1 Introduction

People migration from rural to urban areas [1,2,3] and fluctuations in land surface temperature (LST) and land use/land cover (LU/LC) [4,5,6] have adversely impacted crop health. Recent studies [7,8,9] suggest that these environmental modifications have led to a 3.2% reduction in food security per capita. Green plant cover could potentially affect climate and land surface fluxes at both the global and regional scales [10, 11]. Climate change alters terrestrial ecosystems and vegetation [12]. As plant cover has a significant impact on the energy cycle, hydrology, soil, and climate [13,14,15], it is important to explore its role in the context of post-industrialization LST rise related to global warming [16, 17]. Vegetation cover is crucial in describing human activities, fluctuations in the terrestrial ecosystem, soil dynamics, hydrological processes, and the context of regional and global climate change [18, 19]. According to the Intergovernmental Panel on Climate Change (IPCC) report 2013, a rapidly rising climate system deleteriously affects farming methods and plant life [20, 21]. Due to high rates of evapotranspiration and minimal precipitation, plant development in dry places is very reliant on the availability of water [22,23,24,25,26]. To accurately detect changes in vegetation patterns, researchers have turned their attention to study of plant phenology, vegetation cover and biomass [20, 27,28,29,30,31,32]. Nowadays, change analysis relies heavily on remote sensing (RS) satellite data [33, 34]. In recent years, remotely sensed data from sources like Landsat images have emerged as the dominant data sources for a wide range of change analysis applications [35,36,37]. This preference is attributed to their inherent advantages, including repetitive data acquisition, a comprehensive synoptic perspective, and a digital format that is conducive to computational processing. Furthermore, satellite imagery has been used for development and monitoring purposes in semi-arid and arid environments, as well as for LU/LC change analysis [38,39,40,41] with varying degrees of success.

Likewise, geographic information system (GIS) technique is employed to examine the effects of population density, terrain slope, distance to roads, and contiguous land use on LU/LC changes [42,43,44]. Integrating RS data with GIS is a powerful tool for statistically measuring urban expanding area and representing urban growth on a typically large geographical scale [45, 46]. Because of its ability to simultaneously measure a large area, satellite RS is now widely used to evaluate the biophysical properties of land surfaces, as well as to understand better and monitor landscape development and processes [47, 48]. Organizing, visualizing, and analyzing digital data become easier with GIS revolution, which facilitates the detection of changes and database expansion [49,50,51]. Long-term studies into RS vegetation dynamics have been identified as significant for global ecological research by researchers such as Bashir and Ahmad [52], Chen et al. [43], and Abdo [53]. Additionally, Ige et al. [54] and Mia et al. [55] note that RS is increasingly being utilized to detect seasonal variations in plant life. Applied RS is maturing into a reliable resource for assisting people in their efforts to solve ecologically-related challenges on a global, national, and regional scale [56]. An essential part of RS is keeping track of the substantial changes in vegetative indices and other agronomically relevant physical factors [57,58,59] throughout time. According to many studies [60, 61], the RS is an effective tool for keeping tabs on the health of a plant population and documenting how much of it there is using more affordable and flexible field measuring techniques. An increasingly pressing issue in the global climate change study is the detection and dynamics of vegetation and the mechanisms that drive them. Several satellite-based studies have reported changes in plant growth that are relevant to a changing climate [62,63,64].

  1. (1)

    Nasirabad is a district in the middle of Pakistan’s Balochistan province’s western half. It is well-known for its extensive rice production, making it the “green belt” and “lifeline” of Balochistan province. Notably, 90% of the world's rice supply comes from Asia, mostly from the tropical and subtropical areas. However, despite this abundance, most farmers in these areas are impoverished and isolated. In recent decades, Chinese hybrid rice seeds have helped Pakistan increase rice output quicker than the yields of other main crops like white rice and maize. Strangely, Pakistan is the eighth largest rice producer in the world, but it has fallen out of the top 10 in the last few years. In light of Pakistan’s current population growth rate of 2.8%, which leads to a steady increase in per capita rice consumption, it is clear that a policy shift is needed to increase rice production in order to keep up with demand. Land usage and water resource damage are exacerbated by climate change's effects on plant cover, surface temperature, and natural disasters, including floods, heat waves, and earthquakes. Therefore, the main goal of this research:

    1. (1)

      Identify LU/LC changes in the study region during the last 30 years (1993–2023).

    2. (2)

      Study the changes of LST in Nasirabad district, Pakistan.

    3. (3)

      Analyze the relationship between the LU/LC, LST and the environment.

The remaining organization of this work is as follows: Sect. 2 explains study area and methods used for estimating the LULC and LST. Sect. 3 presents study results comprised of LU/LC and LST variation in the studied region. Sect. 4 compares the current study results with previous studies and provided a more comprehensive discussion of the limitations, such as potential sources of error in land cover classification or temperature estimation that would be beneficial. Finally, Sect. 5 concludes the current study and provides concrete recommendations or suggestions for policymakers based on the research findings.

2 Materials and methods

2.1 Study area

The geographical location of the stud area (Nasirabad district) located in Balochistan Province of Pakistan is shown in Fig. 1. It is located between 67.74°–68.44° East (E) longitudes, and 28.20°–29.09° North (N) latitudes. The district is situated in the central region of Balochistan, with its eastern boundary next to Dera Bugti and its western boundary adjoining Jhall Magsi. To the south of the district is Jaffarabad district, while Bolan district is situated to the north. There are four tehsils (administrative level divisions), namely Baba Kot, Chattar, Dera Murad Jamali, and Tambo, in Nasirabad district. Nasir Khan Noori is honored with the naming of the city of Nasirabad. Geologically, the study are contains a flat area with no highland elements. Alluvial soils predominate in this region, with north-to-south slopes and a terrain size of 50 to 170 m above sea level. July and August, when monsoons are at their peak, are the heaviest rain months. Nasirabad is located in the tropical agro-ecological zone, and its total agricultural land is estimated to be 215,728 hectares, or around 63.7% of the district's entire geographical area. Rice holds a prominent position as a vital cash crops and stands as the most common crop in the area. Rice production accounted for over 70% of irrigation land in Nasirabad during last few years and most of the rice production comes from Dera Murad, Nasirabad Mali and Tambo [65].

Fig. 1
figure 1

Geographical location of study area (Nasirabad district, Balochistan, Pakistan)

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2.2 Landsat remote sensing data collection

The United States Geological Survey's (USGS) website (earthexplorer.usgs.gov) provided the 30 × 30 m spatial resolution, 0% cloud cover Landsat RS satellite images used to determine LU/LC change detection & LST estimation, which included areas of vegetation, bare soil, settlements, and water bodies. The specifics of the gathered Landsat satellite data are listed in Table 1.

Table 1 Collected Landsat satellite data details
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2.3 Methodology used for LU/LC change detection

Landsat images are composed of different and distinct spectral bands. In this study, we used Landsat-5 bands 1–5 and 7, as well as Landsat-8 bands 1–7, to assess land use and land cover change detection. Additionally, we employed bands 6 and 8 (thermal bands) from Landsat-8 to analyze variations in land surface temperature. The Landsat images underwent processing in ArcMap 10.7.1 software for various purposes, including geo-referencing, layer stacking (a method used to create a multiband image by combining discrete bands), mosaicking (the process of merging two stacked images), and subsetting (the extraction of the study area from the stacked image). These procedures were performed based on the Area of Interest, as described by Xu et al. [66]. The study conducted by Adefisan et al. [67] included the analysis of satellite data via the use of per-pixel fingerprints. The land use and land cover (LU/LC) maps were generated by the use of a supervised classification approach, namely the maximum likelihood algorithm. The training site choices were made based on Landsat images from the years 1993, 2003, 2013, and 2023. Training samples were chosen for each of the specified land use/land cover (LU/LC) classes by creating polygons around typical locations, as described by Usman et al. [68]. The satellite image's spectral characteristics of the different land cover types were determined by analyzing the pixels included inside the polygons. The model incorporates the aggregated changes in land surface temperature (LST) and land use/land cover (LU/LC) categories over the whole area from 1993 to 2023 in order to examine their interrelationships. The process for determining LU/LC and LST is shown in Fig. 2, providing a detailed, sequential guide. In arid regions, the LST refers to the average temperature of all unbroken objects. The LST was determined by a large group of researchers using precise measurements on Landsat images. These images acquired at 30 m spatial resolution were used to derive the LST [69]. Figure 3 displays the LST retrieval using satellite imagery. Initially, L values were used in Eq. 5 to calculate spectral radiance. With Eq. 6 [23], we change the spectral radiance to temperature in the subsequent stage. Equation 7 was used to make the transition from Kelvin to Celsius finally.

$${L\lambda = gain\; \times \;QCAL\; + \;offset}$$
(1)
$${T = \frac{{K_{{2}} }}{{In(\frac{{K_{{1}} }}{L\lambda } + {1})}}}$$
(2)
$${T\left( {C^{O} } \right) = T\left( K \right) - {273}{\text{.15}}}$$
(3)
Fig. 2
figure 2

Stepwise procedure to determine LU/LC from satellite images

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Fig. 3
figure 3

Stepwise procedure for LST retrieval from satellite images

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where Lλ represents spectral radiance; QCAL is quantized calibrated pixel value in digital number (DN). K1 and K2 values are 607.76 and 1260.56 for Landsat 5 (TM) and 772.88 and 1321.07 for Landsat 8 (OLI), respectively, and ln shows natural logarithm [70,71,72].

2.4 Accuracy assessment of LU/LC classes

Several studies [73,74,75] agree that error matrices best illustrate precision results. For example, the percentage of user accuracy (UA), producer accuracy (PA), and overall accuracy (OA) all account for random error [41, 71, 72, 76,77,78]. The accuracy and theoretical agreement of RS categorization may be estimated by evaluating the Kappa coefficient (K) values [79,80,81,82]. A measure of the proportion of an error matrix’s accurate values that can be attributed to “true” rather than “chance” agreement [83]. The mathematical equations for determining UA, PA, OA and T can be used as:

$$\mathrm{User\; Accuracy}=\frac{\mathrm{Number\; of\; Correctly\; Classified \;Pixels\; in \;each \;Category}}{\mathrm{Total \;Number\; of \;Classified \;Pixels \;in \;that\; Category }(\mathrm{The \;Row \;Total})}\times 100$$
(4)
$$\mathrm{Producer \;Accuracy}=\frac{\mathrm{Number\; of\; Correctly\; Classified\; Pixels\; in \;each \;Category}}{\mathrm{Total \;Number\; of \;Reference \;Pixels \;in \;that \;Category }(\mathrm{The\; Column\; Total})}\times 100$$
(5)
$$\mathrm{Overall \;Accuracy}=\frac{\mathrm{Total \;Number\; of\; Correctly\; Classified\; Pixels }(\mathrm{Diagonal})}{\mathrm{Total\; Number\; of \;Reference \;Pixels}}\times 100$$
(6)
$$\mathrm{Kappa\; Coefficient }(\mathrm{K})=\frac{(\mathrm{TS}\times \mathrm{TCS})- \sum (\mathrm{Columns\; Total}\times \mathrm{Row\; Total})}{{\mathrm{TS}}^{2}-\sum \left(\mathrm{Column\; Total}-\mathrm{Row \;Total}\right)}\times 100$$
(7)

Here, TS = Total Sample, TCS = Total Correctly Classified Sample.

3 Results

3.1 LU/LC changes

Various LU/LC categories were used to classify the research region during 1993, 2003, 2013, and 2023 (Table 2). In 1993, the area covered by water was 111.16 km2, followed by bare soil at 1471.40 km2, and finally by vegetation at 1245.79 km2, with the area covered by human settlements at 394.60 km2. Image analysis conducted in 2003 revealed that 16.75% of the research area was covered by vegetation, whereas 5.59% was covered by human settlements, 3.48% by water bodies, and 60.44% by bare land (Fig. 4). Similar in 2023, there were 102.05 km2 (3.17%) of water bodies, 1056.12 km2 (32.77%) of barren soil, 1534.07 km2 (47.60%) of vegetation, and 530.70 km2 (16.47%) of settled land. From 1993 to 2023, the %age of water areas declined to -0.28%. From 1993 to 2023, vegetation and human settlements grew by 8.94% and 4.22%, respectively. Moreover, it is noteworthy that the bare soil area in the study are has shrunk by 12.89% over the past three decades due to the conversion for bare soil into built-up as well as vegetation areas. It is intriguing that area under settlement declined between 2003 and 2013 i.e. from 622.84 sq.km (19.33%) to 501.94 sq.km (15.57%) and thereafter increased. Urban floods are prevalent during the monsoon season in Pakistan, affecting several cities, including Karachi, Islamabad, Lahore, Hyderabad, and Nasirabad etc. Besides, Yemyin cyclone (2007), Phet cyclone (2010) Sindh floods (2011) are among the most notable events. The record destruction including the loss of around 1985 lives were witnessed during the flood that occurred in the year 2010 [92]. Rainfall during the monsoon season is one of the major cause of floods in Pakistan which at times is supported by the snowmelt in rivers. Additionally, monsoonal runoff is further augmented by snowmelt upstream of Tarbela Dam in the northern parts of the country. Due to environmental degradation, i.e., deforestation, Pakistan has faced extreme disasters in the recent years, i.e., the consecutive floods that hit Pakistan in the year 2010 and 2011. It is expected that such disasters would frequently occur in the coming years. The risk to the natural environment and the biodiversity of Pakistan are mainly due to changing environmental conditions and degradation (deforestation) [84].

Table 2 Aerial distribution of LU/LC class in Nasirabad from 1993 to 2023
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Fig. 4
figure 4

Maps of LU/LC in Nasirabad from a 1993, b 2003, c 2013 and d 2023

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Nearly 47.60% of the entire land has been changed to vegetation, whereas only 16.47% of the total area has been converted to settlements in the previous 30 years. Rapid changes in LU/LC have been seen, particularly in the proportion of land covered by trees and other plants. Vegetation coverage has grown by 8.94% since 1993 as farmland has given way to highways and cities. Since the study area's primary source of income is crop (primarily rice) production, the expansion of settlements and has also been one of the primary causes of the growth in vegetation areas and the decline in water resources. In addition, Nasirabad typically receives about 12.77 mm (0.5 inches) of precipitation and has 24.99 rainy days (6.85% of the time) annually [85].

3.2 Accuracy assessment of LU/LC classes

Table 3 presents an evaluation of accuracy for different LU/LC classes, broken down by year. Average user accuracy was 95% for water, 97.50% for vegetation, 76.25% for settlements, and 86.25% for bare soil. The average levels of producer accuracy for the four LU/LC categories were very similar: 97%, 76.77%, 94.44%, and 94.09%. Table 3 displays producer and user accuracies and a trend of total accuracy and T values over time. The best overall accuracy (90.63%) and the highest K value (88.24%) were achieved 2023. Table 4 summarizes prior research (using various satellites and their sensor) overall accuracy (OA) and Kappa (K) values. Both OA and K values in this investigation are acceptable (88.63% to 90.63%). These numbers are consistent with previous research (as shown in Table 4), lending credence to our findings.

Table 3 Accuracy assessment of LU/LC classes in study area
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Table 4 Comparison of K and OA values from existed literature
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3.3 LST changes

Climate change can impact vegetation production and health through both direct and indirect mechanisms. Alterations in temperature, precipitation, and CO2 concentration may all negatively affect plant growth and development, which are direct consequences of climate change. A rise in temperature, for instance, may hasten the development of plants, while a drop in precipitation might limit their access to water. Figure 5 displays LST variation maps for the Nasirabad area between 1993 and 2023. In addition, linear trend analysis of LST variation in Nasirabad can be seen in Fig. 6 to support the research findings. It can be observed that, in 1993, the average LST in Nasirabad ranged from 25.25 to 52.06 °C (Fig. 6). The average annual LST ranged from 30.05 to 52.41 °C in the Nasirabad area in 2003. In the green belt (Nasirabad) area of Balochistan, the LST ranged from 32.50 to 46.00 °C in 2013 and from 32.75 to 47.00 °C in 2023. We recorded the lowest, highest, and average LST changes over the research period in the Nasirabad area and then visualized them using a 3D surface Map, heat map, spider-web chart, and parallel graphs as shown in Figs. 7, 8 and 9. The average LST for 1993, 2003, 2013, and 2023, as depicted in Figs. 7 and 8, were 38.65 °C, 41.23 °C, 39.25 °C, and 39.87 °C. Figure 9 illustrates, the minimum, maximum, and average changes in LST between 1993 and 2003, which amounted to 4.80 °C, 0.35 °C, and 2.58 °C, respectively. Similarly, between 2003 and 2013, the minimum, maximum, and average changes in LST were 2.45, 0.35, and 1.40 °C, respectively. From 2013 to 2023, the minimum, maximum, and average LST changed by 0.25, 1.00, and 0.63 °C, respectively as shown in Fig. 9. It can also be perceived from Fig. 9 that minimum, maximum, and average LST changes were recorded as 7.25, − 6.06, and 0.59 °C for the first two decades (1993–2013), and 7.50, − 5.06, and 1.22 °C for the whole thirty years (1993–2023). Due to increasing plant cover, the LST values in the south of Nasirabad are lower than in the rest of the region. In contrast, LST values are more significant in the north, with more exposed ground as shown in Fig. 5.

Fig. 5
figure 5

LST variation maps from 1993 to 2023 in Nasirabad district

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Fig. 6
figure 6

Minimum, maximum and average LST variation in Nasirabad

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Fig. 7
figure 7

Minimum, maximum and average LST variation in Nasirabad

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Fig. 8
figure 8

HeatMap showing LST variation from 1993 to 2023

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Fig. 9
figure 9

LST change from 1993 to 2023 in Nasirabad through spider web chart

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4 Discussion

Researchers used RS and GIS to create maps of LU/LC in the research region and to estimate surface temperature based on the perspectives of local farmers. Because of the profound effect that changes in LU/LC have on regional climate, understanding the relationship between these variables and LST along with vegetation index. This understanding is crucial for making informed decisions during planning and development of new urban areas. For this reason, we have averaged the annual average LST in the green belt (Nasirabad) district of Balochistan, Pakistan, for the most recent period (1993–2003–2013–2023). Similarly, LU/LC statistics are aggregated inside these political boundaries. We conducted our analysis using the percentage LST change (1993–2023) as predictor variables and the LU/LC types as explanatory factors. We fitted models to assess the capability of each explanatory variable in explaining the geographical variation in LST. We then proceeded to train a multivariate model to gain insights into the intricate relationship between LU/LC labels and LST.Our findings showed several notable trends in land use and land cover (LU/LC) changes between 1993 and 2023. There was a decrease in the proportion of water bodies by − 0.28%. In contrast, both vegetation and human settlements increased, with vegetation expanding by 8.94% and human settlements growing 4.22%. Furthermore, there was a significant reduction in bare soil, which decreased by 12.89% over the last three decades. Remarkably, during the past 30 years, a substantial 47.60% of the entire land area underwent vegetation conversion, whereas only 16.47% was converted to settlements. These rapid changes in LU/LC have been seen, particularly in the proportion of land covered by trees and other plants. Vegetation coverage has grown by 8.94% since 1993 as farmland has given way to highways and cities. The significant role that each of this LU/LC plays in establishing the LST in any given place suggests that these findings are to be expected. According to the data, the minimum, maximum, and average changes in LST between 1993 and 2003 were 4.80 °C, 0.35 °C, and 2.58 °C. The lowest, maximum, and average changes in LST between 2003 and 2013 were 2.45, 0.35, and 1.40 °C, respectively. From 2013 to 2023, the minimum, maximum, and average LST changed by 0.25, 1.00, and 0.63 °C, respectively. Minimum, maximum, and average LST changes were recorded as 7.25, − 6.06, and 0.59 °C for the first two decades (1993–2013), and 7.50, − 5.06, and 1.22 °C for the whole thirty years (1993–2023). Due to increasing plant cover, the LST values in the south of Nasirabad are lower than in the rest of the region.

In contrast, LST values are more significant in the north, with more exposed ground. The results demonstrated that climate change has a considerable impact on agricultural output. Even while temperatures are rising throughout the study region, local farmers expressed concern and awareness of changes in temperature and rainfall patterns in a recent survey. Rising temperatures are an obvious indicator of climate change, making it imperative that adaptation programs be bolstered to their full potential in order to satisfy the fundamental requirements of the local population [89,90,91]. Hussain et al. [92, 93] point out that the easiest way to solve this issue is to decrease the number of response variables by comparing and contrasting the goodness-of-fit and values of various multidimensional models. The LST ranges from around 6.7 °C to about 26.5 °C during winter 2022. The projected LST range in the research region for the summer of 2022 is 23.3 °C. The results show that a multivariate model can account for the observed variance in the dependent variable (LST) in 70% of the studied area. By displaying the local R2 values for each municipality, the geographical performance assessment of this model is made clear. Most of the research area's metropolitan areas are situated in the east and north, which makes the LU/LC more complex, leading to the much inferior goodness-of-fit [74].

In contrast, the LU/LC is more straightforward in settings where the model excels. More study is needed in this area since it will be necessary to model these relationships at higher levels before they can be scaled down to extensive metropolitan regions. Extreme weather events, such as floods, droughts, and wildfires, explain how climate change might indirectly affect vegetation. Several environmental factors may negatively impact Vegetation cover and production, including drought and wildfires [71, 94]. According to Lui et al. [23], the LST in a given region affects the degree to which LU/LC varies. Adaptations to a changing climate, changes in LU/LC, and other factors may affect LST values [74, 95]. To promote sustainable land use and effective natural resource management in the face of climate change, the RS technology may be utilized to monitor LU/LC change detection and LST variation over time. Satellites are essential tools for collecting distant object data. In recent years, the role that long-term studies of RS vegetation dynamics have had in the study of global ecosystems has been more apparent. Spectral landscapes in RS images allow for easy item recognition. The RS has widespread use in spotting cyclical changes in plant life. Over time, we may anticipate functional RS to become a valuable resource for advancing humanity's ability to meet its global, regional, and local obligations in matters relating to the atmosphere.

Although the MLC is a widely used classifier, sometimes it could not produce satisfactory results in deriving accurate and reliable classification of LU/LC categories. In future research, studies could significantly improve MLC maps by incorporating additional data, such as land use, DEM, spatial texture and NDVI value of the Landsat imagery using a hypothesis testing framework based system of classification. Also, post-classification correction (PCC) can be used for detailed post-classification change detection and correction of LU/LC maps [96]. This study has demonstrated the usefulness of integrating ground truthing data and knowledge-based rules into a classification scheme to improve accuracy of LULC classification.

5 Conclusions

This research uses satellite imagery to examine change detection in LU/LC and LST variation in the green belt (Nasirabad) district of Balochistan, Pakistan. Analysis was carried out using the most up-to-date satellite information available (1993–2023). Based on our findings, from 1993 to 2023, the total water area on Earth shrank by a negligible − 0.28%. Between 1993 and 2023, both plant life and human habitation expanded. Finally, during the past three decades, bare soil areas decreased from -12.89%age points over the study area. In the previous 30 years, over 47.50% of the landmass has been turned into vegetation, whereas only 16.47% has been turned into towns. Changes in LU/LC have occurred rapidly, especially in the amount of land covered by trees and other plants. Since 1993, bare soil was converted to roadways and built-up space, an additional 8.94% of the land. The minimum, maximum, and average LST changes were calculated to be 7.50, -5.06, and 1.22 °C for the next thirty years (1993–2023) respectively. Analysis of LST data showed that overall; temperatures in the examined area increased by an average of 1.22 °C due to human settlement expansion. The results improve our understanding of LU/LC dynamics in Nasirabad, which in turn helps formulate sustainable development plans. Additionally, the LST evaluation and its association with LU/LC change help to progressively affect decisions and policies about adaptation in Pakistan's Balochistan area. Overall, variations in LU/LC and LST provide helpful information on the state of vegetation and agricultural yields. Land use planning and natural resource management may benefit from a better understanding the connection between LU/LC and LST fluctuation to pinpoint regions of high productivity and those at risk of deterioration (heat stress). With the GIS, updated data can be generated and integrated quickly and inexpensively across various geographies and changes arising from management methods. Based on an analysis of past RS satellite data, we may make predictions regarding vegetation problems on Earth's surface about future climate. This study’s results will also aid policymakers in making judgments on the direction of future growth. In order to support evidence-based decision-making for land use planning and natural resource management, spatial analysis techniques permit the quantification of the extent and magnitude of LU/LC and its associated impacts. The multitude of elements mostly contributes to the favorable outcome of rice production in Nasirabad, with the exception of the growers' experience and the rising input costs. This is due to the unwillingness of experienced farmers to adopt developing methods and technology in the production process. Therefore, it is necessary upon the management to motivate and assist farmers in adopting fertilizers and innovative techniques in agricultural production. Additionally, the management should provide farmers with loans at a low interest rate through an efficient and effective process. Furthermore, adult education programs should be implemented during weekends to educate farmers. Furthermore, it is the obligation of the state to provide training and inspiration to farmers in order to enhance their knowledge and proficiency in state-of-the-art agricultural practices and procedures. Furthermore, it is essential that the government provide interest-free loans to farmers in line with their financial needs. It is mandatory to initiate agricultural training programs in order to convey knowledge and raise awareness among rice producers. Additionally, it is essential to provide inputs to farmers at their doorstep, at a subsidized rate. Furthermore, there is a need to enhance physical infrastructure in accordance with the demands of farmers. Subsequently, it is crucial to introduce modern farming methods to farmers in the study area.