Farrah Melissa Muharam

Phenology and classification of abandoned agricultural land based on ALOS-1 and 2 PALSAR multi-temporal measurements

ABSTRACT Agricultural crop abandonment negatively impacts local economy and environment since land, as a resource for agriculture, is not optimally utilized. To take necessary actions to rehabilitate abandoned agricultural lands, the identification of the spatial distribution of these lands must be acknowledged. While optical images had previously illustrated potentials in the identification of agricultural land abandonment, tropical areas often suffer cloud coverage problem that limits the availability of the imageries. Therefore, this study was conducted to investigate the potential of ALOS-1 and 2 (Advanced Land Observing Satellite-1 and 2) PALSAR (Phased Array L-band Synthetic Aperture Radar) images for the identification and classification of abandoned agricultural crop areas, namely paddy, rubber and oil palm fields. Distinct crop phenology for paddy and rubber was identified from ALOS-1 PALSAR; nonetheless, oil palm did not demonstrate any useful phenology for discriminating between the abandoned classes. The accuracy obtained for these abandoned lands of paddy, rubber and oil palm was 93.33% ± 0.06%, 78% ± 2.32% and 63.33% ± 1.88%, respectively. This study confirmed that the understanding of crop phenology in relation to image date selection is essential to obtain high accuracy for classifying abandoned and non-abandoned agricultural crops. The finding also portrayed that PALSAR offers a huge advantage for application of vegetation in tropical areas.

The Use of Multi-Temporal Landsat Imageries in Detecting Seasonal Crop Abandonment

Abandonment of agricultural land is a global issue and a waste of resources and brings a negative impact on the local economy. It is also one of the key contributing factors in certain environmental problems, such as soil erosion and carbon sequestration. In order to address such problems related to land abandonment, their spatial distribution must first be precisely identified. Hence, this study proposes the use of multi-temporal Landsat imageries, together with crop phenology information and an object-oriented classification technique, to identify abandoned paddy and rubber areas. Results indicate that Landsat time-series images were highly beneficial and, in fact, essential in identifying abandoned paddy and rubber areas, particularly due to the unique phenology of these seasonal crops. To differentiate between abandoned and non-abandoned paddy areas, a minimum of three time-series images, mainly acquired during the planting seasons is required. For rubber, multi-temporal images should be examined in order to confirm the wintering season. The study demonstrates the advantages of using multi-temporal Landsat imageries in identifying abandoned paddy and rubber areas wherein an accuracy of 93.33% ± 14% and 83.33% ± 1%, respectively, were achieved.

Assessing leaf scale measurement for nitrogen content of oil palm: performance of discriminant analysis and support vector machine classifiers

ABSTRACT Nitrogen (N) is a crucial element in sustaining oil palm production. However, assessing N status of tall perennial crops such as oil palm is complex and not as straightforward as assessing annual crops, due to complex N partitioning, age, and larger amounts of respiratory loads. Hence, the objectives of this study were to evaluate the potential of spectral measurements obtained from leaf scale and machine learning approaches as a rapid tool for quantifying oil palm N status. This study involved assessing the performance of discriminant analysis (DA) and Support Vector Machine (SVM) classifiers for discriminating spectral bands sensitive to N sufficiency levels and comparing the predictive accuracy of DA and SVM for classifying N status of immature and mature oil palms. The experiment was conducted on immature Tenera seedlings (13 months old) and mature Tenera palm stands (9 and 12 years old) that were arranged in Randomized Complete Block Design with treatments varied from 0 to 2 kg N. Generally, the discriminant function of both classifiers was age-dependent. A clear trade-off between the classifiers’ number of spectral bands and their accuracies was observed; the DA with a larger number of optimal spectral bands could discriminate N sufficiency levels of all maturity classes with higher accuracies compared to the SVM, yet the latter could produce reasonable accuracies with a lesser number of spectral bands. N status of all maturity classes could be classified satisfactorily with SVM (71–88%) via the satellite-simulated blue and green bands, signifying the possibility to develop spectral index or an N-sensitive sensor for oil palm.

Evaluation of ground-level and space-borne sensor as tools in monitoring nitrogen nutrition status in immature and mature oil palm

ABSTRACT Monitoring nitrogen (N) in oil palm is crucial for the production sustainability. The objective of this study is to examine the capability of visible (Vis), near infrared (NIR) and a combination of Vis and NIR (Vis + NIR) spectral indices acquired from different sensors for predicting foliar N content of different palm age groups. The N treatments varied from 0 to 2 kg per palm, subjected according to immature, young mature and prime mature classes. The Vis + NIR indices from the ground level-sensor that is green + red + NIR (G + R + NIR) was the best index for predicting N for immature palms (R2 = 0.91), while Vis indices blue + red (B + R) and Green Red Index from the space-borne sensor were significantly useful for N assessment of young and prime mature palms (R2 = 0.70 and 0.50), respectively. The application of vegetation indices for monitoring N status of oil palm is beneficial to examine extensive plantation areas.

Textural measures for estimating oil palm age

ABSTRACT In oil palm management, age is one of the yield determinant factors. The conventional field investigations are often exhaustive and costly methods when implemented on a large scale. Despite much attention to classify individual oil palm ages by using various remote sensing images, none of the studies depicted satisfying overall accuracies. The overall aim of this study was to optimize window size and number of texture measurements for oil palm ages classification. The study was conducted in a commercial oil palm plantation comprised of palms of multiple ages planted from 1991 to 2008. Three Satellite Pour l’Observation de la Terre (SPOT)-5 multispectral images, acquired on 12 April 2012, 4 April 2013, and 14 April 2014, were evaluated. The individual ages were successfully classified with accuracy ranging from 59% to 97%, with an average overall accuracy of 84%. The results illustrated that the largest window size related to the smallest oil palm planting block in the study area, 390 m × 390 m on-the-ground window size, and seven combination of texture measurements, resulted in the highest classification overall accuracy. The utilization of texture measurements produced synergistic effects able to discriminate the oil palm age, with mean, entropy, homogeneity, and angular second moment as among the significant textures.

Evaluation of linear discriminant and support vector machine classifiers for classification of nitrogen status in mature oil palm from SPOT-6 satellite images: analysis of raw spectral bands and spectral indices

Abstract Nitrogen (N) management is important in sustaining oil palm production. Remote sensing-based approaches via spectral index have promise in assessing the N nutrition content. The objectives of this study are; (i) to examine the N classification capability of three spectral indices (SI) such as visible (Vis), near infrared (NIR) and a combination of visible and NIR (Vis + NIR) from the SPOT-6 satellite, and (ii) to compare the performance of linear discriminant analysis (LDA) and support vector machine (SVM) in discriminating foliar N content of mature oil palms. Nitrogen treatments varied from 0 to 2 kg per palm. The N-sensitive SIs tested in this study were age-dependent. The Vis index (BGRI1) (CVA = 79.55%) and Vis + NIR index (NDVI, NG, IPVI and GNDVI) (CVA = 81.82%) were the best indices to assess N status of young and prime mature palms through the SVM classifier.

Relationship between High Temperature and Formation of Chalkiness and Their Effects on Quality of Rice

Occurrence of chalkiness in rice is attributed to genetic and environmental factors, especially high temperature (HT). The HT induces heat stress, which in turn compromises many grain qualities, especially transparency. Chalkiness in rice is commonly studied together with other quality traits such as amylose content, gel consistency, and protein storage. In addition to the fundamental QTLs, some other QTLs have been identified which accelerate chalkiness occurrence under HT condition. In this review, some of the relatively stable chalkiness, amylose content, and gel consistency related QTLs have been presented well. Genetically, HT effect on chalkiness is explained by the location of certain chalkiness gene in the vicinity of high-temperature-responsive genes. With regard to stable QTL distribution and availability of potential material resources, there is still feasibility to find out novel stable QTLs related to chalkiness under HT condition. A better understanding of those achievements is essential to develop new rice varieties with a reduced chalky grain percentage. Therefore, we propose the pyramiding of relatively stable and nonallelic QTLs controlling low chalkiness endosperm into adaptable rice varieties as pragmatic approach to mitigate HT effect.

Towards the use of remote-sensing data for monitoring of abandoned oil palm lands in Malaysia: a semi-automatic approach

ABSTRACT Oil palm is a commercial crop that is important for its food value and as a biofuel, along with its other benefits towards the economy and human health. Currently, Malaysia cultivates approximately 5.64 million ha of oil palm. To date, a study identifying abandoned oil palm areas using satellite images is almost non-existent. Conventionally, the monitoring of abandoned oil palm lands is tedious and time consuming, especially over large areas. Hence, in this article, the capability of high resolution satellite image via Satellite Pour I’Observation de la Terre-6 (SPOT-6) products to extract abandoned oil palm areas was explored, as was the use of multi-temporal Landsat Operational Land Imager (OLI) imagery to develop the phenology of abandoned oil palm sites. Homogeneity measures derived through SPOT images played a more important role to identify abandoned oil palm than crop phenology characteristics extracted from high spectral resolution of Landsat images. With the advancement of object-oriented classification, monitoring of abandoned oil palm areas can be done semi-automatically with an accuracy of 92±1%.