts of water scarcity on ecosystems, agriculture, and water resources.The calculation results of drought data processing using the NDDI method shown in Figure 6 are divided into five drought severity level classes: very low, low, moderate, high, and very high [12].Low drought severity level has the broadest area, 93% of the total area when converted into hectares 6,830 ha.Drought severity level classification is shown in the table below.The density of vegetation potentially causes the low severity index due to Slogohimo land use dominated by ricefield, while the high and very high classification is in the settlement area.Hence, the vegetation density is lower or none.

Land Management Suggestion
Climate change-induced disasters can significantly affect ecosystems and various economic sectors, with agriculture and food security most notably vulnerable.Sudden and unforeseen climate-related crises can devastate livestock and crop yields, putting local food reserves at risk [22].One of the efforts that can be made to reduce the negative impact of drought and the potential for lack is by adding organic material, which is applied as mulch.Mulching is a technique that covers or coats soil surfaces using organic or synthetic mulch to create humid and favorable conditions [21].Organic materials applied as organic mulch can benefit the soil by stimulating microbial activity to produce binding agents which can improve soil aggregation and make it more stable [19].Organic mulch shields the soil surface from direct sunlight and wind, which helps reduce water evaporation rates.Reduced evaporation rates can keep soil moisture maintained and provide sufficient water for plant production so that it can be an effort to mitigate drought [20].Using organic materials such as organic mulch can improve the porosity and aggregate structure of the soil to increase infiltration, namely the entry of rainwater into the soil.The capacity of water stored in the soil increases so that the soil is kept moist.Organic mulch is permeable, biodegradable, and therefore can gradually lose its mulch function and, with the decomposition process, becomes a source of organic matter for the soil [23].Adding organic matter can enchance soil characteristics such as increasing soil porosity and decreasing soil bulk density [24], increasing soil infiltration so water is easily absorbed and stored as groundwater.
Over the past few years, there has been extensive research into the benefits of implementing straw return practices for mulching in aspects such as the soil's hydrothermal conditions, nitrogen content, maize crop yield, water utilization efficiency, and nitrogen utilization efficiency [25].

Conclusions
Slogohimo's potential drought index, calculated using the Normalized Difference Drought Index, is divided into 5 categories (very low, low, moderate, high, and very high).Slogohimo district is dominated by a low drought severity level, which covers 93% of the total area.Very high drought severity classification dominates in the middle of Slogohimo district, which has a lower water area and vegetation density because it is a settlement area.Adding the organic mulch to improve soil aggregates improved water infiltration, and increasing water ground storage could mitigate drought potential.