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Assessment of land use and land cover change detection and prediction using deep learning techniques for the southwestern coastal region, Goa, India

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Abstract

Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India. It consists of three components: (i) change detection (CD), (ii) quantification of LULC changes, and (iii) prediction. A new CD assessment framework, Spatio-Temporal Encoder-Decoder Self Attention Network (STEDSAN), is proposed for the LULCCD process. A dual branch encoder-decoder network is constructed using strided convolution with downsampling for the encoder and transpose convolution with upsampling for the decoder to assess the bitemporal images spatially. The self-attention (SA) mechanism captures the complex global spatial-temporal (ST) interactions between individual pixels over space-time to produce more distinct features. Each branch accepts the LULC map of 2 years as one of its inputs to determine binary and multiclass changes among the bitemporal images. The STEDSAN model determines the patterns, trends, and conversion from one LULC type to another for the assessment period from 2005 to 2018. The binary change maps were also compared with the existing state of the art (SOTA) CD methods, with STEDSAN having an overall accuracy of 94.93%. The prediction was made using an recurrent neural network (RNN) known as long short term memory network (LSTM) for the year 2025. Experiments were conducted to determine area-wise changes in several LULC classes, such as built-up (BU), crops (kharif crop (KC), rabi crop (RC), zaid crop (ZC), double/triple (D/T C)), current fallow (CF), plantation (PL), forests (evergreen forest (EF), deciduous forest (DF), degraded/scurb forest (D/SF) ), littoral swamp (LS), grassland (GL), wasteland (WL), waterbodies max (Wmx), and waterbodies min (Wmn). As per the analysis, over the period of 13 years, there has been a net increase in the amount of BU (1.25%), RC (1.17%), and D/TC( 2.42%) and a net decrease in DF (3.29%) and WL(1.44%) being the most dominant classes being changed. These findings will offer a thorough description of identifying trends in coastal areas that may incorporate methodological hints for future studies. This study will also promote handling the spatial and temporal complexity of remotely sensed data employed in categorizing the coastal LULC of a heterogeneous landscape.

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Availability of data and materials

The data that support the findings of this study are available from the National Remote Sensing Centre, ISRO, Government of India, Hyderabad, India, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the National Remote Sensing Centre, ISRO, Government of India, Hyderabad, India.

Code Availability

Not applicable

Abbreviations

AwiFs:

Advanced wide field sensor

BU:

Built-up

CA:

Cellular automata

CD:

Change detection

CF:

Current fallow

CNN:

Convolutional neural network

D/SF:

Degraded/scrub forest

D/TC:

Double/triple crop

DF:

Deciduous forest

DT:

Decision tree

DL:

Deep learning

DNN:

Deep neural network

ED-LSTM:

Encoder decoder LSTM

EF:

Evergreen forest

FN:

False negative

FP:

False positive

FCN:

Fully convolutional neural network

GA:

Genetic algorithm

GAN:

Generative adversarial networks

GIS:

Geographical information systems

GL:

Grassland

ISRO:

Indian Space Research Organization

KC:

Kharif crop

LS:

Littoral swamp

LSTM:

Long short term memory network

LULC:

Land use and land cover

LULCC:

Land use and land cover change

LULCCD:

Land use and land cover change detection

MCM:

Markov chain model

MIOU:

Mean intersection over union

ML:

Machine learning

MLP:

Multilayer perceptron

NRSC:

National Remote Sensing Centre

NDBI:

Normalized Difference Built-up Index

OA:

Overall accuracy

PA:

Producer accuracy

PCA:

Principal component analysis

PL:

Plantation

RC:

Rabi crop

ReLU:

Rectified linear unit

RF:

Random forest

RNN:

Recurrent neural network

RS:

Remote sensing

SA:

Self-attention

SITS:

Satellite image time series

SOTA:

State of the art

ST:

Spatial-temporal

STEDSAN:

Spatio-Temporal Encoder-Decoder Self Attention Network

SVM:

Support vector machine

TN:

True negative

TP:

True positive

UA:

User accuracy

WL:

Wasteland

Wmn:

Waterbodies min

Wmx:

Waterbodies max

Xgboost:

Extreme gradient boosting

ZC:

Zaid crop

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Acknowledgements

The authors would like to thank the National Remote Sensing Centre, Indian Space Research Organization (ISRO), Government of India, Hyderabad, India, for providing time series data in form of LULC maps from 2005–06 to 2017–18 for the Goa Region.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Surathkal, Mangalore, 575025, Karnataka, India

    Nitesh Naik & Kandasamy Chandrasekaran

  2. Department of Computer Science and Engineering, National Institute of Technology, Puducherry, Karaikal, 609609, Puducherry, India

    Venkatesan Meenakshi Sundaram

  3. School of Information Technology and Engineering, Vellore Insitute of Technology, Katpadi, Vellore, 632014, TamilNadu, India

    Prabhavathy Panneer

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  2. Kandasamy Chandrasekaran
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  3. Venkatesan Meenakshi Sundaram
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  4. Prabhavathy Panneer
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Nitesh Naik, Kandasamy Chandrasekaran, Meenakshi Venkatesan Sundaram, and Prabhavathy Panneer. The first draft of the manuscript was written by Nitesh Naik, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Nitesh Naik.

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Naik, N., Chandrasekaran, K., Meenakshi Sundaram, V. et al. Assessment of land use and land cover change detection and prediction using deep learning techniques for the southwestern coastal region, Goa, India. Environ Monit Assess 196, 527 (2024). https://doi.org/10.1007/s10661-024-12598-y

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